June 5, 2019
An early look at CDPs in the APAC market
By Susan Raab, CDP Institute
Demand for customer data platforms has grown internationally in the last two years, particularly in Europe and the UK, so this seemed like a good time to look at the APAC market. Leaders there report Asia-Pacific marketers are acutely aware that they need the functionality of Customer Data Platforms but knowledge of CDPs as a distinct class of software is still in the very early stages, according to local experts interviewed by the CDP Institute. We spoke with executives at Knowesis, Lemnisk, Manthan and MarketSoft to learn more about the state of the market and trends they see evolving.
As in the U.S., the UK and Europe, the main industries to watch for CDP are retail, telecommunications, ecommerce and finance along with government and travel. These industries are feeling pressure to move rapidly to find new ways to meet customer and corporate expectations for improved identity recognition, personalized customer service, increased marketing efficiency, and ROI.
“We are seeing emerging awareness [in Australia/New Zealand] with early adopters of genuine CDPs coming on board,” says Daniel Cummins, CEO of Marketsoft, a consultancy based in Sydney. “At the same time, there are a number of players claiming to be CDPs, but who have products that only offer a portion of the functionality, or without the underlying strategy, so there’s inconsistency about the concept’s definition itself.”
Amit Agarwal, Senior Vice President at Manthan Systems, concurs that “awareness isn’t there, and there’s a big need to evangelize about CDPs and be more aggressive.” Manthan is headquartered in Bangalore with offices in U.S., Singapore and UAE, and serves global retailers and restaurant businesses, hosting over 250 million customer profiles worldwide. Agarwal said Manthan has had significant success making inroads, including with a large-scale implementation for Future Group, a top food, fashion and consumer goods conglomerate with 1,800 stores across India and sends 200 million customer communications every month.
Nathan Rae, chief business development officer for Knowesis, agrees that “customer awareness is still very low,” but he says, “awareness is building quite quickly, and vendors are pushing” the technology. Knowesis is a Singapore-based CDP company with offices in Thailand, Malaysia, Indonesia, Australia, India and the UAE, and 13 enterprise customers. “We still don’t see a lot of RFPs asking for ‘CDP’,” reported Ajith Kumar, Knowesis’ vice president for professional services. Kumar said he’s seeing a lot of analytics providers using some CDP terminology and believes that, “once Gartner and Forrester begin talking more about CDPs, it will have a big influence with enterprise customers here.” He expects this to make it easier for marketers at companies to categorize the RFPs and to tell higher ups why they are shopping vendors in this specific domain.
Rahul Thomas Mathew, Director of Marketing for Lemnisk, observed that the recent Salesforce and Adobe announcements on their CDP plans have sparked a lot of curiosity. Like the others, he sees this as the right time to broaden understanding about CDP in the APAC market. Lemnisk is a CDP company specializing in financial services, headquartered in Bangalore with offices in the U.S., Singapore and the UAE.
On the sales side, there is an additional challenge in APAC companies because while marketers recognize the value having a CDP, IT departments play a major role in decisions and often have other priorities.
“The best case studies and projects are when the decision sits with marketing and is made with customer use cases in mind” says Marketsoft’s Cummins. “If we can get involved at a company supporting CDP from the top down, that’s ideal. We try to position CDP as a service as much as possible via marketing, and then allow IT to assume ownership once they build the capability.”
Corporate decision makers tend to be CMOs, and in some companies, the chief digital or chief experience officer, said Cummins. Pricing in Australia ranges from AU$150,000-300,000 for technology spend, and from AU$100,000 on the low end to AU$300,000-600,000/year for services on the upper end.
All agreed that privacy is a small factor in APAC countries at present, but thought it was likely to grow as a concern. Cummins observed, “GDPR is more of a perception than a reality here, but one of the biggest benefits for us has been that it has pushed awareness. There’s definitely a new level of IS [Information Security] awareness in terms of risk, adoption, and best practices, but there’s a long way to go.”
Rae and Kumar said a lot of governments in their market are cautious when it comes to PII (Personally Identifiable Information) data being stored on cloud, especially for local companies of significant size.
APAC locations mentioned as most engaged in CDP were Australia/New Zealand, Hong Kong, India, Singapore, and the UAE.
As for trends to watch for, Agarwal at Manthan believes educating the market via the demarcation of CDP and RealCDP, as planned by the CDP Institute, will help to offset efforts by other marketing automation players to position other types of products as Customer Data Platforms. Those often sell customers short by just addressing two or three data sources and channels, rather than providing true unified customer data.
Mathew at Lemnisk believes that “CDP’s tightly coupled with other vertical focused offerings can drive significant traction and value in vertical markets.”
Finally, Rae sees a key trend in the movement evolving in APAC companies from on-premises data stores to the cloud, since that will require “refreshing platforms and being willing to look at new tools.”
Share: https://blog.cdpinstitute.org/Blog/Blog867/An-early-look-at-CDPs-in-the-APAC-market
May 30, 2019
The Differences Between Attracting Customers into Your Store, and Catering to Them While They’re in: Combining Fast and Slow Data
By Shai Frank, Optimove
Luring the right people with the right offers requires a rich set of data and sophisticated customer modeling – but it’s a different set than what is needed to react to their signals when they’ve stepped in the door. The magic happens when you combine.
In today’s digital realm, we should always aspire to attract the most relevant people to our business and guide them to the right sections with the right offers to maximize the chances they’ll make a purchase. In order to do so, we need to understand their preferences and their willingness to buy, to learn best what makes them tick.
Some customers always look for bargains and sales, while some get most excited about new arrivals. In order for us to identify these preferences and tendencies, we need to have a broad set of data about each customer, such as their demographic characteristics and purchase history. This data must be integrated from multiple systems, cleansed and matched, in order to allow for segmentation models, predictions and recommendation engines to produce reliable results.
That said, we should make sure we know the difference between the sets of data we work with, to come to the holistic, 360° view of our customers. Most data used for profile creation, segmentation and predictive modeling is historical data, and therefore these processes are non-real time by nature. We call them “Slow Data.” Real-time engagement using live signals is what we call “Fast Data.” Both kinds serve us for different purposes, and they go hand-in-hand. In this piece, we will clarify how to use these two sets of data.
Nice and slow
Look at this example: When a user clicks the purchase button on a website, it doesn’t necessarily mean that this purchase will be completed, and it does not indicate whether or not the customer is happy about the newly purchased product. We know that a transaction could be declined, that the customer may return the item or even submit a negative review.
Because we do have these scenarios happening, and more often than not, we cannot rely solely on engagement data obtained in real time. Doing so, we will misclassify customers who returned their last purchase and placed a bad review as “repeat customers.”
The more accurate data required to create reliable customer profiles can only be obtained from “systems of record,” and then this data must be processed and modeled. More than that, some insights can only be generated and validated after a customer’s actions were compared to that of a different customer. All this does not happen in real time.
Once we have the right data, we can create multiple customer segments and apply predictive models that will help us tailor the right message to the right audience.
Going faster than a roller coaster
There’s an important point that needs our attention here: We’ve talked about how automation without orchestration leads to chaos – and the more granular our customer segments are, and as we add more execution channels, the harder it becomes to effectively manage all these communications.
In many cases a single customer could be eligible for multiple campaigns, and if these are all sent out on the fly, without considering what other campaigns were already sent or what other campaigns are going to be sent later that day, the same customer could get contradicting offers or a less-relevant campaign that could result in a missed opportunity. We should always plan our marketing campaigns in advance and set a framework of priorities and exclusion rules, so the system can orchestrate all these scheduled campaigns and optimize the communication to each customer.
Once we manage to get our customer into the store, we want to switch to a different mode of operation: We still have all our pre-processed customer profile, but now we also have live activity feed from the store that we need to use in order to respond in real time to specific signals coming from the customer. For example, if we know that a certain customer is a deal seeker and we see her currently looking at non-discounted products, we can point her to the clearance section of the store. Another example could be based on our modeling of the customer’s preferred product category: We can welcome a customer in our store by highlighting a promotion for a product in that specific category.
The tortoise and the hare
So far, we talked about how in-store engagement is a real-time process, while profile creation and smart orchestration are not real time. But there is also a third option in between, in which the marketing plan is built in advance but adapts itself to short-term updates. Consider the following scenario: We plan a campaign for some of our one-timer customers, trying to encourage them to make a second purchase, and we schedule this campaign to be sent this afternoon. Let’s also assume our system already completed the modeling and prioritization and has identified the optimal list of customers which should receive this campaign today, with their specific product recommendations. But right before execution, we find out that a couple of these customers just returned their last purchased items – which significantly changes their profile and the way we want to approach them. For them, we are no longer trying to activate one-timers but rather save them from churning. In this case, and even though we already established the optimized list of the one-timer campaign recipients, we need to re-evaluate the audience just before the actual execution time in order to make sure we exclude any customer that no longer matches the desired profile. This example shows how a combination of fast and slow data processing and modeling can help reach the optimal result.
In conclusion
Identifying a customer’s profile is based on different data sources – integrating and cleansing them, aggregating, and running various predictive and AI models on top of them. But to get the full picture and cater to the ultimate goal of reaching the right customers with the right offers at the best time requires not only to know the difference between slow and fast data, but more importantly, that they always go hand in hand.
May 27, 2019
Encouraging Customer Loyalty - Making the First to Second Purchase
By Roisin Evans, RedEye
Research from National Express suggests that, on average, Britons book their next holiday just 37 days after returning home from their previous one. If they booked the previous one with you, that’s a golden window of opportunity to turn that first purchase into a second. It shouldn’t be one you waste.
The ability of a business to retain its customers is crucial for its growth and success. In order to do so, many include in their business objective decreasing the percentage of single purchasers, while increasing the number of multiple purchasers and therefore the overall customer lifetime value (CLTV).
Knowing that people returning from holiday are keen to book their next trip so soon (and also knowing from personal experience how the feel-good factor of a great break can make you anxious for the next one) it’s easy to see how you might encourage that next booking.
You may start, for example, with a “welcome home” message – hoping they enjoyed the holiday, but sympathetic to the truth that it’s Monday again (ugh). Then, a few days later, you can start suggesting new destinations based on where they’ve been before, maybe with a discount code to use in their next booking? Little nudges that show how much you care can encourage your customers to come back, again and again.
But – oh yes, there’s a “but” – not all sectors find one-time purchasers thinking about their next purchase in the same way as holidaymakers. After all, if I buy a new pair of trainers, I’m not immediately thinking about buying my next pair having worn them a couple of times (unless they’ve fallen apart, in which case, I’ll be looking for a refund...)
Let the numbers guide you
For a first to second campaign to be profitable, the best you can do is to maximise your resources, distinguishing between the customers that are worth pursuing to those that are not. How? Not based on your gut instinct, that’s for sure! You have something in your hands that’s much more powerful and precise: your customer database.
The insights from customer data are your best chance to get to know your customers on a deep level and, therefore, to create communications that will engage and interest them.
Data can help you build a profile of the customers most likely to make a second purchase, analysing details such as how much time passes between the first and the second purchase, or how much discounts and peer reviews influence them. Of course, all of this is the more efficient the less your silos are separated, but more on that in a future blog post.
Say you have the optimal database at your disposal, one that can deliver you a single customer view on all channels for each one of your customers. Let’s take a look at the next steps:
- First, you need to define the timeframe for when a customer typically will purchase again from your brand.
- Then ask yourself, does the purchase value or product, customer type, time of year, lifestyle profile or any other factor affect whether the customer will purchase again? Look carefully for these correlations.
- Now you can build upon your previous analysis and create a full profile of first to second purchasers.
Congratulations! You have now a complete understanding of your customers. The final stage is to move to a predictive model, whereby customers are assigned a likelihood to become first to second purchasers, allowing you to target those with high and low likelihood with more advanced tactics.
Automation in action
Once you have a picture of likely second purchaser built by data, you don’t gain just a deeper understanding of your customers and of how your business works. You can use these insights to create marketing automation strategies which will further maximise your resources. One way of increasing customer loyalty, for instance, is to make sure you maximise the excitement of their first purchase with you. That’s something we’ve been working on with Travelodge, and you can read more here.
Ultimately, the devil is in the detail. The more you get to know, and we mean really know, your customers, through data insights as opposed to “gut feelings”, the more likely you are to build a strong and lasting relationship with them and, therefore, increasing their CLTV and your revenue.
For some tangible advice, take a look at this infographic our in-house strategy team have put together, with tips on converting your new customers into loyal multi-purchasers.
Originally posted on the RedEye blog.
May 23, 2019
How Mobile Marketing Works in 2019
By Evan F.P., PostFunnel
Mobile marketing has become a key part of modern digital infrastructure. How does this industry operate in 2019, and what strategies are currently at play?
For almost two decades, tech experts claimed the future would be mobile. With an estimated 2.7 billion smartphone users around the world, it seems that future has finally arrived. Advertisers and marketers have clearly adapted to this new reality, seeking out new ways to turn traditional marketing strategies into effective mobile campaigns. Even major brands like Amazon have introduced customizable ad services that are gaining momentum in online and US retail spaces.
In 2019, mobile marketing has turned into a multi-channel discipline that supports massive segments of our online ecosystem. Yet, this future arrived so quickly that it can be challenging to explain or even grasp many of the seismic shifts this field experiences on an annual basis.
For that reason, it’s important to take a step back and broadly discuss mobile marketing in 2019, the various techniques at play, and the effectiveness of some prominent strategies.
In this new PostFunnel series, Nuts and Bolts, we’ll delve into the Martech world in 2019, trying to shed some light on main tools and best practices being used by you, our fellow marketers, in your day-to-day strategies. Every month, our experts will sink their teeth into another aspect of this fascinating field, with hope to inspire you to elevate your business through some smart marketing.
Check out our features section with special projects and articles for your reading pleasure.
What is mobile marketing?
As the term implies, mobile marketing is a technique where advertisers deliver communications to users via smartphones and tablets. As simple as that description sounds, mobile marketing encompasses a broad range of delivery channels including email, SMS messaging, push notifications, in-app advertising, QR codes, and many more.
Thanks to the prevalence and accessibility of today’s smartphones, mobile marketing has a higher potential to target specific audiences than perhaps any prior marketing discipline. Advertisers can deliver personalized messaging, deploy ads based on time of day or location, and design interactive ad formats that effectively engage specific demographics.
Why use mobile marketing?
Mobile phones are so globally ubiquitous that you’d be hard-pressed to find a more effective marketing platform. The overwhelming majority of adult populations worldwide own some kind of mobile device, while the global median for smartphone ownership is 43%. Customers use mobile devices to play games, watch movies, and communicate via social media — all fertile ground for marketing opportunities.
The significance of mobile devices is even higher in emerging economies, where cell phones have become the easiest method of gaining internet access. Meanwhile, in the developed world, the volume of online content accessed using smartphones has eclipsed traditional platforms such as desktop computers.
What is in-app mobile marketing?
In-app mobile marketing, sometimes referred to as app-based marketing, refers to the deployment of advertisements directly within an app itself. Since over 90% of time spent on smartphones is used to view apps, this is perhaps the most effective and cost-efficient marketing technique available to advertisers today.
The easiest way to deploy in-app marketing is through one of the titans of the mobile advertising space, namely Google’s AdMob or Facebook, or through a specialized in-app advertising network like Tapjoy. In order to monetize their apps, developers often integrate ad network SDKs that display ads when certain conditions are met. Some app publishers like Facebook even use Promoted Post services that seamlessly integrate ads into news feeds across all devices.
One important variant of in-app mobile marketing is in-game marketing, where advertisements are deployed directly within a mobile game. While there are certain ad formats and deployment considerations when delivering messaging to gaming audiences, marketing SDKs function in fairly similar ways to in-app mobile marketing on a technical level.
What is SMS mobile marketing?
SMS mobile marketing is the earliest form of the technique, first implemented when SMS and shortcodes launched in the early 2000s. It requires advertisers to obtain or capture mobile phone numbers and directly communicate with users via SMS messaging services. SMS mobile marketing can refer to both inbound marketing strategies for lead generation and outbound strategies to communicate promotions and events.
While SMS mobile marketing has been overshadowed by in-app advertising, it still remains a powerful strategy. On average, SMS marketing ads have a 98% open rate, a 45% conversion rate, and are typically read within three minutes of deployment. That makes it an impressively effective strategy for rapid engagement with a large volume of potential customers.
More importantly, SMS mobile marketing is widely used internationally, especially in regions like Europe and Southeast Asia. This broad reach is largely thanks to compatibility with non-smartphone cellular devices. SMS marketing is more strictly regulated than other marketing channels, but tends to benefit from having clearly defined best practices that are standardized through cellular carriers.
What are push notifications?
Push notifications are a type of message displayed on mobile devices by third-party apps that aren’t currently running. These notifications serve a variety of purposes, most commonly to inform users of incoming messages from social media apps. From a marketing perspective, push notifications are an ideal format for keeping users in the loop about new promotions or app features.
Above all else, the primary driver behind push notifications is customer retention. It’s easy for users to install and forget about an app, but push notifications let publishers and advertisers continue to communicate once the app is closed. Studies consistently show that push notifications can increase 90-day user retention from 3x to 10x depending on the effectiveness of your messaging.
What are QR codes?
QR codes are a type of matrix barcode that can be scanned by a mobile camera, usually activating a web link in the process. In mobile marketing, this allows advertisers to combine physical and digital marketing techniques by displaying QR codes in the real world. For example, a retail chain could place unique QR codes on receipts to link a customer’s online and offline identities, or a viral marketer might leave codes in public places as part of an augmented-reality game.
In the hands of mobile marketers, QR codes are unique tools that appeal to human curiosity can be placed anywhere, and are easy to track. Unfortunately, QR codes are also not as intuitive as other marketing strategies on this list, and tend to be used by a smaller subset of mobile users. That said, QR codes can be useful when deployed effectively, and are especially popular in regions like China.
What are mobile search ads?
Mobile search ads are standard search engine advertisements that are indexed and optimized for mobile devices. They can be displayed through a web page or search engine like Google, and typically integrate with smartphones to use features like “click to call.”
When search ads are optimized to match Google’s search interface, they have a higher chance of appearing when users search for related products or services on their mobile devices. Depending on the advertised business, a smartphone’s location service can also narrow down the search to relevant local companies. Google search ads can also feature a click-to-call button or click-to-install button as a call to action for your customers.
What are some mobile marketing best practices?
Always keep your audience in mind. A mobile marketing strategy that’s effective on social media won’t necessarily carry over to mobile games.
Be concise. Smartphones have limited screen space for deploying your message, and there are literally thousands of things users could do instead of viewing your ad. Get straight to the point, and give them a reason to engage with you.
Optimize websites for mobile devices. Much like our last point, transferring desktop-optimized web pages to mobile devices usually means your marketing efforts are lost to clutter and noise. Design mobile-specific versions of your sites that are optimized for on-the-go smartphone users, and build your marketing campaign around them.
Adopt multiple marketing strategies. There are many mobile marketing strategies available to advertisers in 2019. Don’t be afraid to experiment with models that show potential and reflect your brand.
Benchmark your results. Keep track of how users interact with each of your mobile marketing strategies. Follow conversion, retention, and engagement metrics to maximize your ROI.
Mobile marketing is a far more complicated field today than it was in 2000, but there are also far more ways to engage with your audience than ever before. By adopting the strategies listed above, your business will be well underway to expanding your reach across a variety of active channels.
May 20, 2019
Behavioural and Engagement Data Key to Success for Predictive Analytics
By Matthew Kelleher, RedEye
At a recent event I presented six case studies about the successful (i.e. they made money) application of Predictive Analytics. All the case studies were based on the application of Predictive Analytics to help target customers or prospects at various points along the customer journey, for instance, identifying the single buyers most likely to make a second purchase or which VIP customers were at greatest risk of lapsing.
At the end of the presentation I was asked, ‘What is your definition of Behavioural Data?’ I had repeatedly talked about the importance of accurate and complete data to drive Predictive Analytics and described 1st party customer data as falling in 3 types: transactional, engagement and behavioural. But I had fallen into the trap of failing to explain what I meant by each of the definitions I was using. So, very briefly:
- Transactional Data. Or RFM. Or RFV. What a customer has bought, how much, when, where… Data that is used to build RFM models (i.e. identifying prospects, single or multi buyers, VIPs etc.)
- Engagement. Points of interaction with the brand, from opening an email to receiving a catalogue to visiting the website.
- Behavioural Data. Usually related to website activity, what the prospect or customer has browsed, their recency frequency across devices, what they’ve clicked, if they visit the website direct from social, if they click on a discount offer, etc.
Behavioural data has always been critical. It is the core of data-driven personalisation. By building up, and allowing marketers to react to ‘behaviour’, from marketing the products someone is interested in through to identifying if the consumer is an offer junky or full price buyer, this is what underpins RedEye’s approach to Predictive and AI. Using this information to work in combination with engagement and transactional data identifies prospects and customers in terms of what someone will do next and when.
But despite presenting six case studies all showing conversion and revenue improvements, we can’t escape the fact that there is a little bit of market weariness to the subjects of Predictive Analytics and AI! Gartner are now stating in their Hype Cycle that Predictive Analytics has fallen into the trough of disillusionment! And with so many marketing tech businesses out there are talking about it, but not many are able to demonstrate the real value it can bring for retailers.
Back in 2016, Forbes research showed that 89% of marketers had Predictive Analytics on their roadmap. Fast forward to 2018 and 93% of consumer-facing businesses are unable to use Predictive Analytics. This really shows the disparity between the desire to implement Predictive Analytics vs. the actual implementation.
The aim of the presentation was to try to reinforce the potential value of these tools to the market, but the key is to start with data. Customers are getting more and more difficult to understand with the proliferation of marketing channels. Just recently WhatsApp was added into the fold, yet another channel that marketers can target their consumers through.
Brands are losing touch as they struggle to track all their customer’s moves, which in turn leads to a decline in customer loyalty. Consumers can feel their favourite brand just doesn’t understand them. As humans we just can’t keep up! This is where AI comes in!
Start by understanding your data. Where is it coming from? I recently joined a panel with the Head of CRM at Domino’s; he told me their journey began by creating a Single Customer view by collating their offline data and combining it with their online channels.
He was right: putting in the leg work at the beginning and creating a true Single Customer View was key. A Single Customer View means you can tie together transactional, engagement and behavioural data, allowing you to paint the full picture of your customers.
Finally, it is key to apply AI and Predictive Analytics to something tangible. At RedEye, our predictive models are based on the customer lifecycle. By making incremental improvements at each of the key customer moments, you can see substantial increases in overall customer value.
You can find more about how we’ve driven revenue increases for our clients using Predictive Analytics here.
Originally posted on the RedEye blog.
May 2, 2019
Customer Experience: A Tale of Two Opposing Views
By Jeff Teugels, Crossroad Consulting
One may not assume that a customer perceives a ‘customer experience’ as was intended by the provider. Conversely, an experience that is favorable for the customer does not always contribute a company’s performance. The challenge is to unite both perspectives.
Anno 2019, Customer Experience or ‘CX’ is omnipresent. The CX concept was introduced in 1982 by Holbrook and Hirschman as a holistic construct. In the meantime, both academics and practitioners believe that a favorable customer experience not only positively impacts customer satisfaction, customer loyalty, and word-of-mouth behavior – something customers themselves have known all along – but that it also is a compelling precursor of the much-coveted competitive advantage.
Despite this consensus, the CX concept remains foggy because the holistic construct has diverged into two mostly unconnected schools of thought. The main reason is that academics and practitioners tend to look at customer experience through one of two opposing lenses. One is the organizational lens and the other is the customer lens. This is one of the conclusions of a review of customer experience research since 1982 drawn by Kranzbühler et al. (2017).
The one who looks through the organizational lens assumes that experiences can be designed and that all customers will perceive stimuli alike. The one viewing through the customer lens ascertains that firms cannot deliver value since the customer is always a co-creator of value. While the former focuses on organizational structure, strategy, and customer-employee interactions, the latter considers individual customer journeys, cognition, affect, and senses.
Static and Dynamic Customer Experiences
A distinction is made between static and dynamic customer experiences. A static CX describes how an individual evaluates one or more touchpoints with an organization on a cognitive, affective, and sensory level at one specific point in time. A dynamic CX considers the evolving cognitive, affective, and sensory evaluation throughout the entire customer journey.
The organizational lens points to the design of static CX and to the management of dynamic CX, yet the focus on static CX tends to dominate. The customer lens analyzes customers’ perceptions in three planes: the static CX itself; how dynamic CXs are formed; and how cognition, affect, and the senses impact both static and dynamic CXs.
- The organizational lens focuses on what is within a company’s control; the customer lens looks at the whole picture.
As one can see, the famous customer journey is a key component of the customer lens. The ultimate goal of a customer journey is to “teach companies more about their customers in order to market better, sell faster and serve more effectively” (Milbrath, 2019). Being taught requires a willingness to learn and to shift to an outside-in view. Only then can one look inside one’s own processes and, with lessons learned from customers, improve them to match expectations.
Yet most organizations fail to truly master the art of customer journey mapping. Three reasons account for this fact. First, looking through the wrong lens sets one up for failure. Very few answers can be found in one’s navel.
- The one who’s usually left out of the customer journey mapping process is the one with the answers: the customer.
Secondly, the proliferation of touchpoints, channels, and offerings makes a customer journey non-linear, unclear, and unpredictable.
- The VUCA-world leaves most of us wandering around blind. Yet, nearly all answers are in plain sight outside.
Thirdly, in this day and age of big data, data still is unable to capture the emotions and feelings of customers.
- Organizations must develop, hire, or acquire the skills to turn an abundance of data into actionable behavioral insight and ditto foresight.
A study from the CMO Council published in 2016 among the top 150 senior marketers throughout Europe and the U.S. revealed that “only 5% of marketers say they have mastered the ability to adapt and predict the customer journey and what actions will derive maximum value.”
It can be done, and it takes two things to start
Among practitioners, the growing consensus is that one must look at the dynamic CX and not just at the static. Moreover, it takes the entire organization to support the customer journey. Two precursors to CX success are:
- You must know who your primary customers are
- You must develop a customer mindset
The first is the select group of people who unlock most value for your organization. The second is “vital to providing a consistent service that meets customers’ changing needs” (Brown, 2015).
- The two lenses need to merge: A successful, agile organization is interdependent from customer success. And customer success comes from deep behavioral insight and foresight.
To end with my mantra once again: “Customer behavior defines the organization; technology enables it, makes it efficient and scalable. Yet only hyper-relevance engages both customers and employees.”
References
Brown, L.R., and Brown, C.L., 2015. The Customer Culture Imperative: A Leader’s Guide to Driving Superior Performance. New York: McGraw-Hill Education.
CMO Council, 2016. Predicting Routes to Revenue: Identifying Real-Time Decisions for Business-Driving Engagement (White paper). San Jose, CA: CMO Council.
Kranzbühler, A-M., Kleijnen, M.H.P., Morgan, R.E., and Teerling, M., 2017. The Multilevel Nature of Customer Experience Research: An Integrative Review and Research Agenda. Oxford: International Journal of Management Reviews, British Academy of Management and Jon Wiley & Sons Ltd.
Milbrath, S., 2019. The fundamental flaw in customer journey mapping—and how to fix it. [online] Vision Critical Communications, Inc. Available at <https://www.visioncritical.com/blog/customer-journey-mapping> [Accessed 22 March 2019].
April 29, 2019
Data Lakes and B2B Customer Data Platforms: A Win-Win for Marketing and IT
By John Hurley, Radius
We’re hurtling at warp speed toward a time when customer experience will reign supreme. In fact, according to Walker, customer experience will supplant product and price as the key differentiator by 2020. In 2014, Gartner said in five years customer experience would be how 89% of businesses differentiate. It’s 2019 – time’s up!
The buyer’s expectation is to have a friction-free customer experience, even as that experience spans an increasing array of channels and touchpoints that rely on complex back-end business operations from vendors. Coordinating all touches along the journey is known as delivering a unified customer experience.
And it starts with unified data, which helps explain why solutions that help unify data are top of mind. Two of those solutions are Data Lakes and up-and-coming Customer Data Platforms (CDPs). Our team is increasingly seeing enterprises that aren’t sure how these two technologies relate and differentiate. It’s a matter of understanding each and how to take advantage of them to best serve the enterprise.
The Shortcomings of Data Lakes to Deliver Customer Experiences
The best place to start when assessing technology is always with a defined use case. Let’s take a look at some of the most common needs we hear from enterprise business teams.
A Data Lake can unify data, but it’s typically geared to serve enterprise-wide data and IT. In other words, it’s not tailored for the needs of go-to-market teams, like marketing and sales. Because it’s a repository for vast amounts of raw unstructured and structured data, a Data Lake can prove difficult to work with for those outside of IT.
They don’t include core capabilities needed to address business growth and the customer experience, like identity resolution or audience management tools. Yet these are essential for analyzing disparate customer and prospect data and combining them into a unified view.
Moreover, they lack out-of-the-box integrations with go-to-market systems and digital channels. Take the integrations for Snowflake, a leading cloud Data Lake. They do not integrate with the channels and solutions a marketer would need to run an effective campaign. Simply put, it’s challenging – and can be quite resource-intensive – adding new data sources and connecting new channels to a Data Lake, requiring coding and integration to enable new data feeds.
Since data is dumped into the lake without any up-front restructuring, resources are needed to apply advanced technologies to explore and gain insights from the data. This forces revenue teams to request data and reports from the IT department. Because these requests are not an IT priority, they often linger for weeks and sometimes months.
Go-to-market teams then have to prep the data for activation in its downstream systems – enriching and restructuring the data, such as validating email addresses and phone numbers – so it can be used effectively by sales and marketing.
This isn’t to deny the power of Data Lakes. They’re just not tuned to the needs of teams focused on driving revenue, customer engagement, and business operations. If the purpose of unifying data is to enable real-time decisions that help orchestrate a unified customer experience, then Data Lakes fall short. There’s no concept of creating – and easily acting upon – a single unified view of each customer in what is essentially a huge unstructured data repository.
CDPs to the Rescue: The Choice of B2B Go-to-Market Teams
That’s where CDPs come in. They make it possible for organizations to ingest and link customer and prospect data and all its detail from virtually any source – including third-party sources – in real time. As such, CDPs help unify siloed data around a customer view, yielding a persistent “golden record” of all knowable data about customers. Plus, CDPs make that record easily accessible on demand so marketers, sales, and revenue ops can ensure personalized and highly relevant customer interactions at every touchpoint.
No wonder Forrester predicts that 70% of B2B marketers will choose CDPs over data lakes in 2019.
As a system that creates a persistent, unified customer and prospect database accessible to other systems (as defined by the CDP Institute), a CDP better serves revenue teams. It’s a packaged platform that comes with prebuilt components and data models, enabling marketers and other business stakeholders to segment, analyze, and activate their data – no significant IT involvement required. In fact, go-to-market teams can easily share and activate data – and change or add system sources – without disrupting the CDP.
A Powerful Duo
Customer Data Platforms aren’t a replacement for Data Lakes. Instead, they’re a perfect complement to them. And, in many cases, enterprises already have – or need – both.
Both Data Lakes and CDPs are persistent stores implemented by IT that host customer data as part of big data infrastructure. But the similarities end there. IT-managed Data Lakes ingest enterprise-wide data – typically first-party data from internal sources – without altering the data form. On the other hand, marketer-managed CDPs unite first- and third-party data and enable a real-time flow of data into and out of the system.
With easy integrations to all channels along with built-in tools for even business users, a CDP makes it easy to view, pull, and analyze data and arrive at audience insights. Because of this – and the addition of third-party data – CDPs help enterprises improve their targeting and customer experiences. Moreover, easy channel integrations that yield net-new data pave the way for faster time to market and expanded customer reach.
In fact, Data Lakes are a key source of data for CDPs. At the same time, CDPs can help improve the quality and completeness of data in a Data Lake.
In essence, CDPs enable the alignment between revenue teams and IT when it comes to an enterprise’s data and technology ecosystem. While revenue teams can use CDPs to capitalize on data to drive business growth, IT teams can enjoy the benefits of CDPs and Data Lakes working in harmony to serve the business.
If you’re convinced your enterprise can benefit from a CDP, understand the pros and cons when it comes to building or buying one. And then download the Innovator’s Guide to B2B Customer Data Platforms – written by David Raab, founder of the Customer Data Platform Institute – and learn how to capture, unify, and activate scattered data.
April 25, 2019
Bad Sherlock and the Case of the Great Data Deception
By Jaeson Middleton and Dave Wardell, Intent HQ
Here’s the theory: if we take all the data we possess about customers, we could deduce a great deal and draw powerful insights. We could use this information to better look after our customers and sell better products and services, tailored to their needs.
It’s the Sherlock Holmes School of Marketing: stunning insights and conclusions drawn from the observation of disparate data. The first time Holmes and Watson meet, the detective deduces from looking at Watson that his new acquaintance is a convalescent army doctor freshly returned from the fighting in Afghanistan.
He explains: “Here is a gentleman of a medical type, but with the air of a military man. Clearly an army doctor, then. He has just come from the tropics, for his face is dark, and this is not the natural tint of his skin, for his wrists are fair. He has undergone hardship and sickness, as his haggard face says clearly. His left arm has been injured. He holds it in a stiff and unnatural manner. Where in the tropics could an English army doctor have seen much hardship and got his arm wounded? Clearly in Afghanistan.”
Clearly?
Well, maybe in those simpler Victorian days. But in our modern world, Sherlock might not be able to make sense of all of the data and may very well get it wrong:
Here is a gentleman of a medical type, but with the air of a military man.
[“I just like the look and the style. I’m not a medic – I work as a barista.”]
Clearly an army doctor, then. He has just come from the tropics, for his face is dark,
[“I just got back from a week in Ibiza.”]
and this is not the natural tint of his skin, for his wrists are fair.
[“Where I was wearing a wristband and a watch.”]
He has undergone hardship and sickness, as his haggard face says clearly.
[“It was a very heavy week, I am dehydrated and I seriously need to detox.”]
His left arm has been injured. He holds it in a stiff and unnatural manner.
[“I fell off a table during a drinking game.”]
So much for the famous detective’s showy parlour games. But there is more to Holmes’s view of the world than his logical pyrotechnics. His creator, Arthur Conan Doyle, knew a thing or two about information, as he shows in his books.
“Data! Data! Data!” he cried impatiently. “I can’t make bricks without clay.”
- Sherlock Holmes and The Adventure of the Copper Beeches
He knew the vital importance of possessing all the information, not aggregating or cutting out any data and being able to understand the patterns and connections.
“It is of the highest importance in the art of detection to be able to recognise, out of a number of facts, which are incidental and which vital. Otherwise your energy and attention must be dissipated instead of being concentrated.”
- The Adventure of the Reigate Squire
Moving from the nineteenth to the twenty-first century, we have radically changed the way we gather, analyse and interpret data. Modern police detection uses DNA profiling rather than Sherlock’s analysis of different cigar ashes, but twenty-first century marketing has failed to grasp the challenge and the opportunities.
Telcos in particular have not kept pace with the expectations of their consumers to provide them with a deeply personalised experience. There is plenty of effort, but much of it is wasted.
Most telcos still operate a marketing and customer experience culture embodied by the question: “What can we do next for our customers?” Campaign managers fight for the next 100,000 customers to put in the audience for their next offer. Contact centre colleagues are asked to sell the next best offer to broad segments of customers who simply aren’t interested. Customer journeys are mapped out at great expense to describe the customer experience for 10 to 20 different customer types.
Telcos do this because the data they have about their customers is limited. And most of the time it is also aggregated. They have CRM data, loyalty scheme data, third-party socio-demographic data, customer care contact data. This data tells the telco teams how much a customer has spent, what products they have, what they last complained about, or their propensity to churn. Laughably, some telcos call this their 360-degree customer profile.
This level of data used to be okay. But the world has changed. Customers’ expectations have massively increased, pushed up by the FANGs (Facebook, Amazon, Netflix and Google) of the world.
Let’s for a moment remove ourselves from big corporation mentality and consider how we think about and interact with our friends, family and neighbours. Humans interacting with humans. Let’s be more Sherlock.
Sherlock doesn’t think about Jane next door as a “Young Professional Mum”. He doesn’t think of her as a part of a segment. He thinks about Jane as a unique human, someone who works in the city, has a young daughter and enjoys running; she’s a fanatical cook and however much she tries, she can’t get on with the latest technology.
A Sherlock who gets his deductions right would look like a man of authority and intelligence – someone you could trust. A Bad Sherlock who ignored the data or didn’t understand the data would look like an idiot – a figure of ridicule.
Telcos face the same problem. They can deeply understand their customers, but they can (and have) alienated them by making broad assumptions based on data aggregation rather than personalised knowledge. Like a poor detective who keeps deducing wrong outcomes based on misinterpreted information. Customers lose confidence in the telcos, become frustrated and even start to mistrust them.
It’s paradoxical, my dear Watson.
“I had,” he said, “come to an entirely erroneous conclusion, my dear Watson. How dangerous it always is to reason from insufficient data.”
- Sherlock Holmes and The Adventure of the Speckled Band
April 22, 2019
How to Build the Case for a CDP in Your Retail Organization
By Kristen Carlson, QuickPivot
Interest in CDPs continues to accelerate, with more retailers realizing that traditional IT-led data management processes aren’t keeping pace with trends in customer engagement, including customers that are demanding a seamless experience across channels. In fact, according to Gartner’s Market Guide for Customer Data Platforms, in 2018, Gartner client inquiries pertaining to CDPs doubled when compared with the same period the previous year.
If you are a marketer that understands that a CDP will bring significant benefits to your organization, that it should be the center of a modern marketing stack, collecting data and feeding it to your organization’s multiple channels, how do you build the case for CDP implementation?
Make a Compelling Case to Senior Management
With so much to gain from a CDP deployment, it makes good business sense to spearhead the initiative within your organization. But how do you convince senior management to take that next step and implement a CDP?
We know that presenting a new budget item to senior management can be a hard sell, especially when they may not be familiar with CDPs, so we created a plan to help. In our guide, Building the Case for a CDP in Your Organization, we lay out four steps to help you demonstrate the marketing, operational and financial benefits of a CDP to your senior management.
In the guide, we suggest the following steps:
- Highlight the three big benefits of a CDP
- Demonstrate how a CDP will benefit multiple departments
- Show use cases with results
- Address the financials
Demonstrate How Performance Lift and Cost Savings Will Improve the Bottom Line
The first step involves demonstrating how a CDP will improve the bottom line by creating greater efficiencies in personnel and resources, and allowing for more automated, higher velocity marketing. It’s important for senior management to understand that a CDP will enable them to derive more value from customer data that they already have.
Next, make the case by showing how, even though a CDP will be owned and managed by marketing, it can positively impact other departments in your organization. Departments like IT, Finance, Merchandising, and even the C-Suite itself will realize improvements in effectiveness and reductions in cost.
Further make the case by introducing real-life case studies which show the positive results realized when retailers implement a CDP in their organization. Our guide provides examples of two such cases.
Finally, address the financials by demonstrating that many retailers see a return on investment in a CDP in under a year and some even see increased ROI in less than six months, depending on organizational goals.
A CDP’s value can be hard to argue against when you focus on the proven benefits: more effective and high-velocity marketing, increased efficiencies, lower costs and more satisfied customers. To learn how to make the case for moving forward with a CDP implementation, read our guide, Building the Case for a CDP in Your Organization.
April 18, 2019
Expected Excellence - The Growing Demand for Personalisation
By Sherrylyn Heise, IntentHQ
According to Forrester, “A laser-like focus on Customer Experience (CX) is the best path to business success”. Telcos need to up their game if they are to keep pace with the expectations of their customers. The key to this is the development of a more meaningful and relevant personalised experience.
Over the last 20 years, there’s no piece of technology that has revolutionised the way we live and work more than the mobile phone. It has transformed from a clunky curiosity to a powerful pocket computer that is all things to all people.
During that time, we have grown accustomed to the benefits of personalised products, services and messages. We receive traffic updates during our daily commute, listen to automatically generated playlists and travel with an umbrella because Alexa warned that rain was likely. No matter where we are, we expect our technology to cater to our individual needs.
Personalisation is not just a great selling feature, but a fundamental expectation customers have towards any provider. However, it is time to expand our thinking beyond features powered by personalisation and start a discourse on how we can make this the next big differentiator in the telecommunications market.
The fine line between convenient features and personalisation has not always been this clear. We remember a time where phones started offering T9, a method to predict text input, to make typing text messages faster, followed by the ability to send two texts, exceeding the character limit without having to press ‘send’ twice. While these features were milestones of the mobile generation, customers still did not expect their mobile phone provider to consider them as an individual./p>
Continuingly growing expectations represent a challenge that the FANG companies (Facebook, Amazon, Netflix, Google) have met. They never see customers merely as users of their technology. Instead, they aspired to make their products part of their customers’ lives. This required them not just to know whether John had trouble finding the right product online or the next series to watch, but rather knowing that John has two sons, the name of his favourite football team and the fact that he believes ski vacations are just too expensive for the whole family.
To achieve this, they need data and the ability to predict choices, spending patterns and future behaviour. They certainly didn’t get it right the first time, and every time, but customers initially accepted the existence of unrelated junk mail or sales calls for products that were irrelevant to their needs. As their marketing reach grew more powerful, we came to rely on things like Facebook birthday reminders, targeted shopping advice from Amazon and the fact that Google really does know where we live.
FANG companies base their success on three key building blocks:
- Access to unfiltered, day-to-day life action data
- The ability to extract insights from this data that creates a concept of individuality but respects the customer’s privacy, and
- Rewarding customers’ trust by providing actual value.
Since the introduction of GDPR and international laws to follow, data sensitivity and a transparent correlation between data provided and returned value is fundamental to successful customer relations. Judging by these building blocks, telcos have the same, if not greater opportunities to create personal customer experiences.
Next Generation Customer Service and the Path to Successful Business
Successful companies have created truly loyal followers, not just through the superiority of their products and services, but by building highly personal relationships with their customers right from their first interactions. Companies such as Apple, Nike and Instagram have created loyal followers, guarding the business from bad publicity and supportive of the idea that not every new product release will be superior to the competition. Marketing and Customer Service lead a crucial role in establishing not just professional, but highly personal relationships with their customers. That said, customers are - of course - fully aware of the fact that a company with millions of users is not able to create a one-on-one personal connection. They do, however, expect an engine of intelligence smart enough to respect the customer as an individual with complex needs and unique circumstances.
If automated intelligence in marketing and support is paired with an informed customer service agent, customer interaction can create highly personalised experiences. The customer journey can be narrowed down to a few key experiences. We can all recall situations of calling customer service because we cancelled a service too late, we thought we were billed incorrectly or we finally picked up the phone after receiving 13 unrelated marketing communications. What customers remember is not necessarily the problem, but how it was handled by the business they engaged with. If these crucial situations are handled not only poorly but also impersonally, most positive experiences throughout the customer’s journey will be tainted and will put your business with this customer at risk. Lastly, in times of digitally shared knowledge, negative as well as positive experiences with customer service can be spread rapidly and at large, potentially risking your relationship with the rest of your customer base.
A Transformation to “Customer First”
Telcos are facing an opportunity impossible to ignore - the market is demanding personalisation and most telecommunication providers are using little to no technology to address this need. We consider three ways in which telcos can transform their operations and improve their performance. Tackling these issues will enable telcos to compete in the market going forward.
- They must stop storing data as a random asset without returning visible, significant value to their customers. Most users are aware their data is being stored by their mobile providers, but it is questionable whether the value and reasoning is obvious.
- Telcos need to lower their reliance on raw, plain data. Instead, they should apply methods of intelligence and machine learning to their datasets. Imagine how your customer service could change if you understood that a customer was actually paying for a phone used by his daughter. For example, would you offer him a better plan, knowing his wife lives abroad and streaming via FaceTime is a crucial element of his daily life?
- They should evolve their internal processes. They must train their customer service and marketing staff to understand when an exception is appropriate to the rule. They need to be able to provide safe management of data and allow service agents to understand customers as individuals.
The challenge is significant and impossible to ignore. Download our white paper “Customer First Transformation” to learn how you can begin to overcome these barriers.
April 15, 2019
Using AI Intelligently, or How to Avoid the “Zombie Marketer” Apocalypse
By Monica Landers, StoryFit
All marketers are storytellers. You’ve heard this a million times because it’s true. As a marketer, you identify key audiences, get to know them, then craft a narrative that helps each of those audiences picture themselves using your product or service and being happier as a result.
It’s no different when the thing you’re marketing is actually a story. It may seem like the marketers on the front lines of Hollywood and the publishing industry have it easy — after all, how many potential ways are there to sell people on a new superhero movie? But the truth is they’re dealing with a lot of the same market pressures and limitations as the rest of the marketing industry, and their job is only getting tougher.
The Plot Thickens
Entertainment is one of the most oversaturated markets in existence right now, with mass amounts of brand-new content competing for audiences’ time and money every day. And with digital marketing ushering in a new era of targeting capabilities, marketers are able to take a much more surgical approach to reaching the people who will ultimately be interested in their content.
The new challenge is getting those audience profiles as accurate and detailed as possible. Anyone who subscribes to Netflix has probably found themselves laughing at some of the categories the streaming service identifies as “interesting” to them. Maybe you were surprised to find out you liked “strong female leads in stories of revenge” or films about “wine and beverage appreciation,” but however the service categorized your interests, they went way beyond just recommending something because you were a “black female” or a “male under 35.”
Most of us would agree we’d rather be targeted based on our actual personalities and preferences than broad stereotypes. And this is how entertainment marketers want to approach their jobs across the board.
But it’s challenging. Because unlike Netflix, marketers do not have built-in algorithms. They don’t see every movie or TV show that comes out each day, and they certainly don’t read every book. They’re also likely to have biased views on the content they do consume, making it hard to “categorize” things properly. (Is “The Handmaid’s Tale” appealing to people with strong feminist ideals or fans of dystopian dramas? The answer is probably both, but if a marketer only identifies with one of those groups, they may ignore or downplay the other category.)
So if a marketer hasn’t seen “Beginners” (or seen it through the eyes of a diverse audience), they may not realize that it shares thematic and cinematic similarities with the project they’re currently promoting. And therefore, they may totally miss out on some valuable audience profiling data.
How AI Can Help
You may see where this is going already. To help entertainment marketers be more effective, we can equip them with the data and algorithms they need to make informed, surgical marketing decisions.
My company StoryFit has a database of millions of film scripts, teleplays, and novels, with new ones being added every day. Our AI breaks down those scripts to make them easier to categorize across detailed metrics like the personality profiles of the characters, thematic elements, the type of story arc (rags to riches, etc.), and even things like whether they pass the Bechdel Test.
Marketers can find comps to their current project, see exactly where the similarities exist, and use that information to target audiences who responded well to the comparable content. They can also look back at marketing campaigns for those properties and see, for example, how they were structured tonally and what aspects of the story they focused on to make more informed decisions about their own campaigns. Comps can even be helpful when determining — and defending — marketing budgets.
Can StoryFit’s database help in the development process as well? Of course, but it’s a tool that has to be wielded wisely. Producers who find themselves reverse engineering scripts from scratch based on what has worked in the past are likely to find the results disappointing. The human brain is well-tuned to identify unoriginality, and playing it too safe will come back to bite studios and publishers over time.
Ideally, if producers or writers are using the tool, they’ll come to it with a list of questions or benchmarks. Are my characters registering as complex, and are they sufficiently different from each other? Do female characters have at least 50% of the dialog? Do they have characteristics that are usually reserved for male characters (are they trailblazers)? Which famous character is my leading male most similar to, and am I happy with that comparison, or should I rethink a few things to make him fresher? In other words, it’s a tool that can help creatives see their stories objectively and decide whether they like what they’re seeing.
As AI starts to become more of a fixture in entertainment marketing and marketing as a whole, it’s important that we use it to augment and improve our decisions, not make them for us. AI is one valuable voice in the room. It should not be the sole decision maker or sole opinion we listen to, any more than we’d listen to one team member at the exclusion of all others.
Machine learning should help marketers across industries become superheroes, not zombies. So as you start to craft your marketing stories, look to AI where possible for data that makes you smarter. Then let your humanity take the wheel, with clearer knowledge of the best routes and the lay of the landscape ahead.
April 11, 2019
The 360° Customer View – Dispel the Myth!
By Mark Hiseman, IntentHQ
Telco Customer Experience and Marketing teams are failing to make emotional connections with their customers. They are frustrated because they are unable to use all of their data to develop rich customer insights. To add to their frustration they see other companies using data in a more personalised and engaging way than they are - we call this the Great Competitive Paradox.
But do telcos have the key to find the Holy Grail - a complete view of the customer?
Local shopkeepers know their customers personally because they meet them and speak to them regularly. They build relationships based on familiarity and trust, and that makes doing business easier.
For the telcos, the challenge is to build a deeply human connection by generating insight about customers’ behaviour based on all the available data, not just the basic CRM and service data.
Making an accurate prediction of what customers want next might be difficult - but it’s not impossible. Let’s look at John, a customer. He’s a 44-year old man with an iPhone X and is on the 10GB data tariff. He spends $60 a month and has been with his service provider for four years. He lives in New York and has a low propensity to churn.
That tells us something, but it’s light years from a complete customer view.
Add a little more data and you discover he also has another contract for an iPad and a prepaid account. His iPad contract is the 1GB tariff and he tops up the prepaid account $10 every month.
Is this a ‘complete’ view?
What about other product lines? He doesn’t have any fixed-line services, but the address does. Third-party data tells us the household details and that John is married to Jane. Our data tells us that these services are in his wife’s name. So now we know John is married and the household view shows associated fixed and mobile services. This additional data indicates they have a child. Do we have a ‘complete’ view now?
What about adding some more data in our search for crystal clarity?
- Has John contacted his service provider in the last 12 months, visited the website or used the app?
- Has he responded to a customer satisfaction survey?
- Maybe he’s raised an issue, complained to the regulator or been on social media?
Let’s add all this data in, too.
So now we know his family and domestic arrangements, the phone he uses, how much he spends, and we have a somewhat blurry picture of his relationship with his service provider.
We can learn more. What about service usage? Perhaps John experiences higher than average dropped calls or maybe has a poor data experience. We should include this quality of service information because that will provide a view of his experience of the network. We have some good propensity models too - such as propensity to churn or Next Best Offer models. They are pretty accurate, so let’s add those in.
Have we yet got to the ‘complete’ view?
We know John’s participation in previous Marketing offers and also in our Loyalty or Reward scheme. We know which offers he clicked on and accepted or declined. He’s given us some personal information in exchange for Loyalty Scheme offers. We still haven’t reached the ‘complete’ view.
This level of customer insight is usually the best it gets for a typical telco. In the past, this level of understanding was ok, but the world has changed. This customer’s expectations have massively increased, pushed up by the FANGs of the world.
We need to add a lot more insight in order to gain a deeply human understanding of John and in turn give John a highly personalised customer experience.
We have the details of who John calls and messages and who calls or messages him in our billing records. We can use this information to identify key interests. We know what websites and apps he visits, so we can use that, too. There’s so much data here that could provide even more insight into John as a person rather than a commodity.
Data is fine, but understanding is the key to greater wallet share. You have to add meaning to the data; otherwise, it is useless. The fact that John frequently visits a website tells us little. Is it a coffee shop or a clothing retailer? If he calls a number, is it a ticket line for the cinema or is he calling a friend?
To really make sense of this cumbersome data, you have to be able to give it meaning; otherwise, it’s just a bunch of words and numbers. The key to all this is to process the data in a way that allows you to create a micro-database for each and every customer. You can add meaning to the data which in turn delivers a depth of insight never experienced before. Instead of knowing John frequently visits the usual suspects in terms of websites, you now know he has interests and brand preferences. He likes coffee, football, dogs and Formula 1 racing. You know he’s interacting with your competitors and he’s looking to move house.
Once you understand the behaviour, you can also infer attributes. Remember John has a prepaid account? The interests associated with this account are tennis, cosmetics and university. Targeting a football-related loyalty offer to this customer is not going to be successful. The behaviour suggests an 18-22-year-old female, probably their daughter, and John pays for her account.
Now you have a human-like understanding of your customer, for all your millions of customers.
You can micro-segment like never before, create tribes of customers with similar behaviour and target with enormous precision to deliver a 1-2-1 personalised experience. This means no more spam. Now you are processing your data and giving it meaning. The profile is as complete as your data allows. Maybe now you can say you do have a 360° view of your customer, and maybe now you can say you have a deeply human understanding of them.
See how O2 have started to challenge the Great Competitive paradox link here.
Contact me at mark.hiseman@intenthq.com to learn more about human-like 360° profiles.
April 8, 2019
The Evolution of the Independent Data Driven Marketer, in 3 (and a Half) Motivating Steps
By Rony Vexelman, Optimove
In today’s demanding marketing orb, every marketer’s quiver has to be armed with the right tools that will allow access to customized reports filled with the most recent data. Here’s the starting point of your quest.
Ever come into your office, look at your calendar, and see right there in printed black and white that today is the day of your big presentation? You walk into the meeting with the higher-ups, sharing how the marketing campaigns are doing great, feeling confident about the progress. And then, someone in the room asks you to prove your work’s impact. “How does it impact revenue or order amount?” Things that move the needle, they call them.
I know what I used to do. I’d go right to the finance team and ask for sales numbers from the day before. I’d have the marketing team put together a report on our campaigns, so we could attribute sales to each campaign. And I would sit down in front of my Excel and start stitching data together to prove the campaigns’ effectiveness.
By this point, much of the morning – not to mention lunchtime – would pass by with little progress. I knew a change was due. And that’s when I began my evolution into an independent data-driven marketer.
Answer Their Questions
The first step? Standardized Autonomous Reports. The type you can find inside your ESP or marketing automation platform. A report provides the exact number of customers who performed a given action. If the system is really advanced, you would even have a dollar amount attached to those numbers.
Standardized reports helped me identify which campaigns had better CTRs and which generated a strong uplift in revenue. They took much of the manual work out of my day-to-day. But I felt we could take this further. There are many articles out there on why marketers need next-best-action recommendations or insights into missed opportunities and potential optimizations. None of which is apparent from standard reports. Additionally, with a small team, it eventually became too difficult to measure our entire operation.
The second step of my journey led me to Smart Reports that could help marketers detect customers at risk of churn and which campaigns could bring them back from near abyss. Ones that provide insights into which campaigns had a significant impact on customer lifetime value or lacked any sort of impact at all. They could even help me see the parts of my database that weren’t being targeted at the required frequency, solving the all too common case of marketer tunnel vision.
And to better answer those company-specific questions that await at the management meeting, we have step three: a Consultant or External Supplier who could help create custom reports with the insights I needed: impact of my marketing efforts for my VIPs, aggregated campaign reports filtered by customer lifecycle and the like.
The Final Ingredient
I’ll admit this solved my problem. I was now an independent data-driven marketer, armed with the tools to prove how campaigns impact the business in any metric we could come up with. But, if you’ve read this far, you know something is still off. I felt too dependent on this external team to send me the report at a certain cadence. Like most marketers, I want access to customized reports filled with the most recent data whenever I want.
Enter integrated BI Dashboards. Customized and dynamic, integrated within the marketing solution of my choice, and always up to date. Reports that support today’s omnichannel marketing efforts and allow me to visually analyze revenue from different lifecycle stages, broken down by device type. Reports that aren’t static, that could filter dates, devices or stages from the total with the click of a button and see the revenue distribution at any given point.
This solution allowed me to slice and dice the data in a visual and intuitive manner. This was the final step for me, the stamp of approval for my personal growth as a modern marketer.
This journey did not take a day or a week. It began with understanding if the team were to achieve marketing agility, then we needed to own the data. From customer lists to sales results, we required access under one interface. We had to truly understand what the most important metrics were. We had to acknowledge the limitations, know when help is needed to uncover the deepest insights from marketing efforts either through smart reports or an external consultant. And finally, dream big. Think hard about the most dynamic report that could answer the toughest questions in an interactive way and find the BI tool that could deliver this promise.
April 4, 2019
AgilOne Welcomes Adobe and Salesforce to the CDP Party
By Gangadhar Konduri, AgilOne
This week we saw interesting and exciting announcements from Adobe and Salesforce. They both announced their Customer Data Platform (CDP) offerings, which seem to be in different stages of development. We will discuss our detailed thoughts on these product announcements in future blog posts once they share more details. But, to start with, we want to extend a hearty welcome to Adobe and Salesforce to the Enterprise CDP party -- a party AgilOne started and been at the forefront of for several years already.
Both announcements acknowledge the excitement about CDPs, the legitimate need for CDPs, and that many brands are in the midst of their CDP initiatives. Both announcements acknowledge the definition of a CDP to include, at a minimum, the following features:
- Bringing transaction and interaction data together from multiple data sources
- Identity management as the critical core of the CDP
- Customer intelligence powered by AI is a key part of a CDP
- Driving customer engagement based on this rich data and insights in a CDP
In fact, we at AgilOne have been saying this all along. We worked with analysts (the CDP Institute, Gartner, and others) and the market to arrive at this definition of a CDP based on our exciting and growing enterprise customer base.
Contrast that with how less than a year ago, these and other vendors dismissed CDP as a passing fad, while at the same time they made attempts at partial solutions that were not well received by customers or analysts. The market forces were too strong for them to continue this approach. Now they are acknowledging and building their own CDP offerings, perhaps finally with an understanding of why CDPs became a “marketing category that enjoyed instant attention,” as referenced in Salesforce’s announcement.
AgilOne’s enterprise-grade CDP has been used by brands for many years as they reaped the benefits of a CDP, and we evolved our platform based on real client usage. Other brands, still grappling with what a CDP is and trying to gain customer adoption, don’t have this same benefit of experience and platform maturity that is a response to real world use cases. Because of this, we have a unique perspective:
- We have learned that brands have their data sources spread across enterprise applications from Salesforce, Oracle, Adobe, IBM, SAP, applications from many other vendors, plus custom and homegrown applications. For most brands, a pure walled garden doesn’t exist.
- During these years, we developed the AgilOne enterprise-grade CDP, hardened it in production with usage and massive data processing for all our clients, and enhanced it with additional modules based on client feedback (see our case studies for more details).
- We’ve learned that no two brands are alike and configurability is critical to a CDP. (Read about AgilOne’s unique approach to configurability and how this supports enterprise use cases).
- Enterprise CDP goes far beyond adoption by digital marketing teams. A CDP is also used by, and provides benefits to, Customer Service, Customer Analytics, Data Science, and all teams that benefit from having access to unified customer intelligence.
All these new entrants to the CDP party will need to go through the same growth curve as they move from this announcement stage to actual customer usage. However, we are happy that these announcements could help accelerate the market adoption of CDPs and thus increase the adoption of the AgilOne CDP. So, Salesforce and Adobe, we welcome you to the enterprise CDP party and to a growing and exciting market!
P.S. We will be back with our thoughts on their product announcements in another blog post (though we are not holding our breath for those vendors to develop and publish more details any time soon...). Until then, here are some interesting articles for your reading enjoyment:
April 1, 2019
If You Can’t Beat Them, Join Them: Adobe & Salesforce Join the CDP Club
By Tasso Argyros, ActionIQ
3 Questions Every Brand Should Ask About Their Own CDP Investments
The Customer Data Platform market officially expanded this week with both Adobe and Salesforce throwing their clouds into the CDP ring. For those of us who have been building this category for the last 5 years – we want to say welcome to the club! This is a huge acknowledgment from the two biggest players in marketing technology space, so we wanted to provide some context and clarity around the announcements and how we view this shift.
What Do These Announcements Mean?
Adobe and Salesforce’s expertise is not in customer data. Their pedigree is in content (think Adobe Photoshop) and business applications (think Sales Cloud). Both “built” their marketing product portfolio (Cloud) through a multitude of channel-focused acquisitions (think Campaign and ExactTarget – both ESPs). Fundamentally, these are execution-first products with very limited data capabilities.
These announcements mark Adobe and Salesforce’s attempts to pivot down from the execution and content layer into customer data. This is BIG. Not just for the marketing clouds, but also for the Customer Data Platform market. For the last two years, we at ActionIQ have been evangelizing the value of CDPs for enterprise marketers. In the last week, we’ve had two very large voices join the club and validate the market need – welcome, Adobe & Salesforce!
Why Are Adobe & Salesforce Pivoting to Customer Data?
Let’s just say it’s been a journey. Their first response was to say, “We already do that!” But as the CDP market picked up momentum, they were forced to respond. Salesforce called CDPs a “passing fad” and Adobe announced a new data layer they described as “more than CDP”. Fast forward to just a year later, Salesforce is finally committing to check-the-box CDP, and Adobe announces a (another?) CDP. What changed?
Ultimately, the market and their own clients forced them to make this change. We know for a fact that in the last 6-12 months both Adobe and Salesforce have lost out to smaller startups with CDP capabilities that power personalization at scale. We also know that once the honeymoon wears off from an Adobe or Salesforce marketing cloud purchase (think 12-18 months), enterprises start to realize that data integration and unification is not their core competency, nor the reality of their offering. Both the market and clients have succeeded in pressuring the marketing clouds to acknowledge their existing data gap and promise a future solution.
How Should Brands Think About This?
Now for the most important question, how should brands evaluate these announcements against their marketing strategy and capabilities? This can be summarized in 3 simple questions all brands should ask themselves:
1. Do you believe Adobe and Salesforce can build a CDP?
This goes back to an organization’s core competencies – do they have the pedigree and proof points to make you believe they can pull this off? Building a CDP requires deep expertise in data integration and architecture which becomes the foundation of the entire platform. Data must be a core competency and this can’t be solved through yet another acquisition.
2. If you believe they can build a CDP, can you afford to wait?
This won’t be a quick fix. At ActionIQ, we spent the first two years of our existence building out this data layer (and we didn’t have to deal with decades of legacy technology). Building foundational data infrastructure takes time. Big companies have a major force working against them that hyper-focused startups don’t – momentum. And not the good momentum, but the kind that makes doing anything new extremely difficult. Change is hard, and when it comes to something as foundational as core data infrastructure, it takes a long time.
3. If you can afford to wait, does it make sense for you to go all-in on a single cloud?
To answer this question, you have to ask another question – do you expect Adobe or Salesforce to be the only vendor you ever use, i.e., is it reasonable for your business to live completely within their walled garden?
Even if you believe Adobe or Salesforce can build their own CDP in a reasonable amount of time, you would still be constrained to only the channels that live within their cloud. Vendor lock-in is a major risk a company must consider, and in this case, it’s lock-in of your entire marketing tech stack. At ActionIQ, we believe that brands should have the flexibility to choose best-in-class channel solutions for themselves and reduce switching costs when their strategy changes or the vendor falls behind.
In closing, we see the last week as a massive win for the Customer Data Platform market and, more importantly, for brands wanting to be more personalized and customer-centric. CDPs will be a foundational layer in the enterprise marketing stack going forward – powering personalization at scale while optimizing marketing performance and efficiency. With these announcements, everyone has now acknowledged that single view of the customer is an unsolved problem and we can all move on to evaluating who has the best solution to solve it not just in the future, but right now.
March 28, 2019
What’s Missing from Salesforce Customer Data Platform?
By John Hurley, Radius
After Salesforce denounced the need and longevity of CDPs, they’re announcing one of their own. The claim is that there is a need for "Enterprise-grade CDP” – one platform to rule them all. By defining their vision, Salesforce does in many ways help clarify where CDPs fit in the tech stack and how they could be used to ultimately create compelling customer experiences for consumers. Customer Data Platforms may be the technology category with the most variance across vendors, with one product looking a lot like BI, one like marketing automation, and the next bordering on master data management (MDM). So I welcome any large cloud provider like Oracle CX Unity or Salesforce raising awareness of the category and guiding the market toward a standard definition.
However, Salesforce’s vision statement also adds to the confusion. I see several gaps, or details that were left out or intentionally overlooked, that will mislead the market toward the CDP that Salesforce wants you to see.
Definition of Enterprise Grade
Salesforce claims to “deliver the first real enterprise-grade CDP.” But what makes a platform enterprise-grade? It is scalability, flexibility, and trust.
First, scalability is critical to a customer data platform. Customer data growth is exponential across volume, variety, velocity, and veracity. The platform must be able to handle massive loads of customer data at once and respond as the data changes. Data storage, matching and validation pipelines, and APIs all must be built for scale. Imagine the data from several enterprise CRMs, marketing automation platforms, dozens of third-party data providers, and data lakes all synced and managed by a single platform. That’s enterprise scale.
Second is an open platform that’s flexible to the customization large enterprises require — purpose-built interfaces, APIs with broad endpoints and detailed documentation, and transparent processes. While the use cases can be generalized to terms like ‘data management’ and ’segmentation,’ each enterprise is a snowflake with unique needs.
Lastly is trust built from a focus on security, privacy, and responsibility. Enterprises across every industry often claim they are becoming technology and data companies. Customer data has become their most precious asset. Platforms must have security and privacy as a pillar to both their technology and ethos to work with enterprises. They also must showcase the values enterprises so desperately as trying to encompass. Is positive impact part of the pitch? Are you using data for good versus evil? How are you giving back?
No knock on Salesforce here. They’re gods of the enterprise cloud. They have enterprise solutions and have mastered selling them. I look forward to seeing if their CDP lives up to the criteria of enterprise-grade.
Where’s the “Data Platform”?
Breaking down a Customer Data Platform into its various parts and where it sits across your technology suite is a worthy exercise. Salesforce lists “Insights Platform” and “Engagement Platform” as the two things marketers want. They understate the foundation to both insight and engagement – and that’s data.
CMO, CIO, and CDOs’ first phase is operational efficiency. How can we clean up and unify the data we have, augment it with critical data we don’t have, and then use our mastered data to improve our decisions as an organization? These actions can be very tactical, like enriching CRM data, or very strategic, like migrating from on-premise data lakes to cloud data management. Next is differentiating with rich customer experience. How do we use the master customer data to create faster, more personalized, delightful experiences for our customers?
Building platforms on top of a shaky foundation puts big promises for customer experience at risk. End-to-end Customer Data Platforms must fully commit to their role in data transformation.
Revenue Operations Inefficiency
Let’s piggyback on the importance of enterprise-grade characteristics and “data platform” specific capabilities. CDPs that address fundamental data accuracy and unification open up a smattering of other use cases under the umbrella we call Revenue Ops. Data is essential to the revenue-focused responsibilities of operations experts who:
- Formulate go-to-market strategies
- Optimize sales and marketing processes
- Implement technologies and manage their workflows
- Deliver data across organizations and systems
A sales ops team with a CDP that offers a robust data management toolset can do better routing, territory planning, forecasting, and strategic planning. Data teams can use CDPs to find data redundancies across internal and external sources that lead to significant cost savings. Strategic groups concerned with data portability can use the open, scalable data infrastructure of a CDP to break down enterprise-wide silos for a broad set of needs. We see customers using CDPs to migrate from one system to another (CRM to CRM), manage M&A go-to-market integration (i.e., customer overlap, joint-market planning, system consolidation), and even analyze shared opportunities with partners.
Is Salesforce definition of a CDP too narrow? They could make the argument that all these technical solutions are in pursuit of better customer experience. We believe the applications and gains for revenue ops are so significant that they deserve their own call out.
Nothing B2B-Specific
As usual, solutions built for B2B are always an afterthought. While some technologies can serve both those targeting consumers and businesses, Customer Data Platforms do not translate well. An email to a consumer is not all that different than an email to a business buyer. A consumer website visitor who converts in a shopping cart is tracked similarly to a business buyer that converts on a demo request landing page. Both B2C and B2B CDPs are in pursuit of a single view of the customer, but the tech required to handle the complexity of B2B data is wildly different.
Data architecture in B2B is built to ingest, match, resolve, and manage a variety of data types: business records, hierarchies, buying centers and locations, contacts, departments, intent data, behavioral data, and countless others. To create a high-performing platform tuned to business data requires years of dedicated work from expert data scientists and engineers. Moreover, unlike their empty-box B2C counterparts, B2B CDPs often come with external data accessible within the platforms. This data is used for enrichment, total address market analysis, segmentation, audience activation, and prospecting lists.
Experts in CDPs generally agree that B2C CDPs are weaker on:
- Third-party data on-boarding
- Proprietary prospect data sourcing
- Data stewardship (hygiene, enrichment)
- Rich top-down profiles of business and contacts
Salesforce’s vision for their CDP isn’t wrong. However, it lacks detail and does not encapsulate the broad applications and technologies that have become known as Customer Data Platforms.
March 25, 2019
Build or Buy Your B2B Customer Data Platform?
By John Hurley, Radius
Looking to reduce your total cost of ownership and accelerate time-to-market? Don’t build a customer data platform from scratch for data management and integration — buy one and build an innovative solution that drives competitive differentiation.
“Organizations investing too much in building or customizing systems of record have less funding available for differentiating applications.”
This statement from Gartner has proven all too correct for many business struggling to create their own customized B2B data collection and distribution systems from scratch. While you might understandably wish for a bespoke solution to meet your exact specifications, there’s a much smarter way to get there — by buying a powerful, out-of-the-box platform that is also open for customization. Then use the money you’ve saved on total cost of ownership (TCO) and time-to-market efficiency to build differentiating applications as needed.
Buy Customer Data Platform (CDP) to Standardize, Build to Compete
Creating systems of record that integrate data flawlessly and connect readily to outside applications is no small feat. This is especially true in the B2B space. Any organization hoping to build a B2B data management system from the ground up must cope with time-consuming, laborious issues:
● B2B Data Architecture: You can’t onboard and manage your B2B data until you’ve created a reliable, well-tuned infrastructure to handle business records, hierarchies, locations, contacts, departments, intent data, behavioral data, and countless other details. Creating a reliable architecture that can actually do all these things requires years of dedicated work from expert data scientists and engineers. That’s talent and brainpower you could (and should) be devoting to more specialized applications.
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● Aggregated 3rd-Party Data: External data allows for data enrichment, net-new data sourcing for prospecting and reaching out to new audiences, and more advanced analytics such as total addressable market analysis, territory planning, and risk assessment. But aggregating a robust 3rd-party data ecosystem internally is very costly and impractical, especially when you consider the issues of maintaining that ever-evolving data.
● Pre-Built Integrations and APIs: Designing and developing the “pipes” to connect and integrate your various customer and prospect data systems is a major investment. Why re-invent APIs when many vendors offer ready-made, flexible, open, well-documented APIs and native integrations built specifically to onboard, register, and catalogue B2B data sources? Don’t forget the additional need to integrate your data with other key go-to-market systems such as ad platforms for activation of your data. You’re better off purchasing a pre-built solution and then focusing on putting that data to work for your business operations, and marketing and sales channels.
The ROI of Buying a Customer Data Platform
Buying an established, pre-built B2B data management platform frees up your engineering resources to focus on your core product and equips go-to-market teams with the integrated tools and data they need to drive meaningful customer experiences. By leveraging a CDP’s economies of scale and expertise in B2B data integration, management, and sourcing, you can create your customizations on a trusted, continually improving platform that is built on best-of-breed tech. The benefits of the platform, along with access to expert teams and communities of peers, can yield dramatic returns as opposed to building:
● Reduce Total Cost of Ownership (TCO): Access existing data, tools, and integrations as your foundation. The solutions on the market may be best-in-class for your needs, or good enough when looking at the cost to build exactly what you want. Your costs can be 3-5x if you choose to hire internal teams, commission technology-enabled service providers to build a custom solution, or buy the entire big data tech stack. Case in point: Comcast Business reduced their TCO by 75%, meaning the solution they were building and maintaining was four times as expensive!
● Accelerate Time-to-Market: While the goal is to eliminate data silos, a risk for new centralized data sources is that they often end up creating a new, sometimes larger silo. For instance, the promise of Data Lakes for marketers was that their teams could do granular segmentation and analysis on audiences from this wealth of data. The problem was data requests required IT, were not fulfilled for weeks, and the data then had to be re-prepped for activation since the data lakes were not connected to sales and marketing channels. Customer Data Platforms enable both IT and revenue teams, and connect to the systems critical to reach more buyers.
● Extensibility & Innovation: Achieve data unification and data stewardship across your existing systems with a Customer Data Platform, and benefit from their innovation and yours. These platforms are well-funded, growing businesses that are constantly releasing new capabilities that you will benefit from. If you’re an enterprise, look for open, flexible options that will allow you to customize and scale to your needs. Beyond the core capabilities of the Customer Data Platforms, other bleeding-edge technologies required holistic, accurate data like BI and analytics, customer experience engines, AI-driven applications. CDPs with robust APIs can make trusted, unified data accessible to these powerful systems.
Forrester’s Steven Casey predicts of the companies investing in data unification, 75% will choose a CDP over Data Lakes and custom build solutions. Packaged Customer Data Platforms and other data management solutions open the door to more than new code, apps, API, documentation and integration. Increase your business agility, experience the ease of maintenance, and access expert teams. You’ll be glad you bought these proven, expertly-crafted, fully-integrated solutions instead of trying to build them yourself!
Want to learn more about CDPs? Watch my webinar “CDP 101” and download the slides. If you’re currently assessing CDP vendors, I highly recommend our Action Brief 5 Key Steps to Streamline Your CDP Purchase Process. If you have any questions or comments, feel free to reach out to me directly – john@radius.com.
March 22, 2019
The CIO and CMO Are Natural Partners: Here’s Why
By Ariella Brown, Zylotech
Different strengths are involved in the various C-suite roles, which accounts for different personalities in the CMO and CIO roles. While that could sometimes bring up opposing points of view, they need to come together to work on the martech. The CMO provides the customer data needed for driving revenue, and the CIO has to set up the IT infrastructure that can extract value from the data. When they both work together, they realize greater efficiency and impact.
The great divide between CMOs and CIOs
Not all CIOs and CMOs are succeeding in working together, according to a pair of recent Deloitte surveys. When surveying CIOs, they found that over 50 percent ranked their relationship with CMOs as “weak or very weak.” That exceeds the number -- 42 percent -- that said they are collaborating with marketing. CMOs don’t have a much rosier take. The majority of CMOs surveyed considered their relationship with their company’s CIOs far from strong. Less than a quarter of the CMOs identified “the CIO as a key alley and champion.”
It’s possible that the small sample size of under 200 CMOs may not be large enough to represent the groups. However, Liz Miller, senior vice president of marketing at the CMO Council, offers an explanation for the two feeling out of sync. CMOs sometimes fail to consult with CIOs in the planning stage. “That isn’t involving technology in the strategy; it’s making IT an afterthought,” she asserts, and not giving those involved in planning the tech the consideration due “valued strategic partners.”
Likewise, in a CMO.com article, Cindy Jennings cites CIOs complaining that they aren’t brought in to set clearly defined aims with input from both sides. That translates into “unrealistic schedules, reactive and poor planning, and shifting requirements that broaden a project’s scope and complexity.” When the marketing department tries to find its own solutions, it can also end up bringing in things without IT’s knowledge, as is often the case for shadow IT.
Jennings also presents the CMO’s point of view that the primary causes of failed partnerships they identify include “a shortage of technically skilled resources, insufficient funding, and a lack of support from IT.” They also find that the tech is not always up to the speed demanded by responsive marketing. In addition, she observed, she has noted “lack of effective governance, a dearth of relevant data, and resistance to organizational change” obstructing collaboration.
Then there is also the fundamental difference in outlook between CMOs and CIOs. As Jennings puts it: “Marketing thinks in campaigns and outcomes. IT, on the other hand, approaches projects from a process standpoint.” But it is possible to bridge across this divide to achieve the ends needed in today’s data-rich and responsive marketing.
Getting the two to come together
In “Successful Digital Transformation Starts With A Beautiful CMO-CIO Relationship,” Peter Horst lists three leadership behaviors that got the CMO and CIO on track to work together. The first was “collaborative decision-making,” which demanded that both sides were brought to the table. The second was “embracing ambiguity,” which entailed accepting that not everything was going to be clearly set from the start, and they would not lose sight of the forest in ticking off every individual tree. The third was “empowered decision-making, whereby choices were generally made by the people doing the work rather than at an executive level.”
Horst’s approach is consistent with Deloitte findings dating back to 2015. The key points that emerged from the survey of C-Suite executives. One of the key findings was the primacy of “shared vision.” Like Horst’s “collaborative decision-making,” this criterion called for being sure both sides agreed to “clear marketing goals, collaborative technology selection, and a defined governance framework for data and technology access.”
The collaboration then had to extend to implementation teams, which relates to the question of who makes the decisions and how they would work through the details. “Shared responsibility helps build stronger cross-teams.” With teams made up of people from both departments, it’s possible to bring in the strengths of each in a complementary way. Marketing’s growing demands of efficiency, speed and availability then push the expectation for IT, which is there to be sure that the demands can be met with their insight into the capabilities of the technology.
As Jennings explained, the marketers can plan their campaigns around data and realize the IT department, which will “know where it is stored, how privacy is protected, if or how it’s integrated with marketing systems, the integrity of the data, and whether it can be delivered in the right format at the right time.” She likened it to the CMO’s saying what a building should look like, and the CIO coming in to be sure it can be engineered.
It is that difference of perspective between the CMO and CIO which makes them natural partners. You need the expertise in marketing vision to be counterbalanced by the expertise in technological capacity. This is the way to build the marketing campaigns of the future.
As Henry David Thoreau said, “If you have built castles in the air, your work need not be lost; that is where they should be. Now put the foundations under them.” The partnership allows the CMO to plan the castles in the air and work with the CIO to set up their foundations.
March 20, 2019
Give your Customers the Chance to Fine-Tune their Preferences
By Eilon Morgenstern, Optimove
Your customers want more control. Present them the subscription preferences option and they’ll repay you with their loyalty. While a one-click unsubscribe option limits your ability to communicate with customers, a preference center gives them more opportunities to remain in touch with your brand.
As marketers, one of our main responsibilities is to continuously distribute personalized content, creating a win-win situation: captivating our customers and earning more engagement for our business. One way to achieve both end-goals is by asking our customers, our subscribers, to share their preferences, giving them the ability to choose. Out of this desire, the preference center was born. The email preference center is a function that allows subscribers to pick and choose which emails they receive from you and the frequency at which those emails arrive.
Customers unsubscribe from our emails for several reasons. In most cases, the culprits are high frequency and irrelevant content. If you send an email to the wrong customer at the wrong time, they’ll point their cursor right at the unsubscribe button and sever their relationship with your business. The email preference center solves this issue.
Email Preference Center Types
There are three main types of preference centers:
- Time-based - customers can choose the frequency in which they receive emails
- Content-based - customers can choose which type of content they want to read
- Hybrid - a combination of time-based and content-based types
1. Time-based
If your business sends daily communications that aren’t varied to a significant degree, we recommend brands present customers with the option to choose the email frequency. Maybe you have an engaged customer who’s happy to receive an email a day, while someone else prefers communication only once a month. We suggest building your time-based preference center around the following three parameters:
- Email frequency (twice a week, once a week, or once a month)
- Receiving emails on a specific day during the week
- Option to unsubscribe from a specific time period
Or, maybe you’ll opt for the next preference center type:
2. Content-based
If your content consists of several different topics, build a preference center around your categories. There are a few ways you can categorize the content-based preference center:
The product/service type - Maps all your services and products and divides them into clear categories. For example, a travel company could categorize their offerings as:
- Flights
- Hotels
- Car rentals
- Day trips
Or if you’re a fashion retailer, you could group items into:
- Men
- Women
- Kids
- Accessories
Based on this method, the customer can choose the content that best serves their objectives. This method is especially helpful because you can use the data from the email preference center and apply it to your segmentation model.
Medium type - According to this method, we’ll create categories based on the mediums we use in our marketing. The most common mediums are:
- Weekly promotions
- Weekly newsletter
- Blog
- Announcements
- Recommendations
- Reminders
Unlike product or service type, the disadvantage here is that we don’t have the information on our customers’ product preferences. We can, however, assume that there is a high correlation between the number of mediums the customer chooses and their engagement level with our brand.
Content-Based Hybrid - Using this method, we’ll divide selections into medium and product type. As in the example below, if we choose personalized recommendations, we’ll further enable customers to specify what they’re seeking:
3. Content- and Time-Based Hybrid
The benefit of combining the content- and time-based solution is that we’re able to provide our customers with a robust list of preference options. The risk is that a hybrid solution may appear overly complicated to the customer and instead of sifting through all the options, they’ll opt for the easiest solution, unsubscribing from our communications altogether. If we choose this type, it’s important we keep the design simple. Below is one example of the content and time-based hybrid:
Structuring the UI
After we define the content categories, we need to build the email preference center page. A few tips:
- All of the content must be on one page. Customers should not need to use extraneous navigation to refine their preferences.
- Avoid using links that take customers off the preference center page or tooltips that hide the information from plain sight.
- Keep the text short and concise.
- Include the customer’s entire email for easy identification and validation.
- Yes, you need an unsubscribe option; it’s required under CAN-SPAM, CASL, and other spam laws.
- Make sure your preference center is mobile-friendly.
Where to Put Your Preference Center
Sticking your preference center in any old spot won’t do. To maximize your preference center’s potential, make it accessible to all your customers through the following tips:
- Present your preference center to subscribers right after they register, so you can immediately begin catering to their needs and show them you’re dedicated to listening to what they want from the very beginning.
- Add the center as a link to your marketing emails to provide subscribers with easy access to adjust their preferences whenever they wish. To reduce the number of unsubscribers, include the unsubscribe option inside the link to the preference center. By using only one link, you’ll likely decrease the number of customers who opt out of your communications.
- Every so often, go over your unsubscribe list and see who hasn’t been active for a set period of time. Your next step is to give them the option to update their preferences. Sometimes, this kind of mail can create reengagement with subscribers who might’ve lost interest.
In Conclusion
Take a look at your offerings and determine which preference center will best serve your customers. Are you regularly sending out emails where the content isn’t drastically different across the board? Or do you have segments of customers who each gain something different from your communications? It’s time to reap the rewards from the mutually beneficial preference center. Matching your content with customers’ needs increases their satisfaction with your brand and reduces the likelihood of unsubscribers. Think of the email preference center as a gift for you and your customers, one that’s tailored to their needs and will result in the long-lasting relationship you desire.
March 19, 2019
CDP Industry Confusion Doesn't Invalidate CDP Industry
David M. Raab, CDP Institute
Technology analysts comment on the CDP industry all the time. I usually don't respond. But since the Winterberry Group's recent report criticized the CDP Institute by name, some comment seems appropriate.
The main critique of the report is that the CDP Institute includes too many vendors in its list of CDPs and applies too inclusive a definition. We recognize the confusion in the marketplace. But we feel that a narrow definition of CDP would not solve the problem, since there's nothing to prevent vendors from calling themselves CDPs whether we agree or not. Rather, we believe buyers are best served by including any vendor whose system solves the problem that a CDP is intended to solve -- building a persistent, shareable customer database -- and then helping buyers understand how these systems differ.
We have recently been using a division of the market into three types of CDPs: those that primarily build the database, those that offer analytics in addition to the database, and those that provide personalization in addition to the database and analytics. Our reports, including the latest industry update (download here), specify which vendors fall into each category. This, along with the more detailed information in our Vendor Comparison Report (download here), is a good place for buyers to start sorting out which vendors they should consider in depth.
There's plenty more work to be done to reduce confusion. Indeed, there are several projects under way at the Institute to do this. I don't want to disclose them prematurely but will note that it has recently become clear that we need to subdivide the "data CDP" category between vendors that primarily collect data and vendors who are expert at building and distributing the customer database. So look for the number of CDP categories to grow.
This may sound like a recipe for still more confusion, but our plan is to avoid that by making it easier for buyers to understand which use cases are supported by each category. That will let them start by considering all possible solutions and then quickly narrow their consideration to a suitable subset. Educating buyers and arming them with relevant information will be the real key to reducing confusion. Ignoring the crowded reality of the CDP marketplace won't help at all.
March 18, 2019
Three Things Retailers Need to Properly Segment Customer Data
By Kristen Carlson, QuickPivot
Most marketers, especially those in the retail industry, understand the importance and value of customer segmentation and personalization. However, that doesn’t mean that most marketers are successfully executing it. The reality is that segmentation and personalization can be overwhelming and dizzying when you start to look at the sheer amount of data, all the possible sources of data and the potential technical barriers.
Yes, retail customer segmentation takes some pre-planning and foundational work, but it doesn’t need to be headache-inducing. Here are the three things retailers need to put into place before they begin to segment customer data.
Task 1: Data Integration
The first step is to identify all channels, vendors and sources of customer data. This could be catalog, online, in-store, email, social media and mobile, just to name a few. Before you even address the technical component of compiling this data, we know that there may be organizational barriers to overcome. All too often the marketing department is divided by channels, reducing opportunities for collaboration and making it more difficult to gain a 360-degree view of the customer.
The key is to identify an executive sponsor, ideally someone who oversees the entire marketing department and can understand the value of bringing together data across marketing channels. The second step is to identify technology that can aggregate all of the data into a flexible and elastic contact database. Look for a tool made for marketers, rather than one that provides marketing insights but still requires a technical team or SQL specialists.
Task 2: Trust in the Data
Once you have all your cross-channel data in one place, the question is whether you trust it or not. Duplicates, old data, wrong data, empty fields and more can wreak havoc on the success of your customer segmentation strategy.
Look for a contact database that includes data cleansing, normalization, matching and merging to ensure the highest data quality. You should also develop a proper data cleansing regimen to maintain data cleanliness levels.
Task 3: A Well-Defined Goal
You have all of the data possible on your customers and prospects. You trust that the data is accurate. Now, you need to create an actual strategy for what you want to achieve with your customer segmentation. “Better personalization” or “more specific targeting” won’t cut it. Instead, think in terms of outcomes such as reduce shopping cart abandonment, increase average customer spend, and increase store traffic.
Once you have your goals in place, you can begin to brainstorm the segments and lists that you need to create and market to. Marketing should be able to own the segmentation process from start to finish, rather than relying on an analytics or technical team, as it’ll make your team faster and more creative.
It’s undeniable that we are living in the era of Big Data, and retailers will either sink or swim. Retail customer segmentation doesn’t need to be overwhelming or scary. If you have ideas for what your marketing team could do with cross-channel data, QuickPivot wants to make them a reality.
Request a demo today to see QuickPivot in action and discuss whether or not it could be a fit.
March 15, 2019
Self-Learning in Customer Analytics: A Primer
By Chuck Leddy, Zylotech
Self-learning is now a key marketing technology and a major trend for 2019 and beyond, according to Gartner. While self-learning technology may be neither widely recognized nor well-understood, the trend is already having a broad, burgeoning impact on marketing. This post serves as a self-learning primer, explaining what self-learning is and why Chief Marketing Officers (CMOs) need to care about it.
What is self-learning?
Put simply, self-learning helps any system get smarter over time, based on insights generated from collected data. It’s a key component of machine learning, which is a subset of artificial intelligence. All these terms refer to software that performs a particular function based on fixed rules, typically provided through an algorithm written by a human programmer.
In classical software development, the inputs or data that provide the foundation for the software are fixed, and so the rules are also fixed (by a human programmer). Machine learning systems are programmed/trained not by human programmers, but by the data the systems collect: this data, filtered through self-learning software, teaches the system to get smarter as more data is collected.
To illustrate, let’s look at one major challenge machine learning is tackling today: the maintenance of high tech industrial machinery (wind turbines, industrial robots, etc.). General Electric is as much a software company as an industrial company. Each piece of GE equipment has sensors attached that collect performance data from the equipment. All that collected data gets poured into self-learning software that “learns” how to maintain and optimize each piece of equipment. So GE’s high-tech equipment maintenance system, called Predix, is being “trained” to optimize its equipment in real-time with data provided by the equipment itself.
Self-learning should thus be seen as a software-enabled version of our cognitive skills. These self-taught systems have the ability to “learn” and adjust from aggregated data: they can grasp complicated concepts and gain insights from the data.
How does self-learning fit into machine learning systems?
It gets a bit “data science-y” here. All machine learning systems have three interrelated parts: (1) a model, which makes predictions; (2) parameters, which are the factors used by the model to inform its predictions/decisions; and (3) self-learning, which dynamically adjusts the parameters and the model by harmonizing differences in predictions versus actual outcomes.
The initial model, developed by a programmer, may be quite accurate/predictive (or not), but the self-learning software adapts the system over time according to how the model is performing in the real-world. Where there are gaps between what the model predicts and what actually happens, self-learning intervenes to dynamically adjust the model. Put simply, the system adapts based on the data it collects.
Why should CMOs and marketers care about self-learning?
Self-learning can be applied to the entire customer experience (CX) through a self-learning customer analytics platform, one that enables marketers to collect multi-channel data about the CX and dynamically tailor content delivery. Marketers can deliver relevant content by “learning” (1) where the customer is in their journey; (2) what behavioral factors drive that customer; and (3) what content they might need at each step along the way. Self-learning enables marketers to deeply understand customer behaviors, enrich customer profiles, make predictions about customer behaviors, map the entire CX, and then scale customer personalization and precision in ways that simply didn’t exist before. That’s powerful.
Self-learning can also help marketers identify new customer segments and create a more unified marketing and analytics system to constantly drive relevance and ROI. With the help of a self-learning customer analytics engine, marketers can truly influence purchasing decisions, drive upselling/cross-selling, and proactively enhance brand loyalty over time.
Finally, customer analytics provided by a self-learning system enable marketers to identify (and predict) areas where customers might be experiencing friction (i.e., places where your funnel is leaky and prospects are abandoning you), allowing marketers to respond proactively to improve customer engagement and conversion rates. With self-learning, your funnel becomes less leaky over time.
What’s the final takeaway here? You should think of a self-learning customer analytics platform as always delivering real-time signals (data) from your customers for you to drive enhanced CXs and revenues. Now if that isn’t relevant for CMOs and marketers, then nothing is.
March 13, 2019
Make Your Customer Data Platform a Corporate-Wide Asset
By Tom Treanor, Arm Treasure Data
Three Rules to Keep You From Building Yet Another Customer Data Silo
Who “owns” the customer experience at your company? For companies like yours – seeking to deliver outcomes that keep customers engaged and loyal – few questions are more important, and unfortunately, more complicated.
The problem is that responsibility for the customer experience is often diffused across different business units that interact with customers across their complicated journey and collect different types of customer data: point-of-sale, online browsing experiences, mobile and other customer apps, Internet of Things (IoT), customer service – and the list goes on.
How Customer Data Platforms Can Boost Effectiveness Across Your Business
Let’s say you head up one of these business units. You want to be effective. You need to understand your customer. And you understand that this requires data visibility. So, you set out to solve this problem by either stitching together multiple systems or trying to build your own customer data platform (CDP) for a single source of truth for all customer information.
Remember, though, that you’re not alone. Chances are there’s someone at your company in another department trying to do the same thing as you read this. Databases of customer information are everywhere. Most of them are homegrown, best-of-breed databases that someone, somewhere attempted to piece together to finally solve this ‘single view of the customer’ only exacerbated the problem with yet another data silo.
How, then, do you implement a solution that actually serves as the single source of truth for all your customer information? The key is to approach a CDP as a valuable corporate-level asset that is sanctioned by IT and the business, yet owned and operated by the marketing team. Make it a system designed to yield visibility into your customers for the entire organization, regardless of the data source. From what we’ve seen at Arm Treasure Data implementations – and in other organizations as well –successful CDP-using organizations usually follow these three rules.
Rule #1: Get support from the top.
It’s important to start at the highest levels of your organization. The objective is to get everyone working together, aligning with the same goals of customer intimacy, loyalty and ROI. Many customer data platforms on the market today emphasize quick and fast, out-of-the-box functionality – but that just won’t cut it now, with the explosion of customer data sources. No way!
And unless the entire organization is on the same page, you’re looking at another data silo that gives you half-measures and half-results.
This is an important insight, and companies are already grappling with these problems. Recognizing the need for a holistic approach, some organizations have even developed C-level positions such as Chief of Customer Experience. Whatever your specific approach, executive engagement is key. If your business is to be truly customer-centric, it needs to be customer-centric from top to bottom, and you need to measure meaningful KPIs that cut across your teams.
Rule #2: Involve IT to empower users with self-service.
Data projects that don’t involve IT are problematic on multiple levels. Chief among them is security. If you’re striving to improve the customer experience, the last thing you need is a security lapse that will put your customers – and your company – at risk. Best to have IT on board from the ground up.
The IT department is also important because of its reach. A customer data platform extends across the entire enterprise – and that’s the whole point. Leaving IT out of the picture will be an exercise in futility.
Not that IT needs to do all the heavy lifting. Rather, they should be your supporter and champion because you have done your homework and given them the peace of mind to let you run your business, engage with your customers in meaningful, holistic ways. Your customer data platform should be cloud-based, which alleviates IT of the complexity involved in an on-premise implementation and maintenance. Be sure to look for offerings with a comprehensive library of data connectors that help speed data consolidation. Also critical: professional services for tasks like building custom connectors to proprietary sources such as customer apps.
Ultimately, your goal is to empower business users throughout your organization with powerful self-service capabilities. The role of IT is that of checkpoint to validate the technical approach taken. Once you’re up and running, IT should feel confident enough to take a step back and allow people in marketing and elsewhere to take it away. Even connecting new customer data sources as they emerge should be the responsibility of business-level data stewards because you have done the heavy lifting early on and implemented a system built for scale.
All of this requires that you strike the right balance between IT and business users. Security is critical, as is proper integration into your environment. But platforms that require IT involvement at every twist and turn simply will not keep pace, and your project will fail.
Rule #3: Start with a proof of concept.
An enterprise-wide customer data platform does not mean that you have to boil the ocean by doing everything at once. To get off the ground (and get support from the top per Rule #1), start with a focused proof of concept (PoC) to identify low-hanging fruit: anything likely to yield high value quickly and visibly.
To demonstrate value, a PoC should unite a minimum of two data sources such as web site interactions and in-store point of sale transactions. This will help jump-start the data-driven, cross-business collaboration required to deliver a holistic customer experience.
Good, quick PoC projects – virtually limitless in variation – tend to focus on a key KPI. Here are some examples for inspiration:
Whatever PoC you choose to pursue, don’t underestimate its importance as a showcase. It will show what’s possible with a proper enterprise-grade customer data platform. It will demonstrate to your colleagues the power of a consolidated, holistic view of the customer. It will generate more than buy-in; it creates enthusiasm and inspires collaboration.
March 11, 2019
Going Direct-to-Consumer? A Primer for Marketers
By Kristen Carlson, QuickPivot
Brands selling direct to consumers (D2C) is not a new concept; however, in the past few years we’ve seen explosive growth in this strategy. For decades, large consumer brands have executed hybrid selling strategies by operating both traditional distribution channels and direct-to-consumer channels such as ecommerce websites and self-branded stores. What has changed in the past few years, though, is the ease and speed in which a brand can now create a direct-to-consumer channel, and how modern logistics make it easier than ever to get a product into a consumer’s hand.
These digital and logistic advances have harkened in a new generation of disruptive brands that are changing the retail ecosystem. Although these new brands are admittedly small, they are growing at an amazing speed, while many of the legacy incumbents who have been slow to adapt are facing slowdowns in growth. Just look at the grocery business. While grocery store growth is projected to be about 1 percent annually through 2022, growth for meal-order kits is expected to grow 10x over that same period.
The advantages of D2C are stark. Not only does a company assume more control of their brand and how it’s represented, but it also reaps the instant benefit of higher margins.
Executing a D2C strategy, however, is not without its challenges. The onus is now on you, the brand, to understand your consumers, connect with them and create the best possible experience for each user, every time.
We’ve outlined the fundamental elements for any company, big or small, considering ‘going D2C.’ These steps will prepare your marketing department to brace against the challenge, while supporting a vibrant direct-to-consumer channel.
Get your customer data in one place – and make sure it’s owned by marketing, not IT
Even if the marketing department is well-aligned, if IT owns and manages master customer databases, the procedural hang-ups can significantly hamstring your efforts to engage with customers quickly, which is the heart and soul of D2C.
When building lists of customer segmentations is contingent on an IT ticket that takes days to fulfill, or specialized data management skillsets not typical of marketing professionals, the resulting delay makes even the most basic marketing campaigns that much more difficult to produce and the speed required for well-oiled D2C virtually impossible.
Learn the difference between useful and ineffectual customer data – clear the noise
Your business most likely already has a CRM system that houses a lot of customer data, but how much of that information is useful and organized in a practical way for marketing to use? CRMs were built to manage and track customer transactions, and while some of this information is useful, other data fields, such as SKU numbers and payment cards used, just get in the way of marketers being able to do their jobs.
Marketers looking to implement a D2C strategy need to review their entire marketing tech stack and first identify the systems that are generating customer data. From there, they should review each of the systems to pinpoint the data that can be beneficial for marketing purposes and ignore what can’t.
Break out of the channel mindset – your customers are not thinking in terms of channels and neither should you
When it comes to reviewing all of the systems in your marketing stack, rather than having each marketing group review the technology specific to their group, create a cross-functional team to perform this exercise and look across the entire marketing department.
To date, many marketing teams have been organized by channel, but modern marketing teams need to tear down these silos and instead begin to think in terms of the customer journey. As a team, marketers should consider the following questions to develop their cross-channel marketing strategy:
- What are the behavioral pathways and attitudes people take at each stage of a purchase journey? Where do they get inspiration and information, and where does that flip over to actual shopping?
- What kinds of products and product attributes do customers prefer across all product-lines and categories? These insights come from analyzing website touches and product-purchase history and can help you develop key preference indicators for your products.
- What are the incremental results from various types of offers?
- How does a customer behavior change during life moments (such as having a child, getting a new job, or relocating) and seasonal events? This analysis provides a better understanding for what’s underpinning purchase frequency.
Make sure your entire marketing team has access to the same data – knock down the silos
Marketing teams cannot work in harmony to expose the above insights if they do not have access to the same information or the means to communicate openly among one another. Meaningful collaboration starts by spreading data access, along with appending privileges, evenly across the team.
Finally, future proof your technology
Technology is evolving at such a rate that in five years, a brand’s most valuable source of customer data could be a channel that we can’t even fathom right now. D2C marketers need to prepare for all types of data, data sources and data formats, and have technology in place that is able to ingest that information.
Additionally, evolving technology like machine learning can continuously analyze purchase trends and call out a wide variety of behavioral patterns. These systems can prompt the marketing team to take appropriate action, whether it means preventing a customer from going away, predicting a follow-up purchase in the near term, or targeting consumers that look like they’d be an ideal customer.
No individual can ever know for certain what’s coming next, and sustained D2C success hinges upon having as much information about current and future customer inclinations as possible.
Learn how a Customer Data Platform can bolster your move to D2C. Contact us and we’ll connect you with one of our CDP experts.
March 8, 2019
4 Tips for CMOs Wanting to Build a Data-Driven Culture
By Chuck Leddy, Zylotech
All Chief Marketing Officers know that collecting data isn’t enough. The real value of data is in leveraging it to develop behavioral insights about your customers and using those data-driven insights to inform better decision-making in real time, driving marketing relevance. To perform this essential work of deeply understanding your customers you need a customer analytics platform that accesses, analyzes and renders your customer data fully actionable.
As the amount of data accelerates, poor data quality will make it harder to implement a successful data-driven culture. Your marketing stack and marketing team exist for just one reason: to drive an understanding of (and engagement with) your customers that supports more relevant customer experiences. What are the capabilities that CMOs need to execute on a strategy that improves marketing’s efficiency, agility and customer engagement?
1. Integrate your “data house.” The challenge that many CMOs confront in leveraging their customer data is that it’s often trapped in various silos. In pursuing the latest “shiny object” or hot trend, companies proliferate tools and platforms that end up becoming “data spaghetti.” Another problem is data becoming outdated if it’s not continuously updated in real time. So the starting point is to get a platform that brings you agility and decentralization with governance and centralization of your customer data.
You should look to standardize your customer data, remove duplicates, and enhance it further with missing information towards getting a single source of truth about your customers across their entire organization. By doing so, you also open up the opportunity to connect specific marketing activities to ROI, enabling the type of marketing attribution that enhances your credibility in the C-suite (where “prove it” is the mantra).
2. Self-learning benefits: drive memorable customer experiences with deep, actionable customer insights. A self-learning customer analytics platform analyzes customer data in near real time to give marketers deep, actionable insights that support strengthened customer engagement, the type that fosters cross-selling, upselling, and higher lifetime value. By uncovering valuable behavioral trends and patterns in your customer data, a self-learning customer analytics platform enables you to map the customer’s journey and reach out to your customers with relevant, personalized messaging across the web, email, and any other marketing channel. The beauty of self-learning is that the platform learns as it goes, unlocking and offering deeper insights about customers as it collects more data. A self-learning customer analytics platform can thus be seen as a 24/7, “always-on” customer engagement/nurturing engine.
3. Automate key drivers of your CX with AI. Autonomous, data-driven decision-making at scale is fast becoming a reality, especially as artificial intelligence continues to emerge. Let’s explore one simple example to understand the enormous possibilities.
The global B2B telecommunications industry, which generates over $30 billion annually, is renowned for promotions, as firms like Verizon, Vodafone, and AT&T battle for B2B customers. A B2B customer might call their existing telecom provider and say, “I have a better offer for my business from a competitor/other provider. What can you do for me?” A telecom company can now create an end-to-end automated process where those business customers are asked to take a photograph of the offer or send a text message. The AI-enabled system would then read the offer and, since the system maintains an ongoing database of B2B telecom offers, confirm that it’s real.
Then it looks at the customer’s lifetime value and history and, based on that, computes a response: “Here is the updated deal we can offer your business today. We want to keep you.” All this takes place in real time, with no humans involved, except in setting up the data/AI system.
4. Democratize your customer data. Key customer insights gleaned from data were once accessible only to the few: the leadership team and data scientists. In today’s marketing landscape, everyone in your organization impacts the customer experience and therefore must have access and use of relevant customer data. It takes a clear, cross-departmental approach to sharing and leveraging your customer data across the organization, something a customer analytics platform enables.
The culture, mindsets, and capacities of your people are as important as, and sometimes more important than, any technology. Today, human and technological capacities are increasingly blended into hybrid solutions. In fact, technology won’t be of much use unless you mobilize your organization around creating a data-centric and customer-centric culture. Meeting and improving the customer experience is a key priority for CMOs, requiring that your people have the tools and capabilities needed to succeed.
March 6, 2019
Personalization: Going Beyond Demographic Data
By Korina Velasco, Lytics
There’s a reason personalization is marketing’s holy grail. When you reach the right customers with the right offers at the right time, the results exceed expectations. Instead of centering marketing efforts around content, future-proof companies are centering their efforts around individual users and their unique needs, then developing content and experiences that are tailored to those unique needs.
Download our latest guide, "Personalization at Scale: 1:1 Marketing to the Millions," and learn how to implement true personalization to the millions.
March 4, 2019
How to Assess Your Data Requirements When Implementing a Customer Data Platform
By Sanjay Kupae, Manthan
Customer data is no longer just an analytics requirement, or something used to send out marketing messages – it is the source of critical competitive advantage for a B2C business.
Truly customer-centric organizations use customer data for all aspect of business – from guiding product roadmap and categories, designing rich experiences, pricing their offerings right, creating the right customer service interfaces, deciding sales channels and even to make the right hiring decisions. Modern marketers use these customer insights to form a unified understanding of every customer and create contextual experiences that strengthen the relationship with the customer, ensuring loyalty and higher word of mouth.
Every company today collects more data about their customers that they use. They can also easily acquire additional, reliable data from data companies, and each byte presents insights that were not available before.
As a first step when deploying a Customer Data Platform (CDP), know what use cases you want to enable – this decides what data needs to be brought together. At this stage, assess your present data and what you need to acquire. Controlling customer churn, lifecycle marketing, and marketing attribution all require certain unique data sets.
The use cases and user types will dictate the format to store data in, the frequency of refresh and ingestion (real-time or batch) data, transformation required and responsiveness.
For example, in an e-commerce retail business like Amazon, purchase history is needed to provide personalized recommendations to customers. While digital data such as web-browsing/ app-browsing, items added to cart, saved for later, searched or viewed is captured, there is value in plugging in additional information such as income bracket, size of family, lifestyle, nature of work, types of vacations taken. This provides deep insights into what the customer would like, and the customer is pleasantly surprised with the retailer’s next-best product recommendations.
Organization owned customer data from the CDP foundation
Let us start with the data you have. If you have a Customer Relationship Management (CRM) program, provide any level of customer service or have a functioning loyalty program, then you have a solid foundation for your customer data platform. The CRM and loyalty systems can provide insights about demographics, purchase behavior, loyalty, and customer lifetime value and also contain the basic identity details.
But to do this right, it’s necessary to have a single customer record across all touchpoints by removing duplicate entries and resolving mismatches, that are created due to multiple reasons: incorrect data entry, different name variations of a customer, customers with multiple loyalty cards, change in their address etc. (Read more about solving the customer identity puzzle here.) After de-duplication and linking customer records, you know the real size of your customer base and have more accurate profile snapshots.
Transactional and digital data generates actionable insights for strategic gains
If your business engages your customers in more than one channel, for example through the store and e-commerce, or over mobile apps, that’s a new set of data streaming in, which can enhance your understanding of your customers. Again, it’s important to link customer IDs across different channels, functions and customer devices to have a holistic customer view, that does not vary based on who is accessing it and where.
Most businesses use outbound digital communication data for pointed interventions, but information such as search, browsing patterns, offer engagement, wish-listing, abandoned carts all tell you something about the customer.
An insight-driven organization focuses on this knowledge, rather than the data itself. If along with the CRM and loyalty data, you have these astute observations, it can power better marketing and experience strategies. Storing every click of every customer is not feasible and honestly, not required. The signals you get by analyzing these clicks, cookies, customer location, and device information during the digital engagement should be part of your CDP strategy.
External data sets to enrich customer attributes
Finally, bring in 2nd and 3rd party web, demographic, psychographic, location, buying intent data to further enrich what you know about your customers. This is the ‘test-use-evaluate and iterates’ part of your data strategy. Not all data providers are the same, they all start their data journey differently and may or may not work for your brand and your customer base. It’s also important to note that data degrades quickly – the data was good a few quarters ago might not be valid anymore. Customer interests and intents may have evolved – they may have changed jobs or had a major life-event. To find out what works for you, do small experiments with a good set of data partners and choose the one that works best for your current marketing and strategy needs.
If you are poorly utilizing the data you have captured, or not capturing certain essential data, you are losing important revenue opportunities. If data is the new oil, accurate customer insights are high octane racing fuel for your B2C business!
March 1, 2019
Who Controls the Marketing Stack in 2019? The CIO or the CMO
By Ariella Brown, Zylotech
The position of CIO is synonymous with taking charge of the technology in a business. But recent shifts in marketing demands means CMOs may actually be spending more on technology than CIOs. Who’s in charge, and how should organizations set up the defined lines of responsibilities for selecting, implementing and managing the use of these technologies? The key is building a partnership between marketing and IT.
How the CMO leads
CMOs’ IT spending is said to already outpace that of CIOs, according to a Gartner report. The reason for that is much marketing efforts have become a lot more data-driven, which translates into increasing need for IT services. However, not all those needs are going to be met by an organization’s IT department. The report also noted a movement toward externally sourced away marketing tech, often through SaaS.
In fact, according to the figures in Infotechlead, CMOs in the US alone are expected to invest more than $122 billion in martech and associated services by the year 2022, up from the estimated $90 billion spent in 2017. The marketing automation market is estimated to contribute $26 billion to that spending within the next three years.
CMOs already determine which martech to buy. Business.com offered some figures that show the current impact of tech driven by marketing needs:
- Marketing bears responsibility for selecting and managing marketing service providers at 83 percent of enterprises.
- Marketing chooses technology providers at 71 percent of companies.
- Marketing has control of the budget for consulting and design of marketing-related software at 75 percent of companies and for purchasing software-as-a-service at 47 percent of enterprises.
- At 43 percent of companies, marketing also controls the budget for external hosting of technology required to run any marketing-related software.
Generally, having the CMO direct the marketing tech works out quite well. According to the survey cited in Infotechlead among organizations that allow the CMO to set the pace for digital transformation, 62 percent report “double-digit growth, compared with 50 percent of respondents whose firm’s digital transformation is run by the CIO.”
The CIO’s role today
Allowing the CMO to lead in this way doesn’t mean that the CIO becomes less important, though. On the contrary, a Forbes report entitled “The Ascent of CIO” found that over 80 percent of CIOs consider their role to have become more important now. But they also realize that their role has evolved. No longer is their value defined strictly by their “technology know-how,” but by what they bring to the table with respect to strategic planning.
While CIOs still rank leadership as a primary attribute of the role, “partnering with others” is also up there.
One of the areas in which they have to partner is marketing. Current trends indicate that the successful companies are ones that will have the CMO leading the search for martech. They deliver the best experience for the firm’s customers and work with the CIO to find the most efficient and effective solution. It’s not a matter of the CMO making a unilateral purchasing decision, but of communicating what the marketing team needs to work with the CIO on making it happen.
Better together
The smart money appears to be on a collaboration between the CMO and CIO that brings them together through a shared interest in data and analytics while also capitalizing on the distinct strengths of each role. That means that while the CIO still works on the integration and implementation of the technological solution, the CMO would be the one who sets the goal for the martech. Working together, they can break through silos to effectively integrate data to extract the maximum from it and derive more accurate insight to inform marketing strategy. That will be the direction for successful companies in 2019.
February 27, 2019
CDP: The Data Foundation for FinServ Marketers
By Pedro Rego, Lemnisk
Effective marketing is all about understanding your customers: their needs, their behaviors, their relationship to your organization. Customer Data Platforms (CDPs) simplify the process and expand the potential of organizations and their marketers using actionable information to build and a better sense of who is your customer, what they want, and improve performance metrics such as cost per action (CPA), customer lifetime value (CLTV), and ultimately drive brand loyalty.
What is a Customer Data Platform (CDP)?
As defined by the CDP Institute, founded by David Raab, a CDP “is packaged software that creates a persistent, unified customer database that is accessible to other systems.”
Raab emphasizes three key components of that definition: a CDP must be understood as a pre-designed piece of software that can be tailored to the needs of an individual customer (packaged software); is designed to collect and archive customer information regarding things such as purchasing history or site behavior, collected across multiple channels and attributed to one customer (persistent unified customer database); and is able to sync with other tools and systems that can analyze the collected data to the benefit of marketers (accessible to other systems).
These three essential features define what makes CDPs so popular among companies looking to optimize their marketing: flexibility, the ability to curate massive amounts of customer information, and their compatibility with a number of other powerful marketing technologies.
What Can a CDP Do For My FinServ Institution?
CDPs are the best way to build communities of customers, and the best way for institutions to connect with those customers on a personal basis.
For FinServ organizations, it’s often essential to constantly track and manage customer loyalty, to keep an eye on what your customers need and how they’re engaging with your business. Patrons are creating relationships with banks, insurance companies, and financial services mainstays across multiple platforms, and a CDP guarantees that these organizations are able to check in with their customers on the platforms they prefer - whether that be email, telephone, or mobile device app - providing the offers that will pique customer interest the most.
With the data collected via a CDP, FinServ marketers are able to build marketing that is both hyper-personalized and omnichannel, connecting with customers on an individual level across every single device that they use to interact with your brand.
Why Does That Matter?
Financial Brand published a fantastic analysis of the June 2018 study of how banking marketing is evolving, published jointly by Adobe and eConsultancy. Surveying marketers and executives working at major banks, the study sought to understand where these individuals felt their industry needed to focus in the wake of the rise in popularity of FinTech. The results were instructive.
The survey shows that 28% of those spoken to consider optimizing the customer experience to be the highest priority moving forward, which is especially interesting when compared to the fact that, as the report contends, only 19% of marketers working most other industries would consider that the highest priority. The study further asserts that its results show that 23% of respondents listed as their highest priority “data-driven marketing that focuses on the individual.” A later excerpt shows that when asked what they believed would be “very important” for digital Customer Experience (CX) over the next few years, 81% of banking providers the majority selected options such as “optimize the customer journey across multiple touchpoints,” or “ensuring consistency of message across channels.”
Personalized marketing, consistent across channels: the perfect task for a CDP.
FinServ Marketers Must Adapt to the Demands of Their Clients
As customers become comfortable interacting with businesses via multiple devices, and as they’re trained to expect content that specifically responds to their individual wants and needs, marketing teams must build strategies that take these realities into account. The alternative is being left behind.
FinServ Customer Data Platforms, like Lemnisk, offer marketing teams a versatile tool to address these very demands: to better understand their customers across various touchpoints, and to design marketing collateral and sales campaigns that are molded to these individualized preferences and behaviors.
As FinServ organizations look to reinforce customer loyalty and attract new clients, they must find the tools that not only suit their unique needs, but those of the people they are targeting. The careful, strategic use of customer data platforms have revolutionized how organizations in myriad industries evolve their marketing.
For FinServ marketers, it’s a sterling investment.
Do You Want to Create a Unified Customer Experience for Your Prospects, Leads, and Clients?
Learn more about how Lemnisk's CDP Empowers Smarter Marketing for FinServ Institutions or visit their profile page.
February 25, 2019
The Identity Puzzle in Customer Data Management
By Sameer Narula, Manthan
 In Hindu mythology, Ravana, the great scholar and demon king, has ten heads, symbolizing his various powers and knowledge. The heads were indestructible with the ability to morph and regrow. In their battle, Rama, the warrior god, thus must go below Ravana’s heads and aim the arrow at his solitary heart to slay him for good.
In modern times, the consumer is a bit like Ravana, not in terms of his evil designs but his multiple identities. Research states that an average consumer in US today is connected to 3.64 devices. With proliferation of a host of new age devices like smart speakers, wearables, connected homes and automobiles etc., it is projected that she could be connected to as many as 20 devices in not so distant future. Like it did for Rama, this poses a clear challenge for today’s marketer – how to navigate through the maze of these devices to identify and recognize THE consumer so she can be singularly, consistently and contextually engaged across her addressable touchpoints.
Industry research suggests that only a small fraction of consumer businesses can currently accurately identify their audience – hence the advent and rapid rise of Identity management solutions that help businesses resolve the identity of their audience into individual consumer identities and profiles. The size of the Identity solutions market is estimated to grow from $900 Mn currently to over $2.6 Bn by 2022, outpacing overall marketing investments growth.
A recent Winterberry research survey indicates that about 50% of consumer businesses have intensified focus and plan to increase investments on Identity solutions. While segmentation and targeting on paid media remain the predominant use cases for consumer brands, cross-device and channel personalization plus measurement and attribution are expected to become areas of focus in the near future.
Identity Solutions: The past, present and future
At its core, an identity resolution solution’s job is to continuously gather audience activity data from a disparate set of data sources, platforms and services to derive a cohesive, omni-channel identity and profile of each individual audience member. However, the approach has been largely siloed so far with marketing channel specific identity platforms and strategies. CRM databases as custodians of first party customer and contact information, have been the mainstream identity platforms for direct marketing activations, primarily over email or direct mail.
With the growth of digital marketing spend, Data Management Platforms (DMPs) that store digital audience behavior data to primarily support display ad buying use cases have come into prominence. However, their relevance is now questionable with walled gardens like Facebook and Google closing doors on them. The other growing channel of influence has been mobile data platforms to support mobile device and location-based engagement.
To overcome the limitations of a disconnected, multi-channel approach that current Identity solutions like CRM databases or DMPs are constrained with, the focus is shifting to emerging modern solutions like Customer Data Platforms (CDPs) and Identity Graphs. These offer a unified, cross-touchpoint and omni-channel approach towards identity resolution and linking, enabling a fully harmonized, single view of the customer to the marketer.
The Mechanics of Identity Resolution
Identity resolution system’s key job is to continuously collect audience related data from a variety of sources and put it through an ongoing process that resolves, generates and updates this data into discrete consumer profiles, which are then used by the business for various forms of marketing or other activations.
The process comprises of 3 key steps:
- Data Management – Includes ingestion of disparate set of consumer data, both identity and activity related, followed by processing and storage of this data into organized repositories.
- Identity Resolution – This is a crucial and complex mix of a deterministic and probabilistic process of deriving identifiers, matching, cross-referencing and linking to unique consumer identities followed by a validation mechanism to maximize the accuracy of the resolution process.
- Consumer profile generation – This associates all identifiers, attributes and activities into a harmonized, holistic Identity Graph of the consumer, an individual or a household.
What makes an Effective Identity Management Solution: 5 Mantras
- Ensure the Identity system is fed with data from a wide array of data sources. Not just device activity but also the applications behind to help drill past the device, cookie or pixel and reveal the real people behind them and their behavior.
- As part of data management, ensure meeting consumer privacy rights and compliance requirements of industry norms like GDPR, CCPA etc.
- Identity resolution should include a consistent, rule based deterministic match process to ensure high accuracy critical to support contextual, personalized engagement in direct marketing use cases.
- The deterministic process must be supplemented with machine learning driven probabilistic matching to expand the data set and meet the requirement of use cases like social media or display ad marketing that expect a wider net but relatively less 1:1 personalization.
- The generated consumer profile, in form of an Identity graph, while having the requisite accuracy and timeliness, should go beyond the linkages to identifiers and attributes by including the desired insights to optimally enable marketing activation use cases.
February 22, 2019
What's Driving the Booming Demand for Customer Analytics
By Chuck Leddy, Zylotech
Forbes recently highlighted the top five B2B marketing trends for 2019. First among these trends is the burgeoning demand for real-time and enriched customer analytics: offering key insights that enable B2B marketers to deliver experiences meeting the individual needs and wants of their customers.
Let’s explore what’s behind this trending demand for customer analytics, and how analytics platforms will help shape B2B marketing and the customer experience in 2019.
Marketers are frustrated with their customer data
Many B2B marketers are profoundly frustrated with the state of their customer data. For some, it’s segregated into siloes, and this segregation inhibits effective multi-channel communications. For others, their data is available but it’s unstructured and un-actionable. And, there are cases of data and key insights being accessible to few (perhaps senior leadership) but not the near-the-customer people/marketers who need it to deliver better, real-time customer experiences. The bottom line is that marketers can't access their data in an efficient way to meet the expectations of their customers.
With such fragmented and disconnected customer data aggregation and analysis, the customer insights that do get delivered to marketers are - it comes as no surprise - fragmented and disconnected. Marketers are skeptical about trusting those insights, and their lack of trust is fully justified.
What these B2B marketers want instead of fragmented, un-actionable data is customer analytics via accessible and actionable insights that allow them to better understand and engage their customers (for example by anticipating cross-selling and up-selling opportunities). With customer analytics, marketers can target the right customer with the right message at the right time. That’s ROI-driving relevancy.
B2B marketers can’t separate the signal from the noise because they’re drowning in raw, unstructured data. The noise is everywhere, but they’re listening for that one special signal, that one piece of relevant customer data that can help inform decision-making and drive a better CX.
Why customer analytic platforms are “in” for 2019
The emerging solution to these data management woes are customer analytics platforms that provide B2B marketers with crucial insights about customer behaviors throughout the CX, while connecting customer data across various systems and channels. You have full access to the signals you want, and the noise gets eliminated.
A great customer analytics platform needs to do two things: first, aggregate your customer data and second, render it actionable.
One layer of an analytics platform might pull all your organization’s collected data into one platform. It would then standardize your customer data, remove duplicates, and enhance it with missing information. B2B marketers would thus get one crystal clear view (a single source of truth across the organization) of the customer that’s as close to a real-time representation as possible.
Then there’d be another layer that analyzes customer data, in near real-time, to provide deep, actionable insights that support smarter marketing to drive ROI. That’s the leveraging part. By uncovering valuable trends and patterns in your customer data, you can map the customer’s journey and reach out to them with the most timely, relevant, and personalized messaging to enhance customer engagement. You can even do so in automated, AI-enabled ways that serve as an “always-on” customer nurturing engine.
Customer analytics will drive B2B marketing success in 2019
Caroline Robertson of Forrester Research is quoted at length in the Forbes article. Ms. Robertson puts things simply: “Most B2B marketers have low confidence in the quality of their data and their data management skills. To reconcile their numerous data silos – and enable the customer-driven engagement the modern B2B buyer demands across the entire lifecycle – many B2B marketers” are turning to customer analytics platforms for answers.
As explained above, these platforms offer massive advantages over fragmented, unfiltered (raw) data, most notably time-to-value. You don’t waste your time drowning in unfiltered data, and searching for relevant customer data. You get the signals that customers are sending you, and you can then leverage them in a timely, relevant manner, even through automation. Make 2019 the year you find better ways to access and leverage the relevant data you need to inform better B2B marketing.
February 20, 2019
The Great Competitive Paradox: Telcos’ Defining Moment
By Alex Holmes, IntentHQ
The biggest problem that telecom operators face is keeping up with the demands of the connected customer.*
Just 4% of executives felt their customers were happy with their current customer experience. The vast majority (96%) of Chief Marketing Officers from telcos said that non-telcos do a better job on customer experience.
The bar isn’t being set by traditional competitors, but by the renowned FANG (Facebook, Amazon, Netflix and Google) companies that have stolen wallet share and commoditised telcos’ offerings. FANG companies are winning a bigger wallet share because they understand their customers and they use that knowledge to predict their behaviour and treat each customer differently. Personally.
In perhaps the ultimate snub to telcos, most of that wallet share gained by FANG companies is being transacted via telco customers’ mobile devices on their network! Telco CMOs know they need to do something, but it seems like a huge mountain to climb and they are struggling to get going.
But there’s a paradox. Amazon and others have learned to understand their customers and predict their behaviour. But telcos have just as much of the raw ingredients to do the same thing. Amazon knows every customer’s browsing and purchase history, searches and ratings. However, telcos are in a position to create an equally - if not richer - view of every single customer. So what’s stopping them? What is so difficult?
FANG businesses have invested millions creating a machine to mine the data, look for patterns and find meaning. For the most part, telcos have invested incredible amounts in creating and analysing data about their network - to improve network quality. The customer understanding and personalisation machine that the telcos have is thin in comparison. FANG companies are using customer insights to create stronger affinity between their brand and their customers; telcos feel nowhere close. Despite having rich data in the business, the majority of it remains unused - it’s difficult to access and virtually impossible to work with using conventional ‘big data’ tools.
Creating a deep understanding of customers to coordinate activities in a unified way across channels still feels like a hopeless aspiration. Their customers often feel as though they are being treated as a commodity: faceless, spammed, irritated and not cared for. CMOs know that they are missing out but fixing it is like trying to turn around an oil tanker. We call this The Great Competitive Paradox because the telcos are losing this battle despite have plenty of powerful ammunition.
It doesn’t have to be like that.
Imagine you could understand your customers more deeply and more personally than Amazon does. What if the digital picture you had of your customers wasn’t just data points? What if it was a solid human picture of their interests and desires; a picture of where they’ve come from, why, and what they will do next? More than that, though, you need to make sense of the network data to paint a rich picture of the world your customers live in. It’s no good just knowing your customer is young and female. If you know she’s a single mother of a young child, she works on shifts and has strong affinities to specific brands, you can really engage her with relevant messages and create a positive experience.
The only way to win back wallet share from Netflix and Amazon lovers is to have a better understanding of your customers than they can. Because you’ve got millions of customers and billions of events, you need to do that at scale.
You also need the same rich human story of your customer in every channel at once. That means moving each unique customer story around to everyone that needs it at incredible speed. Achieving that would be a challenging, but amazing journey. If you could, you would be the ones raising customers’ expectations. And that would make you the stickiest brand in a very slippery world.
See how O2 have started to challenge the Great Competitive paradox here.
REFERENCE:
February 18, 2019
Build or Buy a Customer Data Platform? Here’s the Answer
By Varij Saurabh, Manthan
A packaged, industry focused CDP provides ‘people ready data’ and quick time to value
Any marketer looking to invest in a Customer Data Platform (CDP) will have to grapple with the build vs. buy conundrum. The points of view are more entrenched in this space than any other. This is primarily because the end deliverable is a customized data platform, which resembles a services engagement. Let’s compare the build and buy options.
If you know exactly what you need, can describe it in exact requirements and have a service partner or an in-house team who can deliver to the requirements, build might be a path to consider. You will still have to wait for 6 to 12 months to get access to the solution. If you have a stop gap technology that meets your requirements while you build, or time to market is not critical consideration, you can opt for the build route.
The build option
Organizations with substantial IT budgets and large development teams with complete range of data, design and IT skills might opt for building a CDP in-house. The people investments required to deliver a custom CDP and maintain it are considerably higher than the buy option.
Done right, there could be competitive advantages with a custom solution. However, be cognizant that at every stage there are very real challenges – scope creeps, failure to accurately define specs, cost overruns, overheads of vendor management and staff attrition.
Basically, a large-scale IT project is often lost in translation.
The buy option
A pre-packaged CDP that is crafted for a specific industry offers the best of both worlds – quick time to value of a plug and play solution and close fit of a bespoke solution. While you would need some technical resources for the set-up and upkeep, the time and costs involved are much lower, and there are no harsh surprises.
This means you can run pilots and POCs quickly before you go all-in, you can examine and are sure that the solution really works for you. A much lower upfront cost and quick onboarding takes away risks, and still provides a solution that is tailormade for your needs.
CDP that delivers people ready data
A well-designed solution must cover scenarios and requirements that haven’t been thought of and encountered before while being cost-effective.
A CDP should serve multiple user groups that have unique needs. It should be scalable to serve data scientists who work with large data sets and require clean, well-organized data. It should be flexible to serve marketers and campaign planners who want to promptly analyze metrics and decide targeting strategies or perform customer segmentation. Senior executives form another user group and would typically be interested in trend reports and business dashboards.
Recognizing the distinct personas in the value chain, a differentiated CDP must have distinct interfaces and functionality for each. It’s time you addressed the unique data needs of different people.
February 14, 2019
Gearing Up for B2B Marketing in 2019
By Ariella Brown, Zylotech
CIOs at successful B2B companies are planning for a 2019 marketing stack roadmap with a focus on #CustomerTech. That means not just collecting data, but using it to address the business needs of their customers to achieve an unprecedented lift in KPIs defined around understanding customer behavior.
Next year is set to be one for businesses to fully optimize their customer data quality and insights by setting up the right technology platform for marketing to business customers. As you plan your 2019 budgets, the essential thing to think about is the customer aspect of #CustomerTech. Approaching tech from that perspective results in better customer data and analytics -- the prerequisites for account based marketing (ABM).
Thanks to the rise of digital transformation, marketing models have evolved. The same demand for personalization that has arisen in the B2C realm now applies to the world of B2B marketing. As Gartner put it in "2019 CIO Agenda: Secure the Foundation for Digital Business" [PDF], ”organizations are shifting their focus from what they sell to how they sell.” In fact, Gartner found that 49 percent of CIOs surveyed said they had already transformed their approach or were in the process of doing so.
The lifeblood of digital transformation is data, and that is why we’re seeing B2B marketing evolve now. We’ve arrived at the point of capability to tap into the ubiquitous streams of data to compile B2B marketing data that is rich and robust enough to trigger advanced customer analytics.
Accordingly, Dunn & Bradstreet’s 6th Annual B2B Marketing Data Report found that concern about data quality has grown over the past three years from 75 percent in 2016 to 89 percent this year. However, the numbers are lower for those who can say that their own data meets their expectations; only 50 percent express confidence in it.
And there’s the rub. No matter how much data there is out there or how sophisticated your analytics may be, ABM won’t work if the data is wrong. This could account for why the majority of businesses have still not adopted it.
Only 38 percent of people said they were using it, according to Dunn & Bradstreet. The report attributes the low rate of adoption to the lack of “quality data – specifically strong firmographic and demographic data – to identify key accounts and targets, reach them across a variety of channels, and deliver relevant content that accelerates their buyers journey.”
This is why CIOs have to direct their tech efforts to consolidating quality customer data for analytics that will enable B2B ABM. The key to that is defining metrics around your business customers to determine the ROI of the tech.
As Gartner reports, the overwhelming majority of top performers (89 percent) “favor consumer metrics as indicators of success.” Such companies pay less attention to the time they save from the use of tech and instead focus on results the time savings can deliver to their customers, as well as that most important goal of attaining a “deeper understanding of consumer behaviors and needs.”
Achieving such understanding calls for high quality data and accurate customer analytics. Those form the basis of ABM, which needs to be in sync with the customer experience throughout the buyer’s journey.
This is the equation for CIOs seeking to fuel their business initiative this year: set up the #CustomerTech that will get you the right data for accurate analytics that will enable your marketing teams to tap the great potential of ABM. This will in turn allow your marketing teams to tailor their messages to the needs and preferences of the targeted business contact. If they do that then they will see measurable ROI for tailored B2B marketing in 2019.
February 11, 2019
Smart Audience Segmentation and Suppression With a CDP: A Primer on Segmentation and Suppression
By Korina Velasco, Lytics
67 percent of customers say brands need to automatically adjust their content based on a customer’s current context. And 42 percent say they get annoyed when content isn’t personalized.
Which is why more and more companies are turning to the data to personalize their marketing, to target the right customers with the right messages at the right time and to make sure they aren’t targeting the wrong customers.
In the world of Customer Data Platforms, we call this segmentation and suppression. And here’s how it works.
What is Segmentation and Why Does It Matter?
Chances are, your company has a lot of customer data on its hands. The challenge for most companies is in filtering and using that data to drive real campaigns. Which is where segmentation comes in.
Segmentation is the act of filtering the data to identify a target audience. Maybe that target audience is people who have abandoned their shopping carts (and who you hope to convince to come back and complete a purchase). Maybe it’s your most engaged customers. Maybe it’s customers within a 30-mile radius of an event. Or maybe it’s a combination - shopping cart abandoners who are highly engaged and within a 30-mile radius of your event.
One of the powerful features of a good CDP should be the ability to segment your data and let you identify your target audiences quickly, simply and in real time.
And then there’s suppression…
What is Suppression and Why Does It Matter?
On one hand, you want to target the right audiences for your campaigns. But what about the wrong audiences?
If you chase your customers around with ads for things they’ve already purchased, they’re going to get annoyed. And 66 percent of those customers who you annoy with irrelevant ads? They say that annoyance makes them far less likely to purchase.
Which is where suppression comes in.
A good CDP should allow you to not only target your ideal customers, but also suppress audiences you don’t want to include in a campaign. Don’t want to advertise to customers who just bought a similar product from you? What about customers with open customer service tickets? Or customers who’ve stopped engaging with your brand? You should be able to identify and suppress those audiences in your CDP as needed for each campaign.
Smart Segmentation and Suppression Leads to Big Wins
Smart segmentation and suppression are how The Economist grew digital subscriptions by 300 percent.
Through segmentation and suppression, they identified and targeted users who were not yet subscribed and who were likely to subscribe based on Lytics behavioral scores. Not only did they increase digital subscriptions exponentially, but they decreased acquisition costs by 80 percent as well.
Dr. Martens’ story is similar. They used segmentation and suppression to identify customers who had an affinity for their museum collection, who lived in the UK, who were likely to engage and who had not recently purchased a satchel. They targeted this audience with ads for a museum collection satchel…and saw a 60 percent increase in conversions and a 20 percent increase in average order value.
Ready to Start Segmenting Your Customers?
A CDP like Lytics is the answer. We’d love to show you how. Schedule a demo today to see the product and chat with our expert team.
February 4, 2019
Is Your Organization Ready for a Customer Data Platform?
By Korina Velasco, Lytics
Data-driven marketing is proven to increase engagement and sales. But before you commit to a CDP, it’s important to assess your organizational readiness.
Data-driven marketing is vital in today’s economy. Or so say 64 percent of marketing executives.
So, it may come as no surprise to learn that Customer Data Platforms and data-driven marketing strategies are growing - and fast.
And while speed is important in an always-changing industry with customer expectations that keep on rising, it’s also vital that we put strategy before tech. Because it isn’t just new technology that makes a company data-driven. It’s also about the people, processes and strategy behind the scenes.
In fact, in a recent panel discussion, some of tech’s biggest names agreed that the biggest challenge to MarTech success is people and process.
So, before you tackle the technology side of becoming data-driven, we think it’s important to assess your organizational readiness. Is your business ready for and committed to a CDP? Here are three questions to help you figure it out.
1. Have You Identified Your Goals?
Every tech platform on the market, including CDPs, is a tool meant to help you achieve real business goals. This means you need to know what those goals are and make a plan for reaching them before you choose and implement a CDP.
If your goal is to identify the content each user responds best to, you’ll need a CDP with a content affinity feature. If personalization at scale, like that demonstrated by Netflix or Spotify, is your ultimate goal, you’ll want to look at CDPs with built-in machine-learning and artificial intelligence (like Lytics). If your goal is simply to centralize and unify your data, you may be able to get away with a less feature-rich option.
Whatever your goals, both short- and long-term, identify them up front and use them to assess the technology. Because it’s much easier to buy the right tech the first time than it is to replace it later.
2. Is Your Data Accessible?
For a CDP to really drive business results, it needs to have data coming in. Before you start looking at platforms, take a look at your existing data. Where does your data currently live? In how many places? Is it accessible to you? Is it organized? Are there process or tech restrictions that keep you from accessing it? How will you get around them?
The more you know about your current data situation, the better prepared you’ll be for both choosing a CDP and understanding what you need to do to get the data into it once you have it.
Read more about making the most of your first-party data.
3. Are Your Teams Ready and Willing to Work Together?
If siloed data is bad, siloed teams are even worse. If you’re committed to data-driven marketing, that means your teams are going to have to work together. Are the IT, marketing, sales, PR, customer service and other customer-facing teams already connected? If not, are you willing to make changes to connect them?
According to the aforementioned industry leader panel, the biggest point of failure in this process isn’t technology. It’s communication. For the best results, every team that touches the customer service experience will need to be aligned. The earlier you start thinking about how to align those teams, the better (and faster) you’ll start seeing results.
What Now?
Understanding the people and process challenges and changes ahead will help you choose the right CDP and hit the ground running. We also recommend choosing a vendor that understands that the tech isn’t the only important piece of the data-driven puzzle. Look for vendors with robust services, workshops, learning resources for your team and great customer service reputations.
Want more help assessing your organizational readiness and choosing a CDP? We just re-launched our CDP Buyer’s Guide. We’d also be happy to give you a demo and chat about how Lytics can help you reach your business goals.
January 28, 2019
Everybody’s Talking About Customer Data Platforms
By Matt Cannell, Lytics
With customers demanding greater personalization and privacy regulations demanding the companies control their data, marketers are talking about CDPs more than ever before.
According to CMS wire, CDPs will be worth $1 billion by 2019.
If you’ve been paying attention, this probably comes as no surprise. After all, the customer demand for personalized content is at all all-time high. Data-driven companies are exponentially more likely to be profitable year-over-year. And in 2017, 83 percent of companies who exceeded their revenue goals were using personalization.
If your company doesn’t already have a Customer Data Platform, chances are you’re on the hunt.
And you’re not alone.
According to Forbes, about 78 percent of Enterprise companies have or are planning to implement a Customer Data Platform.
Which is probably why so many guides are emerging to help companies through the process of selecting the right CDP.
CDP buying guides recently published include:
The number of guides, published one right after the other, just goes to show how serious marketers are getting about their customer data. With privacy regulations, customer demand for personalization, and stats that consistently show that personalized marketing leads to big business wins, the search for a data platform is becoming more and more of a priority for businesses as the year progresses.
If you’re on the hunt for a CDP to centralize, stitch together, and help you analyze and use your first-party customer data, we’d love to schedule a demo and show you what Lytics can do.
There’s a reason we took top marks in The Relevancy Ring Buyer’s Guide above. Let us show you.
January 24, 2019
Apply Business Use Cases to Evaluate CDPs
By Tony Byrne, Real Story Group
When comparing or evaluating Customer Data Platforms, people tend to default to feature comparisons. This seems logical, but can risk missing the business forest for the technical trees.
For Real Story Group vendor evaluations, we do examine features, but focus more on business use cases, or "scenarios," to find more meaningful contrasts for prospective enterprise technology customers. While you should always consider product functionality and vendor predilections, the key to comparing technologies lies in how well they fit your particular scenarios.
Nine CDP Scenarios
Explicitly or not, different CDP vendors target different scenarios. This is usually because the product's flagship customers wanted to fulfill a specific subset of use cases. The platform might have broadened its scope as it matured, but typically the initial roots remain visible — and powerful.
Nine CDP Scenarios. Source: Real Story Group
In our recently-debuted CDP vendor evaluations (get free sample evaluation here), RSG isolated nine distinct business scenarios against which we assess platform fit. Some will be more or less germane to you. In some cases CDPs support a business capability, rather than deliver the service itself.
Vendors will of course claim broad applicability for their platforms, but we find in reality they typically specialize in three or four of the nine. Let's look at each in turn.
1. Advanced Customer Data Management
This scenario might seem like an oxymoron because the primary purpose of a CDP is to provide data management services. But some vendors focus intently on this set of back-end capabilities, and less on the more activation-oriented services below. It's a kind of purist approach, but your enterprise might want to focus here if you anticipate having to support multiple data sets, create virtualized data store, expect unusual challenges reconciling identities, anticipate using the data for non-marketing use cases, or have other advanced data munging needs.
2. Outbound Marketing Campaign Support
Support for outbound marketing campaigns is almost foundational and therefore, most CDPs enable this case in some form. However, the extent of that support varies. Some tools enable varying degrees of integration with outbound marketing and campaign platforms, most notably delivering segmented lists for activation. Other CDP vendors provide some inbuilt campaign capabilities to activate that data themselves, while others have built multi-step, journey orchestration features. Your mileage will vary.
3. Predictive Analytics
Predictive Analytics helps you answer hypothetical questions by predicting some aspect of an overall customer journey. CDP tools that offer this service will typically apply machine learning and artificial intelligence-based techniques. With time, and more data, the system is supposed to keep learning and improve its recommendations.
Of course, this also adds complexity to these platforms. Several CDPs can get you up and running within days, but most products that support predictive analytics will need you to do your homework well. Also, you may already possess more advanced predictive analysis services in other platforms and repositories (e.g., data lakes) within your organization that could prove more mature and adept at this.
4. Online Personalization & Experience Optimization
CDPs can potentially address two key dimensions of personalization:
- Understand Individuals: Gain a better understanding of customers, apply them to segments, and then...
- Personalize Experiences: Adapt content, options, and digital experiences based on that understanding
Most CDPs stop at the first dimension. They will integrate with downstream marketing / experience management / targeting systems for actual experience personalization — principally by pushing segments or individual records forward. Many large enterprises prefer this approach as it neatly separates data and experience concerns.
Some CDPs also offer built-in personalization engines. Also sometimes known as "recommender" systems, these platforms try to recommend content based on different attributes.
5. Ecommerce Recommendations & Optimization
Some CDPs offer specific services to target ecommerce scenarios. These include support for features to handle shopping cart abandonment, repeat purchases, next best offers, promotional campaigns, and so forth, leveraging diverse product attributes. These platforms typically feature tighter integration with common ecommerce environments, as well as heavier reliance on machine learning-based techniques for product recommendations. Here again let's distinguish between providing the data for your ecommerce engine to act on and actually providing the recommendation service itself.
6. Omnichannel & Offline Aggregation
While most CDPs can handle data and campaigns for digital marketing, not all of them are as good for integrating the following types of data:
- Offline customer data from stores and point of sale (POS) terminals
- Data from mobile apps
- Data from other internet-connected devices such as Apple TV, gaming consoles, smartwear, and so on
Platforms that excel at this scenario offer at least some of these capabilities, along with services like managing physical address formats, and SDKs for different computing environments like IoT devices.
7. Realtime behavioral analysis
Most CDP vendors will claim they have machine learning capabilities. But very few can apply machine learning to streaming data in real time. CDPs that excel in this scenario offer more advanced algorithms that can work against existing data as well as live streaming data. Mind some significant architectural issues here around performance, reliability, and security.
8. B2B Marketing Support
In this environment, a company becomes a first-class object and not just a facet of an individual's record. CDPs that support this scenario can map customers to an account or organization object and vice-versa. Then ideally other CDP services — like data integration, segmentation, and analytics — can get carried out at an account or organization level as well.
9. Digital Advertising Support
Adtech and martech are slowly converging. Most CDPs can integrate with DMPs and other tools to get second-party and third-party data which they can use to enrich their first-party data. A few platforms also provide specific capabilities for advertising scenarios by integrating with ad servers and other adtech platforms.
Putting Use Cases to Use
You can apply scenarios to find out what a CDP really does well...and not. Consider the case of IBM's Universal Business Exchange (UBX). Many observers would argue it's not really a CDP, though Big Blue will try to sell it to you as one. A deeper, scenario-based evaluation suggests that UBX is reasonably broad but not deep, and, importantly, relies on numerous other IBM platforms to achieve business value.
Evaluating IBM's UBX as a CDP. Source: Real Story Group
Summing Up
To be sure, these scenarios are abstractions. In practice, your own efforts here are likely to represent variants or a hybrid combination of scenarios. The cases overlap somewhat, but they are useful for understanding what types of platforms tend to work better for different types of projects.
So a savvy customer will prioritize their needs and select technology accordingly from among the dozens of choices in front of you. Feel free to ping me with any questions or leave a comment below.
January 21, 2019
Wolf Tales: Observations from the Road
By Ed Wolf, Zylotech
Luxury: the very word evokes images of elegance and extravagance, and an elite and posh lifestyle that all should aspire to. Well known designer brands such as Coach, Gucci, Rolex, Louis Vuitton, and others understand that the lure of the luxury lifestyle is at the heart of their appeal among their clientele. Those customers, in turn, expect white glove service and royal treatment with every interaction. They spend a lot of money on their brands and have very high standards for customer service and personalized communication. In today’s big data, multi-channel world, however, this lofty ideal of high-end red-carpet treatment is more wishful thinking than reality. The truth is that even the most expensive and exclusive retail brands struggle with their customer data, which is the foundation for the personalized treatment that these customers expect and demand.
Luxury brands often have difficulty using data for personalization. These traditional businesses, significantly rooted in an historic past, are now competing with savvier labels. Born in the analytical age, they inherently understand how to leverage such modern technologies as real time data, machine learning, and artificial intelligence.
We met a mix of these luxury brands at the Luxury Interactive event in New York in October. Much of the discussion there centered around the importance for marketing executives in luxury to think analytically and luxury-minded at the same time. Luxury is just now getting a feel for one-to-one marketing, which offers a significant mining potential to capture new luxury consumers, as well as to cross-sell and up-sell existing ones, all while maintaining their exclusive reputations.
The main issue preventing these brands from reaching this holy grail of white glove personalized attention revolves around the use of customer data for insight into shopping behavior, product affinity, and brand loyalty. The problem is not lack of customer data. On the contrary, like most retailers, luxury brands have been collecting voluminous amounts of customer data for years. Whether it’s through ecommerce transactions, brick and mortar stores, department store sales, catalogue orders, or even social media sites, these choice companies have vast amounts of customer data with which to mine unique insight into customer behavior.
Instead, the key is understanding the full picture of everything the customer has ever done regarding the brand. This becomes challenging because the various sources of data often don’t speak. The ecommerce data is usually in a different silo than the email marketing data, for instance. And both are separate from, say, the data showing customer service calls, or POS data from instore purchases. The bottom line is that without these disparate sources becoming unified, luxury brands will find it nearly impossible to understand the behavior of their clientele. Sure, they may know that a customer purchased a pair of shoes and a handbag, but will they also know that the customer had to call the customer service line because of a repair issue on the bag? Or that the same customer follows the brand on Pinterest? Do they have a sense of the customer’s external demographics?
Without this data unification, and the data cleansing that accompanies it, brands will be in the dark when it comes to a thorough understanding of customer behavior. This makes segmentation and messaging that much more inaccurate. This comes back to haunt the brand when they send a marketing campaign that purports to be personalized but misses the mark because of outdated or incomplete data. This results in luxury customers not feeling the individualized love and attention that they crave, and that the brands promise to deliver.
The good news is that, as evidenced by the conversations at Luxury Interactive, these exclusive brands recognize this shortfall and are trying to address the problem. The emergence of Customer Data Platforms (CDPs) is a potential light at the end of the tunnel. These platforms seamlessly integrate and unify ALL customer data (some like Zylotech even enrich this with third party data), making it much easier for brands to communicate to their high-end customers. This is crucial for all brands of course, but even more so for luxury retailers whose reputation means everything to them, and any misstep in marketing messaging or communication can be damaging to their brand image.
To conclude, even though in many ways luxury brands evoke an old-fashioned sort of elegance, they need more than ever to embrace the forward thinking methods of customer engagement. Today’s brand conscious luxury shopper demands it.
January 17, 2019
Enhancing Your Customers’ Shopping Experience
By Hadas Tamir, Optimove
Every holiday season causes the increased need to better identify and treat your shoppers. How should you win their attention? Entice them. Here are some ideas.
Lately, I have been participating in a lot of conversations around how customers should be targeted and with what offers. The conversations focus on which offers are most effective on specific customer segments and how marketers can save money by identifying customers who only return during the holiday season, but don’t stick around for much longer - causing an actual negative ROI for the business in the long-run.
While offering discounts during the holiday season is accepted and implemented by many, some companies are using a different method to prepare their customers for the holiday shopping.
Let’s start out by saying that curiosity goes a long way. Yes, it did kill the cat, but within the marketing industry, it has proven to be an efficient and cost-effective way of getting through the holiday season. Here are some examples of how companies used simple customer curiosity to promote their brand:
The Teaser
Take Black Friday, for example. Most brands publish their deals either the day of or the day before Black Friday. As a consumer, I can attest to the fact that this practice drives me crazy! I’m a planner, and the only way I know how to plan my shopping spree is by knowing what's available, at what price and where.
This is where the teaser comes into play.
Two or three days before Black Friday and before customers are bombarded with Black Friday emails and campaigns from other brands, consider sending your customers a teaser campaign. The campaign template should be the same coloring, creative, and type of wording as the main Black Friday campaign so that customers (on the day of Black Friday) can easily recognize your brand’s campaign. This will make your campaign stand out, since your customers will already know what to look for.
Timing is key since we don’t want to send the campaign out too early (to prevent customers from holding out on shopping until Black Friday), but also leave enough time before the commercial holiday so customers know what they are looking for and remember to shop with your brand.
As for the content, it’s up to you how much you want to share, but note that you don’t need to specify what items will be on sale. You can give consumers just enough information to spark their curiosity and search for your campaign in their inbox.
The Pleaser
Let’s admit it, we all want to feel like we’re special and that we’re not “part of the pack,” and so do our customers. Loyal customers that have been with a brand for a long time and/or VIPs want to feel appreciated and acknowledged for their loyalty.
Reward them with a unique experience such as a special “Just for you” offer, a sneak peek into the upcoming holiday deals, a pre-sale event, etc.
The idea is to give customers a unique and special experience, not necessarily discounts or special offers unavailable to other consumers. The content/timing/creative of the holiday season sales will include copy that acknowledges this segment as your VIPs and/or most valuable customers.
This also works well with shoppers who have churned, but were previously VIPs. It’s a great way to remind them that although they haven’t purchased in a while, they are still appreciated and the relationship they previously had with the brand is still valued.
Another way that brands create uniqueness is in the actual shopping experience. Make it fun! Competitive! Enticing! Gamification campaigns turn the customer shopping experience into a game or a competition. Some examples of this are “Spin the Wheel” or “Scratch Cards.”
Lastly, make the shopping experience as easy as possible. Click2Cart, for example, allows customers to add items for sale into their cart directly from the email campaign. This is much more than just a trip to your cyber mall, but rather a whole new experience for your customers.
And Julius Caesar
AKA the Optimove way: segment your customers, segment your offers.
Define holiday offers by what your customers have previously responded to. Churned or dormant customers may need a bit more nudging, so send them a stronger incentive, vs. an active customer who, regardless of discounts, is continuously purchasing from your brand.
One type of segmentation is by ‘previous categories purchased’ by individual customers. If Julius Caesar only purchases sleepwear and Marc Anthony only purchases jewelry and accessories, then we would want to send them offers that compliment their past purchasing behavior. It seems that Julius is more of a sleepwear consumer and Marc is more interested in day-to-day style. For Julius, you might try to cross-sell socks, slippers, more pajamas. For Marc, you might send a campaign for dress-wear, shoes and more accessories (because you can never have enough). You get the gist. Recommendation models are most effective for these segmented campaigns.
Another type of segmentation is offer based. Send offers based on average purchase amount of each customer. If you know that a customer is spending $100 on average per order, structure your campaign accordingly. If you want to save resources and/or don’t have a full view of what each individual customer’s AOV is, try laddering your offers/templates:
- 10% off purchases of $100 or more
- 15% off purchases of $200 or more
- 20% off purchases of $300 or more
In summary: Stand out from your competitors. Show your customers that you are not like all the rest, not in your offers, not in your campaigns and not in the experiences that they will have with your brand. A customer’s shopping experience combined with valuable products will pave the way for not only this holiday season but will help you acquire long-term, loyal customers. Happy holiday, all. May the shopping commence.
January 14, 2019
Where Does #CustomerTech Fit into Martech? It's at the Center
By Chuck Leddy, Zylotech
Oh no, you might be saying after reading the headline above. Not another new category in the crowded Martech space, one already filled with thousands of vendors and multiple technologies. Yes, #CustomerTech is a relatively new definition, but it’s here to stay and may well be the future of marketing.
What is #CustomerTech?
Marketing legend Doc Searls defines #CustomerTech as “standard ways for customers to engage with companies at scale -- for customers.” So #CustomerTech puts customers first and places them (not marketers) in the driver’s seat. What’s driving the growth of #CustomerTech? Customers want control over their data, and big regulatory changes are giving them more of that control.
What Customers Want Most: Ownership of Their Data
Customers want decision-making power over their data. They want to opt in, to grant marketers permission to reach out to them with messaging that the customer both invites and values, rather than getting spammed by marketers with irrelevant content and being forced to opt-out/unsubscribe. Marketers may talk about “capturing target customers” and “driving them through the funnel,” but unsurprisingly, customers don’t like being viewed as cattle to be corralled and driven through funnels. In fact, customers want to use technology to control how, when, and by whom their data gets used.
This is where #CustomerTech comes in. It flips traditional Martech on its head, because the customer gains the capacity (and tech tools) to decide how they’re marketed to. As a marketer, you can no longer market “at” or “to” customers, but you’ll increasingly need to market “with” customer permission, mediated through #CustomerTech which enables customers to decide the who, what, and when.
Regulatory Changes Support CustomerTech
On May 25, 2018, the new General Data Protection Regulation (GDPR) came into effect across the European Union and for all EU customers, radically altering the way marketers collect, store, and leverage customer data. By the way, just because you don’t do marketing in the EU doesn’t mean you can disregard GDPR. The state of California, for instance, passed a new law quite similar to GDPR. Other U.S. states and nations will likely follow suit, transforming the way companies can market to customers.
Why Marketers Need to Care
The consequences of more customer control over their data and the arrival of new regulations supporting said customer control are simple: (1) more #CustomerTech will emerge to enable customers to “own” their data and control how you market to them, and (2) different approaches and technologies will be needed by marketers based upon gaining customer opt-in/permission and keeping it, as well as supporting compliance with emerging GDPR-like rules. So for marketers, the days of “spray and pray” spamming or consent authorizations buried deep within standard licensing agreements (that nobody reads) are disappearing, much to the delight of customers.
These major changes mean that marketers have no alternative but to take more care in “earning” and retaining customer consent. Marketers need to offer customers value in the content they share, and will need to be hyper-aware that customers always have the right to deny you permission to market to them at any time.
The customer is at the wheel now, and may permit savvy marketers to come along for the ride. But those marketers had better earn their place in the car by offering customers valuable conversations that inform and entertain. New regulations and #CustomerTech are enabling customers to assert control. Marketers don’t have to like it, but it’s the new reality that #CustomerTech helps enable.
January 10, 2019
Drive Smarter Experimentation and Personalization with Fully Integrated Data
By Julie Graham, Tealium
When it comes to your experimentation program, your experiments are only as powerful as the data you use. When customer data is siloed and fragmented, it impacts the user experience and internal operations. You need to ensure your team is leveraging the most comprehensive visitor and event-level data to build better cross-device user experiences and create highly targeted experiments and personalization strategies for audiences.
Kyle Brierley, Director of Global Integrated Solutions, Tealium, recently did a webinar on “Driving Smarter Experimentation and Personalization with Fully Integrated Data.”
In this webinar, Kyle took us through key ways on how to fuel better user experiences with centralized, comprehensive and enriched data. He also showed attendees how to stitch together anonymous and known visitor data to then leverage those audiences to generate more consistent cross-device experiences. You can find key takeaways below.
Experimentation Is Easier Said Than Done
Some factors that can limit being able to do so:
- Lack of a culture of innovation
- Legacy systems hinder innovation
- Processes are complex and time consuming
- Internal resources are limited
- Organizations may be risk-averse
When Universal Action Is Possible Through Universal Data
- Marketing is less reliant on engineering
- Data teams have clean data from all sources
- Businesses and Execs have accessible and actionable insights
- Product teams can make informed testing decisions and rollouts
- The end user gets a unified experience across all devices and contexts
3 Key Components Of A Powerful MarTech Stack
- Collect and standardize data in real time
- Integrate data across end-point solutions and contexts
- Translate data into actionable insights
To get more key takeaways and knowledge around all things personalization and experimentation, watch the on-demand webinar and learn:
- How to leverage both online and offline data to deliver a seamless user experience across devices
- How to connect client-side and server-side integrations for increased performance and real-time experiences
- And so much more!
Watch the on-demand webinar and start personalizing and experimenting with your campaigns today!
January 7, 2019
How Marketing Is Better With AI
By Ariella Brown, Zylotech
It is a truth nearly universally acknowledged that a comprehensive marketing strategy must include AI. Most marketers know that AI is the wave of the future. Yet a disconnect persists between that aspiration and the actual number of AI users. Nevertheless, current trends indicate that CMOs and CIOs will have a meeting of minds on the benefits of committing to AI, and businesses will soon reap the benefits.
Recent research from the IBM Institute for Business Value found that the overwhelming majority -- 91 percent -- of marketers at successful companies believe AI plays a central role in their continued success. Yet, the same research shows that only around 25 percent say they currently make use of it.
While a quarter falls far short of the 91 percent, it still is better than four percent, which is the number that appeared in Gartner’s 2018 CIO Agenda Survey. The survey found that only four percent of CIOs have implemented AI in 2017.
However, that pitifully low number does not tell the whole story. The same survey reported that 46 percent of CIOs said they have plans to implement AI. So adding up ones who already have it with the ones who are indicating a commitment to adopting AI yields 50 percent getting on board for the tech in 2017.>
That number appears to grow on the marketing side to 60 percent in 2018, according to a BrightEdge survey of enterprise marketers. Clearly, interest in AI is on the rise both for the CIO and the CMO at forward-thinking companies that have demonstrated that it contributes directly to their success.
In fact, the McKinsey Global Institute Study, Artificial Intelligence, The Next Digital Frontier devotes a full 80 pages to exploring the different ways in which it can be implemented in various business operations. It found that many businesses have already reaped the advantages and pointed out “that serious adopters have significantly higher projected margins than others.”
What’s key to the improved profitability, though, is a full commitment built on “a strong digital starting point,” the report warns. This is why it is necessary for the CMO to get the CIO to embrace the digital transformation that needs to be in place for effectively leveraged AI.
What’s holding them back? McKinsey’s survey found that 41 percent identified “uncertain return on investment” as the most significant obstacle they faced in getting their firms on board for AI.
But if done right, the return is not uncertain at all, and it’s up to marketers to demonstrate their business case. They can cite some of the figures in the report, for starters.
Sales will go up with AI-enhanced marketing. “Insights-based selling, including personalized promotions, optimized assortment, and tailored displays, could increase sales by 1 to 5 percent,” the report says. Even more impressive is the potential 30 percent growth in sales that can result from “this kind of personalization, combined with dynamic pricing” for internet sales.
The McKinsey report cites the example of Stitch Fix, the personal shopping service that can figure out what a customer wants even if they fail to describe it. Its algorithm reviews the images they favor on Pinterest to get a sense of their taste.
Even retailers who sell in physical stores stand to benefit from AI-enhanced marketing. The report anticipates that “virtual assistants could identify repeat customers using facial recognition, analyze their shopping history to make suggestions, and communicate in a conversational way using natural-language processing and generation.”
Some establishments are already exploring these possibilities, tapping into the power of machine learning and computer vision to deliver responsive, personalized experiences to virtual assistants presented as holograms or to dynamic shop windows. With AI, it’s possible to tap into digital data while presenting an impressive in-person experience that’s a far cry from the static setups associated with old-school retail.
Whether a business faces its customers primarily in-store or online, AI can effectively turn out data in real time making its marketing a lot more personalized and a lot more effective. With the CIO and CMO both on the same page about full commitment to AI, they are set on the path to greater profitability that all successful companies will be taking.
January 3, 2019
Building a Foundation for ML/AI Readiness – Part 1
By Catherine Ballantyne, Tealium
 After a day of mountain biking in the Kosiosko Royal National Park in Australia’s Thredbo Valley, I found myself in the depths of a serious conversation around Artificial Intelligence with a fellow cyclist, whilst hurtling down a mountain trying to avoid falling off the side of a cliff.
Laughing as I realised he knew far more about data than I ever will know, one of his first comments was, "All this talk about Artificial Intelligence is way off – Machine Learning will come first. Those talking about AI are literally doing just that, talking. AI will follow long behind."
It is not often I find a kindred spirit, especially one who rides a bike like that, but there you go, that’s what they call serendipity.
This conversation got me thinking, and doing so has lead to this 4-part blog series, where we will explore what successful organisations who work with Tealium in Australia are doing to prepare for Machine Learning and Artificial Intelligence beyond.
We are currently having a lot of conversations with customers and prospects who want to know about Artificial Intelligence and whether Tealium is going to solve the challenges of scale and automation through AI. The answer to this is yes … and no. Do we believe, Machine Learning and Artificial Intelligence will eventually enable much of the long sought-after nirvana where our electronic relations with people interested in our brands will be as fluid, relevant, and consistent as those we have created in the face to face world? Yes. Is Tealium embracing that as part of what we offer? Most definitely yes.
However, we would be negligent if we simply told you to go out and buy another tool (what is another one in an average stack of 90) that you can solve the problems of scale and automation with. Unfortunately, it doesn’t work like that.
If you are puzzled over how to bring together the many growing sources of data you have, to unify that data, understand it and act on it, all in the same time it takes your customer to click the purchase button, then read on, Macduff.
At Tealium we have spent ten years understanding what makes data fast and useful to all, and whilst we are backing the capabilities of ML and AI to ultimately solve these issues, we don’t foresee it will happen by some magical shake of a wand. Anyone seriously embarking along the path to the world of ML and subsequent AI is preparing now by consciously building a neutral, cross-channel dataset. One that is ‘Machine Learning Ready’ so that when they are ready to start utilising the power of Machine Learning, their data will be ready, too.
When developing something truly unique that maps unchartered territory, it helps to have some guiding principles upon which to make decisions.
We find our most successful ML pioneers are building a neutral dataset which is anchored in the following principles:
- The customer at the center
- Real time
- Data readiness/access ownership
- Privacy, governance, compliance and security by design
- AI/ML Ready
These principles form the basis upon which brands are consciously designing a unified, customer at the center, first-party dataset. Not just giving lip service to the concept of data as an asset, but actually treating it as such. The principles and their outcomes can be summarised as follows:
Customer at the Center: This concept has many facets, but when it comes to communications, the underlying dataset must be one primed for delivering what the customer needs next, not what an organisation wants them to receive. All decisions on how, when and what to communicate should be based on the state of the customer (or potential customer) with respect to the organisation at hand. That can only be achieved if there is a centralised understanding that can be activated as it is generated. To be effective this must be:
- Responsive: Data must be responsive and fluid to provide that edge we call ‘experience’
- Data-Centric: As we have moved into a world of distributed trust, data is needed to reliably bridge the digital relationship gap
- Invest in Content: Data is part of it, content is also a key and needs investment
- Continuous Testing, Learning and Adjusting: Continuous learning and adjustment will become the new business as usual
Real time: The moment matters in the electronic world just as it does in face to face relationships. Ask any shop assistant how they make the most sales – it is by watching and listening to their customer’s needs and responding appropriately. There is no campaign, channel or device. Data is the language of relationships, so to capture the moments that matter most, data must be activated in real-time by design.
Access and Ownership: As the volume of data grows, organisations must enable data democracy. For many it is also about ownership of first-party data in a consolidated manner:
- Organisational Data Freedom: To scale its use, businesses must ensure data is accessible, understandable, and usable by all.
- Omnichannel: The key to this starts with a data layer. This concept is born out of digital which has become omnichannel. It consists of standardising and unifying all data, from all places at the point of collection, in the language of the business, thus creating a fluid dataset in a language that everyone understands.
- First-Party Data Focused: First-party data is a brand’s greatest competitive asset as no other organization has that dataset. Control, ownership, and activation will be key to fully realising its value. It is not only the history of previous transactions and loyalty but also the behavioural data that provides the insight to intent with first-party data.
- Internal Alignment: This is not just a technical requirement but will satisfy business strategy as well. It is important that all internal groups participate in both the definition and implementation of internal alignment.
Governance By Design: Whether you call it governance, privacy or simply respectful, sustainable relationships and the complexity of communications, combined with the growing understanding of the value of data, means organisations are now architecting this into their datasets and tools.
- Consent: Ensuring consent is stated before any data is captured is key as is the ability to review or apply this as required.
- Transparency of Use: To maintain a sustainable relationship where data continues to be freely given will require its use be transparent.
- Consumer Rights: With this transparency, the consumer will increasingly expect the ability to review, delete and be notified of the data a brand has about them and organisations will need to provide real-time capabilities to do this.
- Control and Audit: Finally, to facilitate all these capabilities requires fundamental data control and audit. It will be imperative to concisely articulate the data you collect and how and when it is distributed.
AI/ML Ready: As the volume of data grows, so will the need to automate it using the power of Machine Learning and beyond. Having built a dataset that is customer-centric, real time, accessible, and properly governed means organisations will be ready to utilise the data. The core processes to enabling this mandate are:
- Valid and Consistent Streams of Data: First up is the development of a framework which enables the efficient and consistent onboarding of new data streams which are ready for consumption by Machine Learning and the subsequent activation. This includes engineering the needs of Machine Learning into your organisation’s omnichannel data layer.
- Listen and Respond to That Data: Once the framework is in place, working with what the data tells you is important. Interpretation will be as important as generation.
- Hire, Train, Enable, and Prioritise: This is new, not all the capabilities will exist in your current team – you will need to hire, train and enable employees to achieve these goals. As this happens, prioritise your projects and use these to form the basis of your training and recruitment.
- Brand: Marketing is an art and a science, ultimately data and Machine Learning are still tools to enable the scalable delivery of your organisations brand and the messaging and engagement is the art of this piece.
Tealium helps our customers build a strong data foundation first, one firmly anchored in the bedrock of an owned, accessible-to-all, neutral, governed dataset that will be fluid in nature. We believe this will enable capabilities such as ML to flourish, but without the principles as fundamentals, there will be a constant need to rebuild the underlying architecture. A process that is costly both in time and resource.
The first step towards ML readiness is building a strategy and roadmap for uniformly onboarding data from any source in a state that is immediately ready for use by all.
This is a concept we will explore in part 2 of the ML/AI Data Readiness blog post series.
December 21, 2018
Why You’re in Desperate Need of a CDP
By Tabi Yoo, Simon Data
What makes a marketing campaign effective? Personalization.
What’s essential to drive effective personalization? Customer data.
That said, just collecting customer data doesn’t mean you’re at the end of your journey to successful marketing; it’s just a step in the right direction. How you utilize this collected data is incredibly important.
First and foremost, your data is not stored in one place. Email engagement data, on-site/in-app activity, transactional data, etc. all live in different places. And what’s worse, they all have different data structures with no common identifier, meaning linking all of your disparate data sources requires painstaking manual work. In other words, if John Smith were spending time on your website and separately opened an email, it’s often difficult to tie both actions to him.
Point is: it’s very hard to get a unified customer view when all the customer data you’ve obtained is scattered, and it’s even harder to build a relationship with your customer without this cohesive, single customer profile. The difference really comes from having one coherent conversation with your customer instead of multiple rudimentary ones that prove both ineffective and a little disorienting.
This is where a Customer Data Platform, or a CDP, comes into play. By aggregating all of your customer data into one place, a CDP allows you to fully understand customer behavior across all touchpoints. But the real difference between CDPs and other CRM or data management platforms is that CDPs are designed specifically for marketers.
Let’s take a peek into what managing the customer lifecycle without a CDP look like.
If that sounds difficult to you, you’re right. This is where CDPs come in.
Since CDPs are meant to be marketer-managed, you no longer need to rely on engineering resources to create and deploy personalized marketing campaigns. Further, the most advanced CDPs support both advanced customer segmentation and actual marketing execution, giving marketers a one-stop-shop for building and targeting audiences across channels.
This kind of power has real revenue impact. A recent McKinsey study focused on the value of CDPs cited a multinational retailer that leveraged customer purchase data alongside on-site activity to trigger targeted messaging to specific segments of customers. The result? The company doubled its email open rate from 10–15% to 25–35%. Similarly, the same study highlighted a travel company that employed the same personalization in their marketing strategy to drive a 10–20% boost incremental boost in conversion rates and customer lifetime value.
A CDP isn’t strictly necessary in order to build this crucial relationship with your customer, boost relevant KPIs, and drive an increase in revenue, but going at it alone isn’t easy. Moreover, there’s a real opportunity cost in doing things the old-fashioned way. Time lost due to manual processes isn’t just a quality of life issue for marketers. Moving slowly here means running fewer tests, optimizing less efficiently, and losing out on the ability to capture opportunities as they arise, all of which have significant revenue implications.
Better personalization is rapidly changing from a nice-to-have into a necessity, but building out these capabilities doesn’t have to be painful. After all, that’s what a CDP is for.
Sounds too good to be true? Give us a shout at hello@simondata.com.
December 18, 2018
Designing a Data Supply Chain for Data-As-A-Service
By Julie Graham, Tealium
 Having a clearly defined data strategy is a necessity in creating exceptional moments with customers and ensuring long-term brand loyalty. Yet most organizations aren’t sure which strategies and methodologies need to be implemented to ensure accurate and seamless alignment of data within the overall business strategy.
Ted Sfikas, Director of Solutions Consultants, North America and LATAM, Tealium, recently did a webinar on “Designing A Data Supply Chain For Data-As-A-Service.”
In this webinar, Ted showcased why data is so critical to an organization, what Data Center of Excellences are, and what the requirements are of data today. You can find key takeaways below:
Data Requirements Today
- Data must be up-to-date, trusted and available in real time
- Every department needs the same first-party data set, at exactly the same time
- Privacy and regulations will require a central, governing approach that applies to all
- Implementation must be flexible and modern – data must flow freely throughout the enterprise and must be orchestrated centrally at some point
- Future proof: Machine Learning (ML) is upon us and without a central, governing entity to orchestrate, the business will be adversely impacted as competition moves ahead
3 Keys in a Data Center of Excellence
The CoE can be a virtual organization or a dedicated one. It covers the “people”, “process” and “technology” needed for a data supply chain:
People
- Subject Matter Experts
- Solution Delivery
- Service Administrators
Process
- Data Strategies
- Best Practices
- Data Chargeback Model
Technology
- Data Collection Technology
- Data Enrichment Technology
- Data Activation Technology
What Is Data-As-A-Service?
- The technology delivery method for a Data Supply Chain
- The most cost effective and lowest risk approach for modern organizations
- Delivers on the promise of built-in, unified capabilities for every stakeholder, regardless of platform or department
To get more key insights and takeaways on Data-As-A-Service, watch the on-demand webinar and learn:
- Why a data supply chain is critical in creating a high performing data and analytics program
- How to build a data supply chain
- 4 ways data-as-a-service becomes a necessity for organizations
- How to standardize how data is requested and received
Watch the on-demand webinar and start designing your Data Supply Chain for your Data-As-A-Service strategies today!
December 14, 2018
Decrease Risk Through Holistic View of Data
By John Nash, RedPoint Global
Many data-savvy marketers and martech professionals think about how they can use data to create a competitive advantage. That’s crucial in today’s environment, where the more you show customers that you understand and value them, the better your marketing outcomes will be. But that’s not the only benefit of building the type of holistic view of customer data that allows marketers to deliver contextually relevant experiences and communications. There’s another, equally important, benefit: risk avoidance.
Privacy is paramount and ensuring it can be expensive if you have the wrong processes or systems in place. Whether put in place by federal, state or industry regulators, regulations such as GDPR loom large for marketers, as well as their entire organization. There are other business and technology risks that can be avoided by having robust customer data platform (CDP) capabilities. Decreasing risk by creating a comprehensive and accurate single customer view is a prudent and potentially profitable approach.
There are four primary ways to decrease risk through a customer data platform:
- Improve compliance through strong closed-loop processes, e.g. for GDPR,
- Increase data quality, and lower costs of maintaining customer data through advanced matching and streamlined Master Data Management (MDM),
- Improve cross-team collaboration, through connecting all data and making it accessible across the enterprise,
- Future proof your enterprise to enable innovative customer experiences via emerging technologies, through an open garden approach.
Think about how you currently access the data you need to optimize customer engagement. You may have robust demographic and purchase data in one system and rich online behavioral data in another. Your primary goal in bridging those silos may be to provide your frontline staff with access to the real-time data they need to provide the most relevant interactions, offers, or communications at each customer’s moments of truth.
Certainly, it’s essential to provide that access. But without the right tools and processes, you may be setting up a data free-for-all that leads to non-compliance with regulations, poor data quality and maintenance practices, and siloed operations.
Creating a holistic view of customer data that solves these issues is easier, and more cost-effective, than you might think. The solution is a customer data platform. A CDP is a marketing technology that enables brands to integrate all types and formats of customer data (batch and streaming) to build a golden record of a customer or consumer — including anonymous-to-known behaviors, preferences, and purchase history. This not only enables you to hyper-personalize cross-channel customer interactions in real time, it also helps you to reduce the risks associated with siloed data.
Currently, only 48 percent of executives polled by systems integrator Dimension Data for its “Global Customer Experience Benchmarking Report” have a customer analytics system, and only 36 percent have a big data analytics solution in place. Companies embracing analytics and big data have a distinct advantage when it comes to both personalized customer experiences (CX) and risk avoidance.
Data access helps us to transform and innovate our marketing and CX, and as a result, reach new levels of marketing performance. But that access comes with its share of risks, including privacy, data quality, and fiefdoms. Using a CDP can help you reduce these risks. The key reason: CDPs connect all types and sources of customer data in real time, which creates a unified customer view that is accessible across the enterprise. This level of data access may at first seem too broad if it’s not what you’re used to. On the contrary, it provides a single point of control to effectively guard against privacy and quality issues. It also serves as bridge across data silos, which encourages data sharing and collaboration instead of guarded fiefdoms.
One of the biggest concerns in terms of managing the vast stores of customer data most marketers have at their disposal today is compliance risks. A CDP can lower these risks by helping you achieve compliance with regulations and industry mandates such as GDPR, HIPAA, PCI, and GLBA that focus on personally identifiable data.
Linking a CDP to a customer preference center is one way to decrease compliance risks. Providing a consent-based preference center for individuals allows them to select the exact ways they want their data accessed, processed, and forgotten. It should also enable them to set their preferences for communication across all interaction points along the customer journey. This creates a closed-loop process between data capture, preferences, and use. Along with reducing compliance risks, this approach will ensure that you’re delivering an excellent customer experience, one that’s personalized and delivers high value to consumers, so they opt-in rather than opt-out of your communications. That’s a marketing hat trick.
A CDP can also lower costs and reduce the efforts of maintaining and managing customer data. For example, using a CDP to automate manual data processes, Xanterra Parks & Resorts reduced the number of hours required to prep data by 80 percent. That efficiency not only reduces marketing costs, it also increases marketing performance by allowing Xanterra to get new communications and offers in market faster and enables its marketers to focus on higher-value activities. This overcomes traditional silos and takes the risk out of the complex process required to turn data into business value.
CDPs further help to guard against data quality issues. A CDP creates a singular, accurate, and continuously updated golden record of each customer through probabilistic and deterministic matching algorithms that is maintained with a persistent key. CDPs with streamlined MDM capabilities also ensure that as data is curated in creating precise customer and household views, there is proper data stewardship and governance in place to ensure quality. This solves customer identity challenges that are essential to risk avoidance and will quickly surface any data quality issues.
At a time when most data scientists spend 50 to 80 percent of their time prepping data, according to estimates by The New York Times, maintaining data quality to cut that prep time is more important than ever. And, as it turns out, is profitable: Research from Dun & Bradstreet and NetProspex shows that companies that regularly maintain their database can see 66 percent higher conversion rates than those that don’t.
The most important risk avoidance may be using CDPs to future proof your business, so you can continually innovate customer journeys in ways that take advantage of emerging technologies. This requires CDPs with an open garden approach that allow you to connect to new sources of data and new touchpoints in the future, including real-time engagement through mobile, social and Internet-of-Things (IoT) touchpoints. This effectively avoids the risks of becoming locked into technology that may otherwise prevent you from developing customer experiences that are competitively differentiated.
As an enterprise solution, a CDP can help you bring data together from across your organization, allowing you to know all that is knowable about your customers. You can then deliver relevant, personalized engagement across channels and interaction touchpoints. And just as importantly, it enables you to decrease the risks associated with managing volumes of customer data, improve data quality and maintenance, ensure compliance, and enhance collaboration — all while improving the customer experience and building a profitable competitive advantage.
There are four primary ways marketers and martech professionals can use a CDP to drive immediate and long-term business value. One is risk avoidance. The others are:
- Innovation – Increased market value through breakthrough innovations
- Revenue lift – Increased revenue through improved customer experience
- Operational effectiveness and efficiency – Decreased costs through improved operations and customer understanding
You can read more about innovation in “Increase Market Value Through Customer-Led Innovation,” about revenue lift in “Increase Revenue Through Improved Customer Experiences,” and about operational effectiveness and efficiency in “Decrease Costs through Improved Marketing Operations.”
December 12, 2018
CDP Institute Workshops Set for January in Brussels, Utrecht and Munich
By David Raab, CDP Institute
 The CDP Institute will run half-day workshops in Brussels, Utrecht, and Munich this January, working with local partners in each city. Each workshop will be presented by CDI Institute Founder and CEO David Raab. Format will include a mix of lecture, discussion, and exercises to ensure that each participant achieves a working understanding of key issues and opportunities. Topics will include:
* Why CDPs matter• How CDP fits into the larger marketing data architecture• Key benefits provided by CDPs• An overview of the CDP industry and trends
* How CDPs create value• How CDP relates to business and marketing strategy• Developing CDP use cases• Uncovering requirements for CDP success
* Selecting the right CDP• Requirements definition• Key differentiators• Specific features to look for• Running an effective selection process
* CDP deployment planning• Readiness checklists• Overcoming organizational roadblocks• Project planning• Finding the right deployment sequence
Check out this very nice video overview. Then, use the links below for details and to register.
January 22, Brussels. Hosted by NGDATA and Business & Decision. Register here by December 24 for €100 discount.
January 23, Utrecht, NL. Hosted by Squadra NL and DDMA. Register here by December 24 for €100 discount.
January 29, Munich. Hosted by b.telligent. Register here by January 7 for early bird discount.
December 11, 2018
CDPs Aren’t Just For Marketers
By Mike Anderson, Tealium
There’s still a common misconception that CDPs are just a marketing tool, but in reality, their scope extends much further than that.
While it is true CDPs can help marketers take control of disparate data and deliver consistent customer experiences, there is no reason they can’t do the same for other departments within an organization. As technologies that build unified, persistent, and accessible databases, these platforms offer equal value for sales and business intelligence teams. And in fact, the CDP Institute recently amended the definition to ‘packed system’ as a reflection of broader applications.
To prove that CDPs aren’t just for marketers, let’s explore four examples of their use in wider business:
Upgrade the Customer Service Experience
A complete view of the individual — both the ‘known unknown’ and existing customers — is vital to fuel great service and results, and this is exactly what CDPs provide. By collating event-level data streams from a multitude of channels in real time and combining it with offline legacy data, these tools give sales teams in-depth knowledge about the activities of specific consumers: such as purchase history, items they are browsing, and abandoned shopping carts. Using this insight, call center staff can engage in highly personalized conversations with customers and deliver exemplary customer service.
For example, say a customer calls about a delayed delivery. By logging into the easily accessible and consolidated system, call center staff can quickly retrieve specific order details, such as order placement and expected delivery date. This information can then be leveraged to personalize the conversation and provide a resolution that suits the particular customer — be that free delivery on a frequently recurring order or a partial refund — and ensure their company relationships stays strong.
Smarter Business Intelligence
Customers should always be at the center of business strategy and CDPs generate the granular and constantly refreshed intelligence needed to ensure company goals align with customer needs. By orchestrating data held about individuals across the business, translating it into unique profiles that are continuously updated with new information, organizations can achieve a clearer picture of who their customers are, how they behave, and what they want. This comprehensive information can become the foundation for better decisions, from which markets to invest in — based on the location of current customers — to pricing or future product development. Plus, fast access to feedback about products and services also means adjustments can quickly be made to resolve concerns, keep customers happy, and stay ahead of rivals.
Opening the Door
CDPs can facilitate greater data control across the organization; some have an integrated privacy and consent management tool allowing data leaders to permit and revoke access to data, pending employees’ roles and the business purpose. This enables data controllers the chance to ensure there is a legitimate business interest for employees having access, the data has been collected with consent, and consumers are made aware of how their data is being used.
Similarly, tools such as these might allow some data handlers to view customer information, but block them from editing; meaning that a customer’s “master file” cannot be inadvertently deleted or changed, which could cause problems – such as delivering inappropriate communications and ruining the rapport.
Expanding Horizons
By employing a CDP to re-order data sets and enhance their customer understanding, businesses can obtain valuable insights that benefit the entire company. From addressing customer pain points to adapting products and strategy, these versatile tools give firms the information needed to fuel consistent growth and revenue.
Ultimately, organizations looking to optimize consumer insights need to stop restricting use of CDPs to just marketing teams. Their capabilities extend far beyond one department and can have a tangible impact across wider business objectives, if given the chance.
December 7, 2018
Decrease Costs through Improved Marketing Operations
By John Nash, RedPoint Global
Personalized customer interactions and communications do more than deliver engaging and profitable customer experiences. They also allow marketers to optimize campaign performance and omnichannel marketing, which reduces the costs of customer interactions. In fact, marketers who use personalization can reduce their acquisition costs by as much as 50 percent and can increase the efficiency of their marketing spend by 10 to 30 percent, according to Harvard Business Review.
A customer data platform (CDP) is the engine that can help you decrease costs through fine-tuning operations based on deeper customer understanding. As a result, you’ll be able to drive growth by monetizing your customer data and save money by improving operational effectiveness and efficiency. The insights and actions you can drive from a CDP will help you to:
- Lower customer fatigue, churn, and service costs,
- Shift your channel mix to lower-cost channels,
- Reduce the number of customer interactions required to drive results, and
- Improve marketing, IT, and data science resource utilization.
CDPs support these outcomes by creating a single source of true information for each customer. A CDP ingests customer data from all sources across an organization in real time to create a holistic, always up-to-date golden record that supports effective and profitable data-driven marketing activities such as personalization.
Simply put, using a CDP to get a clearer understanding of customers’ channel and interaction preferences enables you to personalize the customer experience and, as a result, reduce the number of customer interactions required to drive results. Further, by using a CDP to access cross-channel data at the customer level, you can vastly improve attribution and channel mix. By assigning truer values to your marketing activities, you’ll be able to better assess channel performance and then shift activities and budget to the most effective and cost-effective channels.
Additionally, by using a CDP for data-driven marketing techniques such as advanced matching and householding it’s possible to achieve outsized results even within specific channels. These techniques, for example, can put an end to over-mailing, which, in turn, reduces physical direct mail budgets by 20 percent on average.
Optimizing marketing can help reduce costs in other ways. For instance, using a CDP to support progressive profiling and dynamic customer journeys provides a better customer experience by allowing you to respond to customers in their moment of truth, no matter where they are. As a result, customers are more likely to convert. The ability to be wherever customers are as they consider a purchase is not only prudent, it’s essential. According to McKinsey & Company, 50 percent of customer interactions today happen during a multichannel, multi-event journey.
A CDP also reduces operational costs by creating privacy-compliant persistent customer records that are accessible throughout the enterprise, and available in real-time when needed. CDP’s provide a single point of control over data, empowering both applications and users to access it from across the organization to ensure the best customer experience, which improves efficiency. IT, data science and marketing resources are then able to spend their time creating powerful analytic models and customer experiences rather than struggling with inconsistent, inaccurate and inaccessible data. A CDP should also provide a single point of control over its data to achieve the best ROI. This allows you to leverage your existing IT investments without having to rip & replace systems that are in place today, another cost-saving operational benefit for the organization.
Overall, using a CDP to support personalized marketing can lead to double-digital savings. According to McKinsey, data-driven personalization generates a marketing-budget saving of 30 percent.
One company seeing operational improvements and related savings through personalized marketing is Xanterra Parks & Resorts. The company consolidated dozens of sources of customer data from across its properties and systems, including transactional and management systems each with unique characteristic and complexities. Xanterra used that integrated data to create rich 360-profiles for nearly all of its customers and prospects. Using those profiles, Xanterra’s marketing team was able to identify key customer segments, craft detailed personas and journey maps for each, and use them to deliver personalized communications at scale — throughout the entire customer lifecycle. The company reduced its marketing costs by an astounding 40 percent through personalization and now sees percentage improvements in marketing performance that routinely reach three figures.
Even when you consider the direct costs of implementing and running a CDP — software license, professional services and training, infrastructure, and support, maintenance, and data enhancement — you still clearly see a return on investment from the revenue growth and cost reductions you’ll gain by using one.
Industry leaders are capitalizing on this opportunity. Currently, 22.4 percent of executives polled by systems integrator Dimension Data for its “Global Customer Experience Benchmarking Report” say customer experience is a top strategic indicator of operational performance. Those companies and others like them have an advantage because they understand the cost-savings potential of CX and the data-driven marketing that underpins it.
Plus, increasing personalization across channels to deliver that stellar customer experience not only reduces operational costs, it also drives revenue — a potential 500 percent overall boost in consumer spending when marketers take an omni-channel approach, according to The eTailing Group and MyBuys. The research also found that 40 percent of consumers buy more from companies that personalize their experience across channels.
Investing in a CDP not only reduces costs by improving operational effectiveness and efficiency, it also drives additional revenue. The result is a double dose of profitable marketing practices.
There are four primary ways marketers and martech professionals can use a CDP to drive immediate and long-term business value. One is operational effectiveness and efficiency. The others are:
- Innovation – Increased market value through breakthrough innovations
- Revenue lift – Increased revenue through improved customer experience
- Risk avoidance – Decreased risk through a holistic view of data
You can read more about innovation in “Increase Market Value Through Customer-Led Innovation” and about revenue lift in “Increase Revenue Through Improved Customer Experiences.”
December 4, 2018
6 Ways Customer Data Platforms and Personalization Drive Retail Revenue
By Erik Archer-Smith, Arm Treasure Data
Smart retail executives know they’re under pressure due to changing consumer retail behavior, new technologies, and the influence of digital-first companies like Amazon and incumbent retail giants with massive digital transformation budgets, such as Walmart.
But several recent reports suggest that maybe it’s not enough just to understand what consumers say they want; the trick lies in anticipating the things that they might want, for the right offer and the right appeals. In other words, data analytics and personalization, powered by customer data platforms (CDPs) might just make the difference between those who can’t survive, and those who thrive, in the current cutthroat environment.
Are Data and Personalization the Keys to Survival?
One thing that makes innovators like Amazon so successful in driving profits is their strategic use of technology to build loyalty through positive, personalized experiences. Using customer data brilliantly is the secret to this personalization effort.
And while it has long been known that about half of all shoppers report having bought a product that they had not originally intended to buy, it’s also clear that a ham-handed approach drives people away. Smart personalization blazes a clear path to revenue growth. But uninformed recommendations are ineffective, according to recent reports indicating that 79 percent say they’ll only engage with brands that personalize accurately based on their previous interactions and behavior.
Omnichannel Retail Understanding of Customers Is a Crucial Survival Skill
As the retail world has become omnichannel, customer journeys are in turn becoming increasingly sophisticated, as customers navigate their way to purchase their product of choice. Customers leverage online data before, during and after their purchase, often switching back and forth between online and offline channels, on a single buyer’s journey. More than two-thirds of consumers use smartphones for product research while in a physical store, mostly for price comparison with other retailers, product information, and/or online reviews.
Personalization of the Buyer’s Journey
True omnichannel personalization of “the customer journey” – one that spans online and offline channels – has eluded retailers for years. Part of the problem is that marketers, in an effort to do the best with the data they can access, try a one-size-fits-all to customer personalization. What we really should be discussing are “customer journeys,” plural, because most of the time, people not only come to experience your product through their own unique personalities, but through their own unique trajectories of successive experiences of the product, often through different paths and even totally different channels.
But it’s often tough to manage – or even be aware of – all of these different paths, experiences, customers, and prospects. That’s because most retailers keep their online and offline activities separate and stored in different databases. The data is siloed, because these activities are managed by different teams using entirely separate systems that don’t ‘talk’ to one another, making anything other than a cookie-cutter approach to modeling consumer behavior virtually impossible.
Here’s a clever way to use data to personalize your customer experience. First, gather as much data as you can on your customer. Next, enrich your first-party data with second-party data (partners) and third-party data (DMPs, data marts, etc) to help to fill in the gaps. That’s a good start at creating a clear picture of each customer’s activity and interests, updated in real time.
But how do you go about it? Here are some smart, real-world steps you can take to get started:
- Start with the purchase histories and customer data that you already have. You already have data from receipts, loyalty programs, email campaigns, and where customers live, to name a few important types of information that can help you analyze habits and behavior.
- Add tracking data from mobile devices. The analysis of mobile device location can help you understand your customers’ commuting patterns: for example, knowing how often they are near your store, what day of the week, and what time of day. If you have a mobile app, this data can usually be pulled directly provided the user has location services enabled. If not, you can look into partnering with location service partners to get this data.
- Add “in-store” data from sensors, beacons or in-store WiFi. Information such as when a customer is inside your store, which aisles or departments they’re spending time in, and what they’re buying is the next step. In-store sensors or beacons are an easy way to do this. But if you haven’t deployed those yet, you can get started by pulling data from customers who connect to your in-store WiFi. If you do, just make sure you update your terms and services to inform people that location data can be tracked while they’re connected to your WiFi.
- Combine the mobile device data with the in-store data into one unified customer profile. After these are combined, you can create customer segments around frequency of visits, in-store paths, and even the “halo effect,” where one customer’s behavior might influence another’s.
- Use the newly created segments to deliver customized coupons to customers while they’re still shopping. Many retailers are experimenting with this location-based approach, with some even tracking the aisle the customer is in and conditionalizing the offers to in-store customer location. Doing this avoids a common pitfall that some retailers suffer when their in-store and digital data is siloed: They only analyze mobile data to deliver mobile coupons and analyze in-store data for paper or email coupons. Muji, for instance, increased in-store revenue 46% by delivering coupons based on in-store shopping behavior, but delivering them in real-time on a mobile device when a customer was near a target item.
- Tie in weather and seasonal trend data to predict behavior and create personalized offers. Customer behavior is often profoundly influenced by weather. That’s why it pays to conditionalize offers based on what you know (or can predict) about the retail behavior of your customer segments. You might have to experiment some, with A/B testing on different offers, to tease out the details for your particular customers and product lines.
Customer Data Platform Technology Helps
A customer data platform (CDP) is key to marketing success as it collects and unifies data across the enterprise for a single, actionable view of your customer. Increasingly, these platforms are used to create and manage complex data sets that represent every detail needed to map a customer’s brand experience and create one source of truth for every customer profile. These profiles can then be shared with other marketing systems for the purpose of personalized marketing and/or predictive analytics.
Now more than ever, CMOs should be efficiently harnessing the data — providing their teams with tools that can measure customers’ propensities, frequencies, and rates to engage and purchase. Within a CDP, machine learning (ML) can be useful in finding your ideal customer segments based on a deep analysis of first, second and third-party data, personalized customer journey data, advertising/retargeting data, web personalization and recommendation engine data. It can also be used to pick the best creative and messages for each segment.
The sooner CMOs have the power to collect, aggregate, process and use big data more effectively, the better prepared they will be for success in the marketplace. The forward-thinking leaders in the industry are already moving towards a future of retail innovation, one where online and in-store will work together to generate loyalty and a smarter, more integrated shopping experience. CDPs, and the deep customer understanding they generate, might just be what saves some of these embattled companies.
November 30, 2018
Increase Revenue Through Improved Customer Experiences
By John Nash, RedPoint Global
“We’re customer centric” is little more than an all-too-common refrain today. The reality is that more companies talk about being customer focused than are actually doing it well enough to create a competitive advantage. Although 81 percent of executives polled by system integrator Dimension Data for its “Global Customer Experience Benchmarking Report” say they recognize customer experience as a competitive differentiator, only 13 percent rate their company’s CX as a 9 or 10. The challenge? CX is too difficult, according to about 44 percent of respondents.
Yes, completely overhauling your customer experience is difficult. However, it’s still possible to increase revenue through improved CX while leveraging existing engagement systems. The not-so-secret formula for doing so is by using a customer data platform (CDP) to create uniquely personalized, innovative customer experiences that spurs sales. You can see revenue lift using a CDP in three primary ways:
- Improved revenue per customer through intelligent cross-sell and upsell
- Improved customer retention through frictionless experience
- Improved customer acquisition through relevant targeting
These approaches are so effective because customers today want to feel both understood and valued. Nearly 80 percent of U.S. consumers say they expect brands to show they “understand and care about me” before those consumers will consider making a purchase, according to the “Wantedness” study by marketing agency Wunderman. Additionally, 56 percent of consumers polled in that study say they’re more loyal to brands that “get me” as a segment of one; in other words, showing a deep understanding of customers’ preferences, needs, wants, and past purchases.
Delivering on those expectations is a tall order for most marketing and martech professionals. Despite the advantages of using CX as a competitive differentiator today, few marketers have the ability to deliver truly personalized, relevant experiences consistently across all channels. The reasons? What marketers know about customers is limited, and marketers are unable to keep up the cadence of today’s always-on consumer.
That’s where a CDP comes in. A CDPconnects data from across a company in real time to enable an always-on, always-processing holistic view of the customer. Marketers can use that unified and complete golden record to deliver unique and highly relevant experiences.
Marketing professionals who use a CDP have easier access to more robust, timely data than their peers who don’t use one. Which means that they’re more likely than those peers to deliver a customer experience that improves revenue per customer, as well as increases retention and acquisition rates.
Using a CDP, you can perform advanced predictive modeling and enhance your customer data with information such as propensity scores to help determine customer intent and anticipate needs and preferences. This enables tactics that are individualized to each customer, such as next-best actions and dynamic customer journeys; for example, by presenting personalized, relevant communications and recommendations for the content and offers most likely to convert. Because the data is available in real time, marketers can now react at the speed of the customer — essential for delivering the experiences customers expect today. According to Boston Consulting Group, “brands that create personalized experiences by integrating advanced digital technologies and proprietary data for customers” see revenue increase by 6 to 10 percent, at a rate of two to three times faster than those that don’t.
The Impact of Transforming the Customer Experience
The business results of using a CDP to support your CX efforts are numerous and invaluable. They include increasing upsell and cross-sell, wallet share, average order dollar value, purchase frequency, and customer lifetime value because interactions and communications are contextually relevant and personalized. And, personalization can deliver five to eight times the ROI on marketing spend and can lift sales by 10 percent or more, according to Harvard Business Review.
Now is the time to start using a CDP to build the customer engagement that marketers covet — because not only will it deliver game-changing ROI, it also could provide more of a competitive advantage than you think. Although 43 percent of organizations claim to have a single view of the customer, a mere 12 percent say they’ve implemented the required technology to achieve that holistic view, according to Econsultancy. Further, 64 percent of respondents to the Dimension Data study say they have no big data capability that combines data from across channels.
Marketers who use a CDP can bring data together from across their organization to know all that is knowable about their customers. In doing so they have a distinct advantage. Companies considered leaders in data-driven marketing are about six times more likely than laggards to achieve a competitive advantage in increasing profitability and five times more likely to do so in customer retention, according to a study by Forbes Insights and Turn. The reason: The customer experiences these leaders deliver by using data-driven marketing are competitively differentiated. And there’s no greater advantage than using the robust, continually up-to-date customer data that a CDP provides, delivering a deep understanding that informs the interactions, messages and offers that comprise your CX.
There are four primary ways marketers and martech professionals can use a CDP to drive immediate and long-term business value. One is revenue lift. The other three are:
- Innovation – Increased market value through breakthrough innovations
- Operational effectiveness and efficiency – Decreased costs through improved operations and customer understanding
- Risk avoidance – Decreased risk through a holistic view of data
You can read more about innovation impact in “Increase Market Value Through Customer-Led Innovation.” We’ll cover the other two topics in upcoming posts.
November 27, 2018
Not All VIPs Are Created Equal: Get to Know Your Most Valuable Customers Better
By Chen Ofir, Optimove
Investing time and money on your VIPs is never a wrong tactic, but you can make it even more personalized, and profitable. A new experiment by Optimove tells the tale of the Resellers and the Enthusiasts.
When exploring e-commerce client data, we often run into different active customer personas from which we might benefit.
The most common separation is between standard active customers and VIPs. As the Pareto Principle states, roughly 80% of the effects come from 20% of the causes. When applying this principle to the eCommerce industry, we see the same effect time after time: 80% of the revenue is generated by 20% of the customers.
Recently, during an interesting chat with one of our clients, we discussed the possibility of going deeper into our VIP breakdown: what if these VIPs could be split into many diverse groups?
In this case, the separation into having Resellers and Enthusiasts made a lot of sense, but not every e-commerce business is built in a way that supports the existence of different kinds of VIPs, and the terms, of course, may vary between one brand and another.
In our case, and after exploring dozens of e-commerce brands, we came up with a unique separation – to Resellers and Enthusiasts with the following approach:
What is the difference between an Enthusiast and a Reseller?
The Reseller is a pretty common definition in the e-commerce industry. It refers to customers who make high volume purchases of cheaper items, in order to make a profit when selling these items to their own clients for a marked-up price.
The Enthusiast is a client that loves your product and is willing to pay a high price for it. The definition of the Enthusiast may vary among different brands, for example: a brand that sells art might call them ‘Collectors', whereas a photo equipment brand might call them ‘Professionals’, etc.
Both Reseller and Enthusiast have different behavior from the standard Active customer, but there are no similarities between the two:
How can we distinguish Enthusiasts from Resellers?
When trying to identify customers belonging to these two persona types, there are two attributes for each that we should look at. In this case, we used cluster analysis to identify these customers.
Resellers: The two main attributes that we should take into consideration when we try to identify Resellers:
- Number of Orders
- Number of Items Per Order
The combination of these two may help us detect a reseller early on in their journey as a customer. It might be enough to see that a customer ordered a huge number of items in their first order to flag them as a Reseller. But, if the customer ordered many items, although not a significantly high number of times, it might take a few orders to identify their Reseller pattern.
Enthusiasts: The attributes that correlate with Enthusiast’s behavior:
- Average Item Price
- Max Item Price
An Enthusiast might not buy as frequently as a Reseller, but their purchases are very valuable. In this case, the attributes above might do the job perfectly. Please note that your brand’s SKU has a crucial impact on the existence of Enthusiasts, in the case that your catalog’s range of prices is short, you shouldn’t expect to find Enthusiasts within your customers.
The example below shows how a simple cluster analysis discovers Enthusiasts based on Max Item Price and Average Item Price:
Each dot in the plot represents a customer. The X-axis represents the maximum item price, while the Y-axis represents the average item price. In this plot, we created five clusters based on these attributes. Our Enthusiasts, those who have high Max Item Price and high Average Item price, are the dots within the purple area on the right.
That being said, the CRM professionals might have insights that the data can’t detect, such as personal relations. If a client is known as either a Reseller or Enthusiast, you should always flag them manually, in addition to using the cluster analysis.
So, we can define two types of VIPs, how can we benefit from it?
We know that Resellers and Enthusiasts have different behavior, and we know how to detect each. The next step is to use this knowledge to target our VIPs better and boost their value.
When developing marketing and campaign strategies, we should take into consideration the type of VIPs we might encounter. For example, contacting an Enthusiast with a new line of luxury items makes perfect sense, but the same interaction will go straight to the Reseller’s Junk folder. On the other hand, when trying to push a surplus product at a low price, we might want to reach the Resellers, rather than the Enthusiasts.
Another crucial aspect is the churn indication. As Resellers buy much more frequently than Enthusiasts, we should consider raising the flag of possible churn much earlier. The ordering activity varies between different brands among the e-commerce industry, but the pattern is clear, as stated above.
In the chart below, we can see the frequency distribution of Resellers and Enthusiasts belonging to one of our clients. In this example, we might consider a Reseller as churned after 90 days of inactivity, as only a small fraction (16%) of the Resellers’ frequency exceeded this period. On the other hand, for an Enthusiast, this is a very short period of inactivity and should not indicate churn.
In conclusion, defining different personas within your customer database may be very valuable for your marketing strategy. Look into your stock, asses your products and think about your clients; who they are, what they do and how they achieve their goals, to help you segment them better. In our example here, understanding the different behavior patterns of a company’s VIP segment allows the brand to communicate with its customers and offer them the best offers, with the most suitable content, at the best timing.
November 20, 2018
Art of the Possible – The Role Data Can Play in Unlocking Marketing Creativity
By Aaron Brennan, RedPoint Global
According to Astro Teller, head of moonshots for Google X, “It is a lot easier to hit a 10x number improvement than a 10% improvement.” The reason for this is when you set your sights on small improvements you tend to look for incremental ways to improve rather than big ideas to reach toward the stars.
The concept can easily apply to marketing teams that adopt a 10x strategy to reach major growth objectives, and the rise of NoSQL and document databases helps marketers to deliver on that strategy.
In traditional marketing campaigns the time and effort required to access data and easily query it has been a major obstacle to success and has limited marketing creativity. The need for a data scientist or an IT manager to assist has also added to the burden.
With NoSQL databases, marketers have a new way to easily access data and unlock marketing creativity to get a multiplicative level of improvement instead of incremental improvement. NoSQL document databases offer more flexibility in accessing all data types, cost savings by reducing the overhead and expense of data wrangling, and allow marketers to scale and grow their efforts more quickly and easily.
Following are a few ideas of how marketers can now utilize customer data to uncover the art of the possible:
- A picture says a thousand words: We all know that messaging isn’t the only reason people click or engage with marketing campaigns. Often there are beautiful images or videos along with the campaigns, but it is hard to measure the impact of images on customer engagement. With a NoSQL document database, image content becomes searchable by engagement: who bought what, when, where and with that you can pull together a timeline of highly effective customer images. This allows for smarter and more contextual retargeting strategies in digital and traditional channels. With this capability you always can bring forward each customer’s favorite image to help boost conversions and drive revenue.
- Turn your frown upside down: A bad customer experience doesn’t have to be the end of the customer journey. What if it was possible for you to run a marketing retention campaign more quickly and efficiently for people who returned items and had a bad experience? The ability to access product information in a timely manner from a NoSQL document database means that your marketing team can easily find and target all the people that have faced certain challenges with a product or didn’t connect with the messaging. You could also re-engage with customers who have return items or stopped services.
- Data defines you: Data is one of the most powerful assets for today’s business and the ability to derive insights from customer data is what differentiates market offerings. Metadata has long been important for indexing and ranking, but there is marketing potential in metadata as well. With NoSQL document databases, you can simplify and reduce the time it takes to create audiences based on buying trends and metadata tags associated with them versus segmentation based on traditional age, gender, and affiliation demographics. Campaign development on similar metatags would be a simple process and could have potential to reach segments with much higher engagement and conversion rates.
These are just a few examples of what is possible with the right tools. By enabling marketers with a new level of connectivity and access to data you unlock the potential of the data. Furthermore, with a NoSQL document database such as MongoDB or Cosmos DB, all this information lives in one place. Then the question becomes more about how you encourage your team to embrace the art of the possible and think more creatively around the data and the customer journey.
November 14, 2018
From Customer Data to ROI: How AI Unlocks Cross-Channel Success
By Marina Ben-Zvi, Blueshift
Data and AI are today’s power couple. While customer data holds a wealth of insight, marketers have reached a point where their ability to capture data has exceeded their ability to take action on it. But coupled with AI, marketers have a way to make sense of the volume, velocity and variety of their data and use it to adapt marketing actions in real-time to drive relevance and impact across the customer journey.
Yet, there is a lot of confusion around AI because even though it’s been hyped with promises of breakthrough transformations, it’s remained largely black box. As a result, marketers are questioning AI’s credibility and are unsure where AI belongs in their marketing strategies.
AI: Unlocking ROI from Customer Data
To separate the hype from the real ROI, Blueshift set out to quantify the impact brands implementing AI-powered campaigns have realized in customer engagement, conversion and revenue. We analyzed 3.8B marketing interactions across channels and verticals, including e-commerce, media, consumer finance, and travel. Results of the benchmark study, published in the report, ROI of AI Marketing: 4 Levers for Cross-Channel Success, show that AI-powered marketing campaigns achieve:
- 3X revenue relative to their use in the marketing mix
- 3.1X-7.2X lift in customer engagement
- Nearly 2X greater impact on engagement for mobile push compared to email
- Continuous improvement in campaign performance as the AI engine learns from customer interactions, driving an additional 50% lift over initial results
Brands across industries are experiencing performance gains from incorporating AI into their marketing campaigns. For example, LendingTree saw a 35% lift in revenue per email by sending “Savings Alerts” that are personalized to each user based on their credit history and optimizing its send times for engagement. Tradera saw gross sales per session increase by 125% when deploying AI-recommended content.
Optimizing the Customer Experience with AI
People today want relevancy and expect immediacy, but marketers face challenges delivering that experience in today’s world of always-on consumers, channel proliferation, and fleeting consumer attention spans. Marketers need to act fast to deliver tailored, timely, cross-channel experiences and that requires a tool that scales insights, decision making and cross-channel automation. AI is that tool. AI is also the ingredient that advances Customer Data Platforms from a system that unifies and distributes customer data to one that truly activates that data by using it for real-time decisioning and dynamically orchestrating customer engagement.
At its core, AI helps marketers be smarter and faster about how they engage customers along the customer journey by optimizing the “Who, What, When & Where” of marketing:
- “The Who” with Predictive Audiences: Select the customers who should be targeted for a marketing campaign.
- “The What” with Predictive Recommendations: Determine the right piece of content, offer or product to show each unique customer.
- “The When” with Predictive Engage Time: Tailor campaign delivery to the times when each individual customer is most likely to engage.
- “The Where” with Predictive Channel-of-Choice: Deliver campaigns on each individual customer’s channel-of-choice.
Activate Customer Data with AI
Data can be a powerful competitive advantage when harnessed effectively. Companies that are able to successfully unify their customer data and translate it into improved customer experiences will be the winners in today’s dynamic landscape. But realizing data’s potential requires a tool built for its increasing volume, speed and complexity. Realizing data’s potential requires AI.
For the full set of findings of AI’s impact on the core components of marketing and real customer examples of revenue gains achieved by implementing AI-powered marketing, read Blueshift’s report The ROI of AI in Marketing: 4 Levers for Cross-Channel Success.
November 9, 2018
Why Every Marketing Organization Benefits From Having Their Data in Order
By Albert McKeon, Zylotech
You're probably tripping over an abundance of marketing data: behavioral, historical, demographic, lead generation, social media. But at any moment, can you harness all of that information into an accessible, easy-to-discern stream? If not, you're just like any other marketer who won't realize your ROI.
After acknowledgement, action is the next step to getting your data in order. So here are four strategies you should consider if you want to realize the full potential of your data.
Knock down all silos
Many marketers face a seemingly insurmountable hurdle: organizational silos. But it's not insurmountable. All you need is a golden hammer, basically the promise to turn data into gold for the entire organization. That should tear down the walls.
Make data unity a constant goal by establishing a company-wide data management strategy. Charge leaders from each department to ensure that all information is shared. Product development, sales, shipping and just about every department matters in your pursuit to market effectively. Know what's going on across the company.
Clean up dirty data
Having dirty data might be even more frustrating than having siloed information. After all, disjointed data is at your fingertips but it's far from perfect to be used effectively. This is where an organization without silos has a huge head start.
When departments are aligned on the value of sharing data, they're also of the same mind on the value of the data itself. Have those data representatives occasionally meet to prioritize data. This will help departments decide which data needs to be made whole and which data can simply be discarded. Thankfully, data analytics tools help departments easily collaborate and make joint decisions on data.
Interpret the data
Once you decide which data is pertinent to operations, it can't just sit there waiting for the sunny day of exploration. Your organization needs to consistently interpret data to find patterns and discern its meanings. You'll want to know if preliminary findings can lead you down unexpected paths, or whether it's necessary to redefine the parameters of a data-driven project.
Not every organization can afford to employ a team of data analysts. Fortunately, data analytics technologies do the heavy lifting of learning. Actually, some platforms today are self-learning. Here, the analytics platform learns on its own, finding patterns and anomalies without first having to be instructed what to look for. This leads to unimaginable and exciting possibilities because human programmers don't always think of everything.
Choose the right technology
Speaking of technology, it's critical to have the right technology that will collect, coalesce, clean and analyze your data. Your marketing team should choose a data analytics platform that will identify, unify and enrich all of your relevant data points. You'll know it's the proper platform when you see that it's thoroughly aligning clean data with your marketing strategies. The ROI will be obvious.
November 2, 2018
Four Ways AI and Machine Learning Enhance the Customer Experience
By Chuck Leddy, Zylotech
While everyone has heard of artificial intelligence and machine learning, not everyone understands how these technologies work and how they support successful marketing. In this blog post, we’ll demystify these technologies and outline four important ways they can help you better know your customers and enhance revenues/ROI.
AI and ML, demystified
The type of artificial intelligence now commonly in use (called “narrow” AI) is a computer program humans teach to do one particular task, like identifying spam email, monitoring customer behaviors, or routing drivers through rush-hour traffic. AI is built upon data: you take data about the past, analyze it, and make predictions about the future.
AI is most effectively used in situations where historical patterns uncovered in the data are predictive of future actions. Machine learning/ML is a field within AI that uses statistical techniques to give computer systems the ability to "learn,” to progressively improve performance on a specific task, without the need for explicit programming (the data itself does the “programming”).
4 ways AI and ML are changing the marketing game
Here are four ways that AI and ML can help your marketing efforts. They are in no way exhaustive but are intended to offer you an idea of what’s possible with this emerging martech:
1. AI can create highly-personalized customer experiences, the kind your customers increasingly demand. As you collect data about what your customers do (and where they go), AI allows you to understand the behavioral factors that drive your customers’ behaviors. With this “actionable intelligence,” you can then send just the right message to the right customer at the right time, leveraging AI. In the same way that Pandora uses machine learning to suggest songs to listeners (based on prior listens), you can use marketing technology to send the right content to the right customer at the right time.
2. AI can better engage customers. No marketing team has enough members to engage all your customers all the time, but AI and machine learning can be used for customer segmentation, allowing you to send them more personalized messaging resolving their particular issues. Through combining “human + tech,” you can have chatbots perform simpler levels of engagement and then escalate more complex engagement tasks to your human marketers, optimizing efficiency and ROI.
3. Predictive analytics to promote customer retention. Every marketer knows that focusing on turning customers into repeat buyers can positively impact your bottom line. AI and ML can be used to identify patterns among those customers who are most likely to “leak out” of your funnel before they convert. By understanding why they leave, you can take proactive/anticipatory action to keep them within your funnel until ROI and beyond. This is what “retention marketing” is all about. When you keep your customers, you also grow your business.
4. Actionable insights for better performance. In the past, marketers were often blind to the customer’s journey. When might a customer purchase, and why then? These key questions matter for speculation and “gut” thinking. Now, we have data about every step of the customer journey. We can leverage that data to understand our customers and develop actionable insights that inform what we do. Data, the “oil” fueling AI and ML, simply gives us more visibility into the entire customer experience. In the end, AI allows us to market at scales not humanly possible before.
October 26, 2018
Let the Numbers Persuade You: The Success of Multi-Channel Campaigns is Indisputable
By Jonathan Inbar, Optimove
Are you still hesitant about investing more time and money upgrading your marketing plan with multi-channel campaigns? Let this research by Optimove show you exactly how more effective they can be.
In today’s digital-focused world, multi-channel marketing is important for the simple reason that you must be able to reach your customers where they are.
The number of devices per user is growing and it is important to have an orchestrated strategy of offers sent through several channels, so you won’t miss any potential touchpoints.
The following research comes to show exactly how effective multi-channel campaigns are compared to single channel. For this research, we looked at over 20 gaming and e-commerce brands from around the world and measured the percentage of multi and single-channel type campaigns with statistically significant results. Then we tested the best combination of email, SMS and push notifications, and which single channel leads to a higher percentage of campaigns with significant results.
Go Multi-Channel Already
Before we get to the part of measuring the percentage of significant results, we first examined the role of multi-channel campaigns in the entire marketing plan.
On average, the percentage of multi-channel campaigns represents only 15% of the entire marketing plan, a surprisingly low number given the fact that these multi-channel campaigns yield more significant results. In terms of the most common combination – the email & push combination won with 60% of the multi-channel campaigns observed in the research:
Significant Enough for You?
Next, we looked at the difference between multi-channel and single channel in terms of the percentage of campaigns with statistically significant results. It’s important to note that determining whether the campaign is statistically significant or not indicates how likely it is that the marketing action was responsible for its recipients’ behavior. The numbers speak for themselves: On average, 24% of all multi-channel campaigns ended with statistically significant results, while only 12% of the single channel campaigns ended with significant results:
We also looked at specific channels for single and multi-channel campaigns. We checked different combinations for multi-channel campaigns and saw which ones have the highest percentage of campaigns with significant results.
While none of the single channel campaigns passed the 15% mark, we can see an obvious leader from the multi-channel combinations chart. A third of all campaigns sent through email and SMS resulted in significant results:
When checking the biggest difference, comparing email only to email and SMS, we see that the addition of the SMS channel increased the percentage of campaigns with significant results by 250%.
What Should You Do?
Here are three key components to succeeding at multi-channel marketing:
1. Create and maintain a single customer view: Customers often interact with your brand in a variety of ways that involve more than one touch point. Create a single customer view with a centralized marketing data hub that consolidates your customer data in one place. This data must be dynamic and constantly refreshed and updated.
2. Launch a multi-channel marketing platform: Multi-channel campaigns involve synchronizing your messaging across different channels. By establishing a multi-channel marketing platform, you will simplify the creation and execution of cross-channel campaigns. Here are the capabilities you’re looking for:
- Campaign management, including capabilities for segmentation, workflow creation and campaign execution
- Advanced analytics, including predictive analytics and campaign optimization
- Advanced execution, including capabilities for event triggering, real-time messaging and full multi-channel support
3. Generate consistent customer experiences across all channels: The quality of the customer experience is greatly influenced by its consistency. By managing campaigns across multiple channels, you’re creating a presence for your brand in your customers’ life. If you treat each channel separately, you will fail to deliver consistency and damage the customer experience. Gone are the marketing days when a single message through a single channel sufficed to reach your complete customer base.
Conclusion
This research again shows why you should invest resources into multi-channel campaigns, either by making an integration with a certain type of channel or by creating the right strategy that will send the right content through all the channels together. The benefits of applying multi-channel strategy are great and could help substantially increase campaign uplifts.
Advancements in data and technology these days will help you achieve this goal quite easily, and there’s little choice but to join the new game. Luckily, tools like Optimove are designed particularly to help you win. Your customer base is out there, waiting for you. The right technological platforms will make your brand’s voice louder and much clearer.
October 23, 2018
Five Success Factors for Implementation of DMPs and CDPs
By Rutger Katz, EY VODW
Data Management Platforms (DMP) and Customer Data Platforms (CDP) are both technologies that have the potential to uproot the internal organization due to their cross-departmental function. Both technologies enable a link between online and offline customer information and allow marketeers to track, automate and orchestrate large parts of a customer journey. This requires that online (e.g. social media) teams have to collaborate with offline (e.g. CRM) teams. Mature companies would have cross-departmental and multidisciplinary teams that focus on key aspects of the customer journey such as sales funnel conversion optimization. Unfortunately, there are still many organizations either in their digital transformation or have not started it yet to such an extent that it can sustain such teams. Because the DMPs and CDPs are such disruptors of the organization, companies should start a transformation program to facilitate the transition that enables cross departmental and multidisciplinary teams. An important starting point is to create a strategic vision of initial focus areas and ultimately a total transformation goal for the entire company. To assist companies in establishing a strategy and adoption of these technologies we wanted to create a maturity model which could also be used as a benchmark in a company’s growth towards being a CDP/DMP enabled organization.
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A pinch of old with a dash of new
To structure this maturity model we used the model of M. Lippit from 1987 for successful business change. M. Lippit’s model was focused purely on project management for the successful implementation using five success factors. However, when focusing on the maturity of an organization we should also look at the maturity of each of those success factors. It is through the combination of M. Lippit’s success factors, including a slight adjustment, with our proprietary maturity levels, that we could create a maturity model that fits extremely well with the issues we experienced in companies struggling with gaining value out of their CDP/DMP.
The maturity model consists of three maturity levels, Explorers, Adopters and Leaders, each with their own goals over each of the five success factors of M. Lippit’s model: 1. Vision & Strategy | 2. Organization | 3. Roadmap | 4. Resources & Skills | 5. Processes.
Getting a running start and growing from there
Organizations that are exploring or have just acquired a DMP or CDP would do well to start with creating a Vision & Strategy and establish a core team (including all the necessary roles) if they are not already available. Make use of the platform suppliers or external expert consultants to assist in establishing these goals as they are of high importance for a running start. The next priorities are ensuring that the initial roadmap is impactful and is aligned with IT should their assistance be required for any integrations. Again the supplier should be able to provide a shortlist of easy to implement use cases and customize them to your business. At this point the business and IT management agreements come around in order to facilitate the core team to be able to work on the platform. The core team should establish a delivery process (e.g. Scrum) and pick a selection of productivity tools (e.g. Jira) which works well for them and the organization. Now the organization should have its Explorer goals reached and can start creating value out of the platform while the entire organization learns what it is to work with a DMP/CDP.
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In our maturity model the entire organization grows along while each goal has been accomplished. The achievements of earlier goals, when communicated widely enough in the organization, will release a ripple effect through the departments and colleagues to facilitate the changes in behavior and thought that make it easier to complete higher level goals. For example, it will be difficult creating a set of aligned CDP/DMP related KPI’s (a Leader goal) in most organizations without first getting agreements with the managers (an Explorer goal) and establishing a steering committee which influences priorities for each business unit’s resource (an Adopters goal). The managers first need time to see the value of the platform and realize that in order to motivate colleagues from different departments to collaborate on a CDP/DMP, they should also have KPI’s that focus on that cross departmental work.
As companies grow along their maturity they will realize that the earlier level goals have been the stepping stones that to pave the way for the higher level goals. The time required to progress from an organization without any of the goals to a fully mature DMP/CDP enabled organization differs per organization and situation, but on average would be 2-3 years. Using two real life examples we will demonstrate the worth of using our maturity model in overcoming roadblocks related to the absence of achieving success factor goals and that the sequence of the goals is of importance.
Case 1: Creating your internal experts and processes
The first case was at a FMCG organization that implemented a DMP one year ago and was working on their vision of a global rollout of the product (Explorers goal in Vision, working on Adopters goal). They had made management agreements (Explorers goal in Organization), a shortlist of easy to implement use cases that could be done in each country (Explorers goal in Roadmap) and a core-team which worked on the DMP part-time (Explorer goal in Skills & Resources) without any real structure in their approach (no Explorers goal in Processes). However, due to a lack of skillsets and knowledge abroad they hit a wall trying to establish the DMP in new markets. The local team and agencies did not know where to start and the global team had just enough knowledge on how the tool works, but not enough maturity to guide other local teams.
We decided to train the global team to be experts in the tool and the guiding light for the rest of the organization. After a large set of in-depth training days to create our center of excellence (Adopters goal in Skills & Resource) we went to work with the global team to establish standard and structured delivery processes explaining the entire process (Explorers goal in Processes) of a use case from idea to measuring it’s success (Adopters goal in Processes). Furthermore we created a standard country rollout plan that they could use for each new country that would be onboarded on the platform. From then on each country could use the delivery process to get a running start and contact the global expert team to resolve questions and issues.
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Case 2: Too fast for your own good
The second case was at a large car importer for multiple brands that had a very strong strategy of where the company should go in terms of customer experience the next few years (Explorers and Adopters goal in Vision & Strategy), complete with a high level roadmap of the next three years (Adopters goal in Roadmap). The company also had a core team with full time members that was able to establish use cases (Explorers goal in Skills & Resources, advancing into Adopters goal). Furthermore, they had some agreements with the different car brands to work on the DMP platform and was implementing KPI’s for every department to spend at least a few hours a week on the DMP after convincing senior management that this was the way to go (Explorers and Leaders goal in Organization).
However, most colleagues and managers from other departments had no idea what they should do to achieve their DMP KPI’s or even what a DMP was. The KPI’s were only vaguely formulated as a 10% time spent on DMP activities requirement. Furthermore, the use cases that they executed were small in impact due to a lack of certain technical integrations (e.g. CRM integration with a weekly change upload) and not prioritizing higher impact use cases. And because the impact of these use cases weren’t communicated, the brands had no way of knowing how the DMP platform could influence their core business.
In short, the company had already gone ahead and reached some higher level goals on some success factors while ignoring intermediate and beginning steps on others. To reground the organization we first took care of all the basics, getting the technical integrations in order which were crucial for some easy high impact use cases. We created a short term roadmap with technical integrations and use cases that build on them (Explorers goal in Roadmap). We also started to include the impact measuring of use cases and sharing of successes as a step in the standard process in order to spread the word of what a DMP can do to inform other departments of our capabilities (Explorers and Adopters in Processes).
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Not a one trick pony!
Organizations should not underestimate the amount of coordination and effort it takes to successfully implement a DMP/CDP. Unfortunately, it also takes time. Colleagues and managers from different departments have to experience the success of the platform before they are convinced to want to try it themselves, before committing fully. This is exactly why it is important to follow the sequence of the maturity model, to let the earlier level goals pave the road in order to achieve the next ones effectively. Using our maturity model, organizations can either prepare for an implementation by strategizing when to accomplish which goal or use it to further their implementation using our achievable goals in order to increase the impact the DMP/CDP has on their daily business.
The model has been made specific to DMP/CDP implementations, but it can also be adapted to different technology and platform implementations into organizations. I have used it for data science maturity within organizations and plan to use it for Robotic Process Automation. If you are struggling with such an implementation yourself and you could use my insights or clarification on the model, feel free to reach out to me through Linkedin. I would also love to hear your thoughts and experiences in the comments below.
October 19, 2018
Strictly Private: The ABCs of CDP and ePR
By Susan Raab, CDP Institute
 Last May, when Europe’s GDPR law went into effect, it was clear the rules of customer engagement would change significantly for consumers, and for companies both in the EU and for those outside whose customer engagement included sales within Europe. But GDPR, which obligates companies to protect the privacy of personal data and adhere to consumer preferences for engagement, was not intended to be the whole story on ensuring consumer privacy. A second, even larger regulation, ePrivacy, was planned to become law along with GDPR, but has been delayed in a prolonged editing process and it currently exists as the EU’s ePrivacy Directive.
This is due to change, and while a firm date has not been set for ePrivacy Regulation to become law, many believe it will do so before the end of 2020. According to Matthias de Bruyne, legal counsel for the Data Driven Marketing Association (DDMA) in Amsterdam, “the legislation currently sits with the Council of the European Commission and there’s a lot of pressure right now from those who have been working on it for the Council to reach an agreement by December, so it can go into trilogue with the European Commission and European Parliament and finalize the law by April before the European Parliament’s elections.”
As with GDPR, companies will be given a grace period to comply with ePrivacy regulation, but will need to prepare well ahead to ensure they are in compliance on the allotted time schedule. “Right now,” de Bruyne explains, “the ePrivacy Directive allows a lot of interpretation and countries have their own implementation of the ePrivacy rules – for example Germany and the Netherlands are very strict, whereas other countries such as Ireland, are much less so. As a result an organization can currently look for the minimum standard in each member state as opposed to having to adhere to an EU-wide standard. After the ePrivacy Regulation enters into force, the differences will be much smaller, so there will be less to choose.”
ePrivacy, which has been called the “Cookie Law,” will extend beyond regulating the direct relationship companies have with their customers by regulating further how all EU consumers can be marketed to, including via advertising, telemarketing, email, and social media.
Compliance with this will mean ensuring that companies must have a clear picture of the state of their customer data, ways to ensure their data is consistent and up to date, and an ongoing knowledge of consumer preferences as they evolve. Customer Data Platforms are well positioned to take a lead helping to ensure that is possible.
According to MarTech Advisor, “there are two laws because they are derived from two different rights in the European Charter of Human Rights….The GDPR covers the right to protection of personal data, while the ePrivacy Regulation encompasses a person’s right to a private life, including confidentiality.” IAB Europe, in its FAQ on ePrivacy states the regulation would “introduce rules allowing users to set general privacy preferences in their browsers and other software, which would be binding on and enforceable against any other person.” Additionally, as Pravin Kothari, CEO of CipherCloud states in CMSWire, “the regulation is expected to include specific language ‘with respect to the confidentiality of communications data such that listening, observing or monitoring a user specifically on a website is prohibited.’”
According to CMSWire, experts advise companies to, “take inventory of your current data, work with the teams that have the best insight on data infrastructure and finally adopt a data-privacy program that adheres to the most strict laws…. Andrew Frank, vice president distinguished analyst for Gartner for Marketing Leaders, said in the same article, “Trying to keep up with different privacy laws on a case-by-case basis will be a nightmare to try to stay on top of for marketers. It becomes much more costly and risky to try to maintain a hodgepodge of separate privacy policies rather than have one global policy that works everywhere.”
The CDP Institute recognizes this a very important and evolving area, so welcomes input from readers, and we will continue to explore ePrivacy and GDPR topics as they relate to CDP technology and use.
October 12, 2018
Three Reasons Marketers Should Be Enthusiastic About Machine Learning
By Janet Wagner, Zylotech
Most marketers today are aware of machine learning (ML) and how many companies are using it for a wide variety of use cases- from automating business processes to creating highly personalized applications. Some marketers may be concerned that machine learning will leave them without jobs. However, the core tasks of marketing involve creativity- an attribute machine learning does not have. Machine learning can’t take over every marketing task, but it could be used to assist marketers in a wide variety of ways.
Automate repetitive marketing tasks
According to a recent Workfront survey, U.S. marketers spend 38% each workday on primary job duties. Marketers are spending 62% of their time on tasks many of which are repetitive, monotonous, and aren’t directly related to marketing at all. Emails and wasteful meetings are among the top three things the marketers surveyed said decreased their productivity.
Thousands of marketing technology solutions are available today, and most of them leverage machine learning. With the help of ML-based solutions, companies can automate and streamline a variety of marketing workflows- emails, marketing campaigns, data and documents, and analytics to name a few.
Boost marketing productivity
Machine learning allows repetitive marketing tasks to be completed at a speed and accuracy that humans can’t achieve. When repetitive marketing tasks are automated, marketers can spend more time on brainstorming marketing campaign ideas. They can spend more time on building strategies aimed at finding, attracting, and retaining customers. Marketers can also spend less time on testing their ideas. Some testing solutions leverage machine learning and AI so that marketers can try out all their ideas all at once instead of only a few at a time.
Automating repetitive tasks leaves marketers more time to spend on the core marketing tasks they were hired to do.
Enable self-learning for analytics
Analytics is a crucial part of marketing. Analytics helps marketing teams find potential customers, understand customer purchasing patterns, and even predict and prevent churn. Analytics involves many processes including collecting and preparing data, building and training ML models, and creating visualizations and dashboards.
Machine learning along with artificial intelligence (AI) enables platforms to be self-learning and allows many of the processes needed for analytics to be automated. For example, the Zylotech platform features sophisticated self-learning algorithms for embedded analytics. Our platform is also designed for the whole automation of customer analytics. It features built-in data analytics tools, including predictive analytics, for creating highly personalized 1:1 customer experiences.
Machine learning can help marketers
Automating repetitive marketing tasks, boosting marketing productivity, and enabling self-learning for analytics are just a few of the ways machine learning can help marketers. Thanks to machine learning, many companies are including in their digital marketing strategies chatbots to engage customers, highly personalized loyalty programs, predictive analytics, and so much more.
Marketers should be enthusiastic about machine learning because it can enable marketing innovation, boost creativity, and increase marketing campaign successes.
October 9, 2018
5 Top Quality Tips to Transcend Your Welcome Email Stream
By Shefa Weinstein, Optimove
As I continue with my “Top 5 Tips For…” articles, I thought, what better topic to kick off the series than the Welcome Stream. When I began building my first Welcome Stream years ago, I first had to familiarize myself with the concept of the email journey and how to tell a story in just a few emails. Today, the streams are much more complex and may include conditional sends, real time data, and high-level personalization. But we are nowhere near the end of the innovation train – there is so much more we can do to grab the users’ attention and turn them into brand ambassadors. Here are 5 tips for stepping up your Welcome Stream game.
1. Start with a cohesive experience:
Congrats! You’ve convinced the user to sign up for your newsletter. You’ve shown them that you have something to offer that they want. As soon as the user provides their email address, you begin your journey towards building a trusting relationship with that customer. Users will subscribe to your newsletter from various avenues and for various reasons, and it is your job to give them more of what initially attracted them. Using dynamic content, scrape the main banner from your website and add it to your email, showcasing the same experience that attracted your customers to begin with. This has two great outcomes: 1. You are building a cohesive experience from your site to your emails. 2. This keeps your emails fresher longer. The scrape will continuously update itself and by using this method, you can buy time before you redo the stream.
2. Make sure your stream has offshoots:
If you are already doing conditional streams, you rock! If not, we are here to help you rock it. You want to think about the different journeys your users have embarked on, and where you want them to go. For example, if they signed up for your newsletter in-store, you should consider sharing more store sales. If they signed up on your site, show them online offers. Be mindful of your goals; at times your main objective might be to get them to download your app instead of continuing to shop online – make sure your stream directs them to your chosen destination. You can also add different flow offshoots to your stream. If they complete a purchase, they might receive a different sequence than users who haven’t. If they haven’t completed another purchase in X days, you might want to send only these users a coupon, instead of sending offers to all customers.
3. Get more data:
Everyone knows that knowledge is power. The more data, and therefore, knowledge you have, the stronger the connection between you and your users. The Welcome Stream is a great time to dedicate an email to customers who have not filled out their profile – this gives you an opportunity to get this information while developing your relationship with your customers. There are two ways you can collect data from emails: actively and passively.
Actively collecting data involves asking questions in your emails. Here, think carefully about the two most important profile questions you have – make them simple with multiple choice answers. Asking your user to fill in the text will result in a lower response rate and it’s harder to compile.
Passively collecting data is a bit sneakier. You might be tracking clicks to gain knowledge of what intrigues your users, or you might grab their location over time to understand their patterns and drive customers to the closest store. But remember, always be super careful of your users’ privacy, avoid violating GDPR, PII, and all those other scary, yet very important acronyms.
4. Get them to the store:
If you have a brick-and-mortar location, you obviously want to get as many people as possible in the door. Our suggestion? Geo-target them. As mentioned above, the Welcome Stream is your first opportunity to begin building that relationship and teach them about your brand. This is also your chance to reveal your closest location– and take it to the next level by adding more useful data for your users, like opening hours, local events, and news that might be attractive to them.
5. Entice them:
I am a big fan of Nir Eyal – the father of the hook theory. In short, his theory posits that for users to keep sticking with the brand, you want to create habits for them. You should create the possibility for them to make small investments and then reward their efforts, so that a habit is formed in how they interact with the company. It is vital that during the welcome process, you get users to make another purchase and habitually start using your brand. Remember, you are not looking to maximize the order value – you just want them to take out their credit card again. We suggest starting off light, reminding them of the cool items you offer, maybe showing a few sale items. Then, you can start adding a coupon or a discount. As you continue with the stream, you want to keep messaging them until either 1. it’s been too long and it’s time to give them a communication break, or 2. they make another purchase. Once they complete the transaction, you want to keep rewarding customers with further coupons and discounts, showing them the value of their loyalty to ensure they get to that next purchase. Get them into the cycle of thinking about your brand whenever they want to buy what you sell.
In Conclusion: Before you start building (or revamping) your stream, you should lay out your KPIs very clearly. Make sure your message is focused on getting those numbers higher. While it is important to say welcome, remember that the purpose of the Welcome Stream is to begin fostering trust with your users, and to initiate the journey toward a long-lasting relationship between consumers and your brand.
October 5, 2018
Predictive Analytics: A Powerful Tool for Marketers
By Janet Wagner, Zylotech
Many companies today are using a variety of analytics tools including predictive analytics. Predictive analytics can be applied to many areas of a business. But for marketers, predictive analytics is an extraordinarily powerful tool.
What is predictive analytics?
Predictive analytics involves applying statistical algorithms and machine learning techniques to historical data (and sometimes real-time data) to make predictions about future events. Predictive models estimate the probability of a specific thing that will happen in the future. For example, a marketer could use predictive analytics to figure out which customers are likely to buy a particular product soon or to forecast and prevent customer churn.
It should be noted that businesses must feed predictive models and analytics tools high quality, contextual data. This blog post highlights several essential components of quality data.
How marketers could use predictive analytics
Predictive analytics can be used to optimize and personalize just about every type of marketing campaign including web, mobile, and email.
Web marketing
Predictive analytics makes it possible for marketing teams to create highly personalized web marketing campaigns. For example, predictive analytics could be integrated with an e-commerce site to optimize onsite ads. If a customer is logged in, historical data could be used along with real-time data to make predictions about the products that customer is likely to buy. The site would then automatically display ads offering personalized discounts based on the customer’s propensity to purchase specific items. Predictive analytics could also be used to enhance the cross-selling and up-selling capabilities of the e-commerce platform.
Mobile marketing
Businesses could use predictive analytics to re-engage past customers via mobile marketing. For example, an office products company could send customers timely 1:1 personalized sales offers via mobile phone push notifications or SMS text messaging. Retailers could leverage predictive analytics along with location-based technologies (e.g. GPS, BLE Beacons) to create marketing campaigns based on geographic data and shopping behavior. Retailers could send sales promotions to mobile customers who are near specific store locations and most likely to buy the sales items. These location-based promotions would help increase local sales revenue.
Email marketing
Using predictive analytics businesses could optimize and personalize email marketing campaigns. Some companies use a one size fits all approach when it comes to email marketing. For example, a clothing retailer may send an email every August offering a 50% summer clothing discount to all customers to get rid of excess stock. Using predictive analytics, the retailer could tailor that email marketing campaign based on the propensity of customers to buy. Instead of emailing all customers a 50% discount on summer clothing, customers who are more likely to buy would instead receive an offer of 25% off. By reducing the number of 50% discount offers and emailing each customer a discount they would likely respond to, the retailer could unload the excess inventory and significantly boost revenue.
A powerful tool for marketers
This post highlights just a few of the ways marketers could use predictive analytics to create effective marketing campaigns and drive customer engagement. When given high-quality, contextual data, predictive analytics is a powerful tool for marketers.
October 2, 2018
5 First-Rate Email Tips to Deepen the Relationship With Your Customers
By Shefa Weinstein, Optimove
When Optimove acquired DynamicMail (the company I worked for), I was super excited. I really believed it was a great fit for the company. When two businesses have different offerings, yet share the same tagline – deliver the right message to the right person at the right time – they are sure to produce great things.
But what I initially loved most about Optimove—and deeply connected to—was its commitment to helping marketers deepen the emotional relationship with their audience. I’ve learned over the years that a strong relationship is the basis of a good email campaign. The more relevant an email is to the user, the higher the open and click rates. Building these relationships with your customers will not only help increase their engagement (resulting in a rise in sales), it will also help generate brand ambassadors.
So, to deepen my relationship with our community, I want to share 5 great email tips to help you and your users form a meaningful, mutually beneficial relationship.
1. Start with the basics – how often are you emailing your customers?
We’ve all had days when we open our inbox and see four emails from the same brand. When I see a brand clog up my inbox, I can’t help but wonder “what on earth are they thinking?” Last week, after experiencing this very nuisance, I snapped.
In that moment, I hunted for the unsubscribe button. I filled out the form with a feeling of glee and satisfaction, but I also harbored negative feelings after the act. Even though I know they didn’t mean to annoy me, I felt that they weren’t thinking about their customer enough, about me as a user. My advice? Whenever possible, think like a user. Would you want to receive so many emails in such a short time span?
This type of consideration can ensure that you are actively working on your relationship with the customer. Using automated rules and segments can help you take this step. For example, if you sent out a birthday note, there is no need for them to receive the weekly promotion as well. Using automation, you can dictate that a user who received an email during the current week will not receive another email on the same subject, or that a user who received the birthday note will not receive a promotional email a few hours later.
2. Do what you say you will
Recently, we began working with a client who placed product blocks in their email campaigns. Each block had an image, name, and price. When the user clicked on the box, however, the action directed them to the category page rather than the product page. To me, this felt like false advertising. The user saw a product and was interested enough to click on it, but instead of giving them what they wanted, the brand redirected them to a completely different page. Take a minute to think about all the links you publish – do they all connect to the content you promised? If you haven’t been mindful of this, now is the time to start. Building trust with customers takes time, but with an increase in trust comes an increase in open rates, engagement, and loyalty.
3. Personalize the email – and there are many options
It feels like personalization has been ‘one of marketing’s biggest trends’ for over a decade. Personalization started as a small concept, just adding the user’s name to the email. As time went by, the options for personalization have evolved tremendously. From the users’ name to their loyalty points on demand, to what type of content they want and should be seeing, everything can be personalized. True, you can always tell your customers how to find your closest branch, but wouldn’t it be better if you automatically showed them the closest store? In every section of your email, consider how you can take it a step further and personalize, personalize, personalize.
4. Contextual clues and behavioral triggers
Contextual clues and behavioral triggers come hand-in-hand with personalization. Instead of deciding ahead of time what to show your customers, let them “pick” their own content. By using information that you’ve gathered when they open their email, you can determine what users want to read. For example, you can use contextual clues to determine which assets to show based on the email capabilities – iOS users get a full video, while Outlook users see a single image. Behavioral triggers are even more exciting. If the user hasn’t purchased anything in over 60 days, you can give them a reason to come back based on their previous purchases or sales within their geographical location. If your customers left your site without checking out, you can show them what they left behind the next time they open their inbox. Using these clues and triggers, you show users that the email was tailored specifically to them, turning your emails into ‘must-reads!’
5. Realtime content
More than ever, users expect a lot from your marketing efforts. The offers within an email need to be enticing and dynamic in realtime. If your email advertises a fantastic deal, but it suddenly isn’t valid when the user clicks onto your site, you can forget about that customer opening your emails again. Make sure the content in your emails is updating in realtime as soon as the users open the email to ensure that you won’t disappoint your customers, and that clicks will lead to conversions rather than another disgruntled customer. Can you imagine the excitement on a customer’s face when they open your email and see “15% OFF, and we're only 0.6 miles away from you”? Using up to date content creates a unique and exciting email experience for each customer. Likewise, if a user opens your email and sees his coveted item on sale only to click and see it is out of stock, their frustration will have a tremendous effect on whether they continue using your brand.
These 5 tips are just the beginning. As Optimove and DynamicMail continue to grow together, the benefits of cutting-edge marketing technology and love for visionary email tools will help our clients take their email marketing to the next level. Stay tuned for our future blog on updating your welcome stream, birthday emails, and weekly promotion emails.
September 28, 2018
The New Role of Marketing Operations and Technology
By Chuck Leddy, Zylotech
The role of the marketing executive has changed dramatically in the last decade. With the accelerating growth of marketing automation and other emerging marketing technologies, a key component of the executive’s role has become dynamically matching the organization’s marketing needs with just the right technological tools, while ensuring that the martech stack remains integrated.
Marketers have turned to digital channels because that’s where customers are and new technologies to track them. Marketing executives are using their marketing technology stacks in so many ways, including: (1) collecting customer data, (2) creating customer personas and segmentations, (3) communicating with customers, often in automated ways, (4) distributing and scheduling content across multiple channels, (5) nurturing/managing leads, and much more.
No more genuflecting to IT
Years ago, marketing executives might go hat in hand to IT, explaining their technological needs and asking IT to select and install the right marketing technology. In those bygone days, marketing executives waited for implementation, prodding IT leaders who were managing multiple projects from multiple business units (HR, Finance, etc.). Today, marketing executives are deeply involved every step of the way, typically taking the lead from the beginning (choosing the martech) all the way through implementation and far beyond.
Wearing many hats
In terms of managing marketing operations (MO) and driving technological change, today’s marketing executive requires new skills and new roles. Marketing executives act as data-driven process optimizers, work as data scientists who garner key insights from data in order to drive better decision-making, and serve as portfolio managers who deeply understand ROI. As MarTech advisor Debbie Qaqish explains, marketing executives are tasked with analyzing how their marketing operations are performing, much “like they are looking at a stock portfolio. Analysis and optimization of all current [MO] programs is an on-going and agile practice.”
Martech’s accelerating pace
A new set of processes has come into play for acquiring marketing technology. Perhaps the biggest challenge marketing executives face today is curation, finding just the right technology tool (among thousands) for just the right marketing task. There are currently 6,242 martech vendors (compared to 150 in 2011) in 48 categories offering a booming number of solutions. And since martech is evolving so quickly, it’s becoming harder to simply keep pace with what’s even possible, let alone implement (and integrate) new, cutting-edge martech capacities and solutions.
4 habits of highly-successful marketing executives
What makes a successful marketing executive today? Well, it helps to be an open-minded, fast learner. Marketing executives must also be masters of integration, data analysis, customer experience/CX, and marketing attribution (connecting activities to ROI). Here are four required qualities a great marketing exec needs:
1. They are team builders. Great marketing executives attract, assemble, manage, and retain talented teams. Of course, in a landscape of accelerating change, an organization’s talent needs can shift over time. Great marketing executives are continually assembling and tweaking their teams to align with evolving business needs.
2. They blend talent and tech. The marketing solutions of today and tomorrow blend people and technologies in ways that get optimal results from each. As technologies like AI and machine learning continue to evolve, “human + tech” blendings will become more dynamic and essential.
3. They are “business-first.” Great marketing executives think far outside their departmental silo, collaborating across functions as strategic partners in the C-suite in order to grow the business. They know that marketing isn’t about marketing, but about growing the whole business.
4. They learn to remain relevant. Great marketing executives constantly stay up-to-date, not only with technological change but with the behavioral trends of customers. The truth is, companies don’t even know what specialized skills or emerging technologies they may need to leverage in ten years. Great marketing executives know that agility and continuous improvement are the keys to success today and tomorrow.
September 12, 2018
PWAs: What They Are and Why They’re Important
By Ty Gavin, Tealium
What is a PWA?
A Progressive Web App (PWA) is an app that runs in a web browser with extra features that make it behave as if it was an app installed from an app store. These features include “push notifications” as well as the appearance of being online even when in airplane mode. With the introduction of PWAs, we now have webpage technology running in a small screen, and while it does have a limited network it still keeps us in a state of app-like bliss — even when offline. If you’re like me, you’re very excited that PWAs are JavaScript and HTML. Google Android and Chrome have the most support for this technology, but other browsers support service workers which can help speed up the experience and handle the offline state.
The ‘Sort-of’ Online State
Google is using a term describing the state of “sort of” online called “Lie-Fi.” Sometimes your phone’s Wifi reading is lying to you that it is online. The strange state of having poor Wifi connectivity is something we’ve all experienced and we typically blame our network administrator (at home or in office) or our cable company. But it could be the web server itself to blame — for example, this can happen when retailers have a sale and their website speeds are slower than usual due to increased online traffic. The Wifi lights may be on, but nothing seems to show up after clicking refresh.
According to Google, “Lie-fi” is when the device is connected but the network connection is extremely unreliable or slow and the network request drags on and on before eventually failing. Users end up wasting precious seconds just waiting for the inevitable.
Cache and Service Workers to the Rescue
Retailers and other web content providers (anyone who has a website you visit infrequently and will never install their app) now have Progressive Web App technology to solve for this offline state. The solution is simple: a modern web browser will run something in the background that will display content from a previous visit to the website when the web app determines that the connection is off or just really slow. The underlying technology is a ‘service worker.’
What is a service worker? A service worker is extra logic that intelligently decides what state you’re in (offline or online) and if the app should get new content from the network or get content from the cache.
You’ve most likely heard of cache before and may be thinking, “Doesn’t the browser cache files already? If I go to clean out my browser file storage, I see hundreds of megabytes to delete.”
While the browser has been doing some optimizations for speed improvements, this cache was not designed to help with the “no network” experience. The difference with traditional browser cache and service worker cache is that the service worker cache is available when offline.
For typical websites, when you’re offline (and don’t have a service worker), you don’t get to see what is in the cache, and instead, see the famous dinosaur image in Chrome (pictured below).
Web developers can now handle this situation by building service workers to pull content out of their cache. The experience (for the most part) is now just the same for the person browsing the web high up in the mountains where Wifi may not be plentiful.
Web Analytics When Offline
If you need to report analytics data on website usage, you may be wondering how to report usage to a web analytics vendor(s) when there is no connection and how to track some of the new events that happen in PWA.
For the offline state, this is rarer in our current 4G world. Of course, you won’t know how rare until you attempt to measure it.
If you have a large client base with intermittent internet, your analytics vendor tracking libraries are already ready to queue up tracking when offline.
- If using Google Analytics and Google’s workbox library, it’s just a matter of calling:
- workbox.googleAnalytics.initialize();
You can even set up your PWA to store/retrieve the utag.js itself from your service worker cache in an offline scenario. If your utag.js has loaded and has the Adobe Analytics tag bundled (often the best practice) then you’re ready to go. There is no need to reinvent the wheel here.
New Events are Now Ready to Track in PWAs
The following events are introduced with Progressive Web Apps that are likely interesting metrics when determining the success of your PWA-building efforts.
Window Events:
- The App Install Banner Appears (“beforeinstallprompt”)
- The App Was Installed (“appinstalled”)
Service Worker Events:
- Push Message Received (“push”)
- Push Message Clicked (“notificationclick”)
- Push Message Dismissed (“notificationclose”)
- Additional Events (“install”, “fetch”, “activate”, “sync” events available as well)
NOTE: At the date of this article being drafted, most of the app-like events are not-yet-supported in iOS (Safari), but they do have service workers.
Now the Code to Listen for and Track Events
Final Recommendations
While there aren’t any necessary changes needed in your PWA when working with Tealium’s Tag Management (TMS), we do recommend making the following updates:
- Use the service worker to serve up a cached utag.js (for when offline) and enable the offline features of your analytics vendors.
- Add additional tracking events for the new PWA-specific events.
While PWA technology is still in the early stages and things may change, it is only a matter of time before you’re reporting on your user’s interactions with your updated Progressive Web App.
September 10, 2018
How Does a Customer Data Platform Solve These Common CMO Challenges?
By John Nash, RedPoint Global
Chief marketing officers (CMOs) are often tasked with executing the organization’s customer experience strategy. Meeting this expectation requires a deep understanding of customers, the technical infrastructure to personalize interactions across channels, and visibility into updated customer data in real time. Many CMOs lack any or all of these capabilities, with a recent survey of senior marketing executives finding that 47 percent of marketers have selective personalization at best, 37 percent still have siloed customer data, and 49 percent lack real-time customer data.
These are not new problems, and marketers have attempted to solve them for years. The issue is that traditional marketing technologies such as data lakes and campaign management solutions are ill-suited to meeting marketing’s needs at the real-time pace that is required. To truly achieve their goal of meeting customer expectations, CMOs need to adopt a solution that can break down data silos, leverage that insight to personalize omnichannel orchestrations, and access cleaned customer information in real time.
Only a customer data platform (CDP) solves all these challenges, empowering CMOs and marketing departments with unified data that can be leveraged to orchestrate personalized messaging and is accessible at the cadence of the customer. For this reason, a CDP is a powerful tool in the CMO’s toolbox – especially as their customer-centric responsibilities increase over time.
Customer Data Platforms Eliminate Data Silos
Siloed customer data is often a major customer experience issue. When customer data is kept in silos, marketers are unable to recognize loyal customers across different channels. In a practical sense, this means a customer who visits the brand’s website and then interacts via social media is often treated as two separate entries in the database. This happens because most marketing departments added customer engagement systems piecemeal over time, and each point solution collects and stores data a little differently.
CMOs need to break down these data silos, especially because customers expect a consistent experience from brands across all engagement touchpoints. Brands that retain data silos are unable to meet this need. A customer data platform is designed to solve this problem by ingesting data from all the marketing point solutions into a single portal, regardless of structure, cadence, or volume. The end result is a unified customer profile, or “golden record,” that collects all that is knowable about customers throughout the organization.
The better customer data platforms maintain this golden record over time. Keeping the golden record consistently updated is key; recent research found that organizations who maintain their database have a 66 percent higher conversion rate than their peers. This makes a golden customer record key to long-term customer engagement success.
Improve Personalization with a Customer Data Platform
The modern omnichannel customer expects a personalized experience across all digital and physical touchpoints. Many brands lack the ability to deliver on that expectation, whether because of data silos or a lack of other capabilities within the organization. With a CDP in place, the CMO gains access to a centralized source of clean, updated data for better personalization. Brands who leverage a CDP also become able to progressively profile their customers and alter personalization strategies over time.
This means a CDP can have a powerful impact on long-term business results. A recent survey found that data-driven marketing organizations are six times more likely to increase profits (45 percent vs. 7 percent), and are five times more likely to achieve a competitive advantage in customer retention (74 percent vs. 13 percent). As personalization improves through better data, consumers notice the improvement and stay loyal. It is far less expensive to retain an existing customer rather than acquire a new one, and the behavior and preference insights of a CDP streamlines that process dramatically.
Customer Data Platforms Provide Real-Time Data Availability
One of the most significant challenges CMOs face is gaining access to updated data in real or near-real time. The average customer’s path to purchase is often so dynamic and fast-moving that brands need to be able to respond in the moment of need. Right now, only seven percent of marketers can consistently deliver real-time experiences across all digital and physical touchpoints. Traditional data management solutions like data warehouses and data lakes only update at a batch cadence, and that is too slow for the quick-moving nature of customer engagement.
Customer data platforms are designed to accept batch and streaming data equally, and update the golden customer record accordingly. A CDP’s ability to maintain the golden record in real time puts all relevant information at marketing’s fingertips, which allows marketers to more easily personalize customer interactions and respond on the fly to signals that they may otherwise miss.
CMOs are increasingly responsible for delivering contextually relevant, impactful customer experiences. They need the right tools for this job, and a customer data platform is one of the most powerful. With a CDP deployed in the organization, the CMO can eliminate internal data silos, more easily personalize customer interactions, and have clean customer data accessible in real time. With these three challenges solved, CMOs are better able to meet the demands of their customers and power revenue growth in the organization.
September 6, 2018
Personaliz(s)ation: Where to Focus?
By Greg Morton for CDP Institute
Is the EU a good place to invest and focus if you are a provider of personalization software and services?
Personalization is becoming a standard that all companies need to adopt to survive moving forward. It is a game changer that will shift $800 billion of revenue to the 15% of companies that get it right over the next five years, according to Boston Consulting Group1. In addition, 98% of marketers1 agree that personalization positively impacts customer relationships with the principal areas of benefit coming from increased visitor engagement, improved customer experience and increased conversion rates.
The quest for personalization is becoming an important capability for marketers globally and a high growth marketplace for organizations who offer personalization software and services. But, North American-based brands are twice as likely as EU-based brands to be more advanced in their personalization journey, according to Monetate1. With all this value driven from personalization, why is the EU not as advanced as the US? Should personalization software and services vendors focus on the EU? The answer to these questions depends on the state of personalization in EU. This report looks into that.
What is Personalization?
Gartner defines personalization as "a process that creates a relevant, individualized interaction between two parties designed to enhance the experience of the recipient. It uses insight based on the recipient's personal data, as well as behavioral data about the actions of similar individuals, to deliver an experience to meet specific needs and preferences."
What is the EU consumer’s attitude towards personalization?
Let’s start with customer behaviors. Personalization is proving to be a lucrative opportunity for EU retailers. It has proven to lead to impulse buys. In a recent EU personalization market report from Segment2, 59% of EU shoppers said in the past three months a personalized offer led them to purchase a product that they didn’t initially intend to buy. In addition, 20% more EU shoppers were likely to make an impulse buy based on a personalized recommendation than shoppers in the US. Overall, 40% of EU consumers say they have purchased something more expensive than they originally planned to because their experience was personalized and 65% report being satisfied with their last-minute purchases.
Despite the opportunity, only 34% of EU consumers expect highly personalized experience when they shop. More consistent personalization should improve results, so long as recommendations are accurate.
How do EU marketers compare with marketers elsewhere in their approach to personalization?
One way to understand the EU personalization market potential is to compare EU marketers to others elsewhere. We’ll look at this from angles including (1) the sophistication of the current martech stack, (2) level of Machine Learning, (3) level of Attribution, (4) level of data analysis sophistication and (4) adoption of buy online and pick up in store (BOPIS) and (5) and level of martech spend.
· Marketing technology. EU marketers are less confident in their tech stacks than US marketers. According to Salesforce3, over 70% of marketers in US say their current tech stack is extremely or very effective at increasing productivity by delivering better analytic insights, providing more cohesive view of customer data and delivering more efficient marketing spending. Just 60% in EU of marketers make similar claims. A 10% difference is significant.
· Machine learning. Monetate’s1 research has shown that UK companies are several months behind their peers in the United States in adopting machine learning. This is likely a reflection of different data privacy policies and consumer expectations, but also the adoption stage of personalization in the United Kingdom and elsewhere in the EU. European marketers are less likely to have a documented plan or dedicated budget than their counterparts in US. We expect the gap on machine learning to close over time. This provides a unique opportunity in the EU with a high propensity for growth in personalization adoption.
Monetate, 2nd Annual Personalization Development Study
· Attribution. Marketing attribution is another indicator of personalization readiness. A recent Adroll attribution4 study found that North American and EU marketers agree that attribution allows better allocation of budgets across channels. But 51% of American companies were actually using attribution on most or all of their campaigns, compared with just 39% of EU companies. The study also reports that North American marketers and analysts are more than twice as likely to say that their systems are ‘very flexible’, suggesting greater confidence in their attribution tools.
The good news is that Europeans see the value of attribution. In fact, 71% in EU vs 55% in US believe that a better understanding of how digital channels work together is a benefit of marketing attribution. This is more evidence that the EU is a ripe marketplace for personalization.
AdRoll, State of Marketing Attribution 2017
· Analytics. One the main capabilities required to drive personalization is the ability to analyze the data an organization collects from all channels. This is another area where EU companies lag the U.S. In a recent Gemalto study5, 42% of North American respondents believe their organization can highly effectively analyze the data that it collects. Figures from EU countries ranged from 29% in Germany to 19% in the UK, with France and BeNeLux in between. Clearly there is room for improvement for the majority of organizations when it comes to analyzing data – if they can’t analyze it effectively, then they may as well not collect it at all. The EU region’s inadequacies to effectively analyze data pose another obstacle to expanded personalization and a good opportunity for personalization services and software firms to drive value.
Gemalto, Data Security Confidence Index Report, 2017
· Omni-channel retail. Omni-channel personalization grows revenue. The EU is actually ahead of U.S. retailers in the pocket of omni-channel retail capability known as BOPIS (Buy Online and Pick up In Store). A recent Order Dynamics Global Omni 10006 study found higher levels of BOPIS capability in UK and Nordic countries than in the US and Canada. BOPIS can be an important revenue driver because it provides consumers with greater flexibility and options. These positive results show the potential value from further personalization investment across all sectors in the EU.
Order Dynamics Research, Omni-1000 Research: Global
· Technology spend. Another reason the EU is has a great potential for growth is that the EU marketers are actually spending a larger share of their budget on marketing technologies. A recent study by Moore Stephens7 found that martech consumed 18% of the average EU marketing budget compared with 14% in the U.S. This is mostly due to a higher propensity to outsourced marketing spend rather than build in-house. This is good news for personalization software and services wanting to invest in the EU.
Summary Findings
The EU provides a growth opportunity for personalization software and services vendors. Although the EU marketer is behind the US in many areas, the time is now for EU brands to start really driving personalization initiatives to surprise and delight their customers with accurate, one-to-one personalization. EU brands that adopt personalization have proven it to be extremely effective.
There are a few obstacles. The EU lags behind in marketing technology, machine learning, attribution, and analytics. These findings may seem to show there are foundational obstacles that may delay the market in taking off. However, those that have adopted omni-channel retail have seen very positive results. In addition, the EU shows a willingness to invest in martech. All of this points to the EU having a potential for high growth in personalization.
With so much proven value that can be driven from personalization, this should be the immediate focus of all EU marketers. Thus, the EU personalization market has a higher propensity to likely grow faster than the US over next 12 – 18 months. There is already a precedent of a higher percentage of marketing dollars spent on martech solutions. This budget will be focused on personalization technologies which will become higher priorities than some of the other supporting marketing capabilities.
FOOTNOTES
1 Monetate, 2nd Annual Personalization Development Study, 2017
2 Segment, EU State of Personalisation Report, 2018
3 Salesforce Research, Fourth Annual State of Marketing, 2018
4 Adroll, State of Marketing Attribution, 2017
5 Gemalto, Data Security Confidence Index Report, 2018
6 Order Dynamics Research, Omni-1000 Research: Global, 2017
7 Moore Stephens, Martech: 2018 and beyond, 2018
September 4, 2018
CEO Jeff Lunsford Describes Long Term Guiding Principles
By Matt Parisi, Tealium
 Read below for part 1 of an ongoing interview series with leaders from Tealium describing how we approach today’s data challenges. Our first interview is with CEO Jeff Lunsford to delve into Tealium’s mission and guiding beliefs:
Matt Parisi, Interviewer: When comparing software and vendors out there, it can be easy to lose sight of the vendor’s purpose as you check off feature boxes. But with how fast tech and behavior changes, it’s arguably more important the direction a company is headed than the specific feature that exists today.
What is Tealium’s vision in the market? What was the initial opportunity and how did it evolve to where we are today?
Jeff Lunsford, Tealium CEO: The world is awash in data. We started Tealium back in 2011 because of this explosion of customer data. Companies saw an opportunity to leverage these new digital touchpoints that were creating all of this data and wanted insights about their customers from it…but they didn’t have the technology to manage it. This great opportunity with so much data became a problem having so much data, which then created a very large data fragmentation problem that we’re tackling today.
We, as software engineers, found an opportunity to create a standard data layer across the enterprise that defines who a customer is and what’s important, where all important attributes could live and be accessed in one place. We called this platform Tealium’s Universal Data Hub (UDH) and we set out to build it in 2011 when we founded the company.
We built it step by step. The first component was that we saw 80% of the big data problem was from interactions happening on a website. We started with tag management (Tealium iQ), then advanced to creating mobile capabilities. In 2014, we created a Customer Data Platform (CDP), called AudienceStream, where we take visitor-level data, store it and route it to other vendors. Then we created DataAccess, which offers the capability to store that data and filter/stream it to a data warehouse. And finally, because of the advent of IoT and mobile, we added EventStream, a cloud-based API hub. Those 4 components make up Tealium’s UDH.
Our UDH allows companies to listen to all their different data sources, normalize it and leverage that data across all of their best-of-breed apps. Rather than picking up a software company only at the customer experience layer, we allow a customer to have a flexible, vibrant and real-time data model that is leveraged across all packages.
MP: Interesting to hear how the needs of customer data management have evolved from tag management in the early days to the capabilities offered by customer data platforms today. When Tealium built its customer data platform, the category didn’t exist, so I’m wondering what guides Tealium’s decision-making.
What does Tealium view as its guiding mandate? What is Tealium trying to solve and how should people out there researching Tealium view its approach?
JL: Digital Transformation mandates. All companies are going through digital transformation initiatives – so how will they be competitive in this new world, where there’s all these data sources and customer touchpoints?
We believe there are 5 mandates for successful digital transformation:
- Put the customer at the center of your business, technologically and operationally.
- Do this in real time. If you ingest data in a manual, batch way, it could take 24 hours to get the data in and you’re now trying to create a personalized experience based on what someone was interested in yesterday. That doesn’t work in the long term.
- Follow the privacy laws. With all this data comes great responsibility (i.e. GDPR in Europe and privacy regulations in California).
- Machine Learning (ML) ready. A human cannot process this data alone because of the sheer volume of data today. It needs to be programmatically managed and able to leverage sophisticated ML algorithms.
- Data should flow freely across the enterprise. It needs to be accessible and real-time for all teams that are accessing it (i.e. when a customer calls in to a call center, that department is able to see any actions that same customer just took on the website), while still following privacy laws.
Companies that go through this Digital Transformation and incorporate these 5 key requirements into their processes will be more competitive in the future.
We believe Tealium is the only company in the world that has built an end-to-end platform that covers the whole data supply chain and lifecycle from point of data creation to final resting place, at a global scale, that meet these 5 requirements of Digital Transformation. No other company has built that (yes, of course there are other point solutions here and there and in other categories that cover some portion of the data supply chain) – but no one else has built what we’ve built.
Why Tealium? We’re the innovators; we invented the real-time CDP concept. We’re the first ones that knew data collection and activation had to happen in real time.
Why Tealium? We’re pioneers, thought leaders, and listen to our customers religiously. We listen to their pain points and have built products to solve those pain points. We stretch ourselves and we build for the future and we invest in our customers future.
Why Tealium? We have a model where you can build it yourself or use Tealium.
Why Tealium? We’ll be here for decades to come, still listening to our customer.
MP: Tealium’s first mandate of putting the customer at the center of everything you do brings the current CDP category buzz to mind.
There is a very diverse collection of tools in the space, and as a pioneer in the space, how did Tealium’s vision lead down that CDP path before there was an even a CDP category to build against? And what does that mean for a Tealium customer? What advantages does our approach give over other tools?
JL: Prior to Tealium there were companies that tried to solve this problem with an advertising approach by way of Data Management Platforms (DMPs). They were combining third and first-party data. In our approach, we were the first vendor that said we’re going to help you manage all of your first-party data – as well as all of the other data you have about your customer. We’ll help you capture the data and you decide what you send to your DMPs and what you don’t, in real-time.
That first mandate is customer data – your data. We’re first party data, in real time, and you can control that data geographically to follow privacy laws. And we added ML and future-ready data and always ensure data is freely flowing between the four walls of your business.
MP: Speaking of the future, how do you see that evolving and how will Tealium’s vision keep its solutions in tune with the market? What’s coming out now that you’re excited for that our vision is leading us towards?
JL: The most exciting thing is the continued addition of data sources and digital touchpoints for customers. There are only 24 hours in a day and now we have smart devices like Alexa and Google Voice and Siri…listening to us all 24 hours a day with these different voice interfaces. We have smart IoT devices, smart homes, cars and TVs.
What’s going to happen is that every company we work with, depending on their industry, will only continue to grow their number of data sources and type of data they’re collecting (ie: healthcare having doctor’s office visit data, sports teams having sports venue data), and we will help them seamlessly collect, correlate, and act on that data in a way that satisfies the first mandate. There will be a Darwinian explosion of companies that help with these touchpoints, and Tealium will be the leader in how brands are leveraging this by way of providing the capability for companies to manage their data from the point of collection all the way through delivery, and back again.
I’m excited about bringing in atmospheric data (i.e. weather, sports scores) like things that allow our customers to deliver a more contextually relevant experience to their customers because we have access to that data (i.e. body temperature, sports data).
Building a massive engine that can pull customer data from all sources and allow you to act on it and correlate it in real-time – Tealium is the engine. You set it up, you do with it what you want – Tealium is the engine that helps you drive your data to where you want it to be.
MP: What are your thoughts in the Artificial Intelligence (AI) realm? What kinds of advantages does Tealium have with working with AI and the data, based on the approach Tealium takes?
JL: AI is obviously one of our 5 mandates with ML-ready data. We’re very excited about customers having the ability to take this data and use whatever algorithms they want (Tealium will package some), and they’re becoming more accessible. Tealium will be open in our architecture. You can use whatever AI models you want or you can use ours. We believe these models are required to do things like predictive modeling, next best action, product recommendations and pricing algorithms. No matter what companies are doing with AI, it’s essential to have a substantial amount of data that has been managed for this purpose— that’s what we do.
Tealium is not an AI company – we’re an AI enablement company.
With Tealium you get clean, structured data that will get you results. We’ll get you clean data which is the first step in successful AI.
Thank you Jeff!
August 30, 2018
B2B Marketers Fail to Act in Time
By Sandeep Koul, Zylotech
Even with a sophisticated marketing stack, b2b marketers are inefficient in using data for decision-making due to delays between insights and action.
There is an ever increasing pressure on marketers to have KPI’s directly linked to revenue. Despite this, marketing teams are failing to demonstrate their impact. A recent survey in a white paper by Marketo shows that almost 61% of marketers say they are either fair or poor in this area.
It is puzzling to see almost two-thirds of marketers fail to show their impact despite having cutting edge technology at their disposal. Modern marketing stacks constitute tools capable of capturing customer data, analysing it, and automating marketing operations.
One clue to this puzzle can be obtained by a recent survey of marketers by MIT Sloan Management Review. In the report, only 49% responded that they were able to use data to guide marketing strategy.
This means that though marketers have access to technology that is capable of capturing and analysing data, they still fail to use insights for taking action. But then what is stopping them?
The culprit: Delay in data activation
Analysing current marketing stacks points to a gap: there is a delay between the times when insights are generated and when actual action is taken—insights and action are out of sync. Gartner provides a power framework to make sense of this. The contemporary analytics continuum looks something like this:
The feedback loop you see in the above diagram causes a delay between insights and action. Most of the analysis done by b2b marketers is reactive in nature, inhibiting marketers who actively use prescriptive analytics with decision automation. This means that though they know answers to:
- What happened? (e.g.: 20% more conversion in a campaign about X service)
- Why it happened? (e.g.: person Y is inclined in knowing more about X service) &
- What will happen? (e.g.: 70% chances of conversion if we immediately pitch X service to just person Y)
They still cannot use this knowledge to act in time for results that are impactful.
Marketing stack that can help
As in most cases, technology can save the day for b2b marketers by having a marketing stack with not only a Data & Decision layer, but also an automated Delivery layer.
Here is our take on this solution:
The above diagram shows an ideal stack that can considerably shorten activation time. This delivery layer would capture insights by the decision (analytics) layer and, with the help of tight integrations via APIs, could deliver insights back to campaign and CRM tools for timely action. For example, the system could maintain automated delivery of suggested content to a suggested segment by delivering insights directly into campaign workflow.
August 29, 2018
10 Questions to Ask Your Customer Data Platform Vendor
By Buck Webb, RedPoint Global
Does this sound a bit too familiar? Articles and whitepapers espouse the benefits of a holistic view of the customer. You agree, but your data is in purpose-built silos that aren’t being dismantled anytime soon. You’ve determined through research that a customer data platform (CDP) is the right solution to help you create that coveted single view of the customer and you’re ready to talk to vendors to see which CDP is best for your organization. The problem is that numerous companies you’ve encountered claim to provide a CDP, yet they all handle customer data differently.
The confusion in the marketplace about what a customer data platform is — and isn’t — stems largely from a lack of an agreed-upon definition of the solution. Even analyst firms such as Forrester and Gartner, and organizations such as the Customer Data Platform Institute, all define a CDP somewhat differently. As a result, some vendors have branded their product as a customer data platform when they perform adjacent functions or only some functions of a robust solution.
A true CDP is an enterprise solution that ingests all sources and types of enterprise customer data to provide an always-on, always-updating golden record that is continually available at low latency to all touchpoints and users across an enterprise.
It should help you…
- Bring together data from across your organization;
- Know all that is knowable about your customers;
- Deliver relevant, personalized offers whether customers are in your physical location, online, or on their mobile device;
- Recognize a customer across multiple channels and interaction touchpoints;
- Build progressive profiles and create privacy-compliant persistent customer records that anyone appropriate throughout the enterprise can access when they need it.
There are 10 questions you should ask the vendors on your short list to ensure their platform meets that criteria before you invest in their CDP:
- What kind of data structures does the CDP accept? A true customer data platform supports any kind of data, whether batch or streaming, internal or external, structured or unstructured, transactional or demographic, personal or general.
- What kinds of databases does the customer data platform integrate with? CDPs are database-agnostic and take an open garden approach to handling data. This means it should be able to function equally well with Hadoop, NoSQL, and traditional data warehouse technologies.
- What solutions does the CDP accept data from? The function of a CDP is to provide a fully unified view of the customer. Real customer data platforms are able to ingest data from every solution in the marketing stack, without limitation.
- How does the customer data platform resolve customer identities across touchpoints? CDPs are meant to provide a single point of control for all that is knowable about a customer, whether that point of control is in marketing, IT, or elsewhere. Identity resolution — the process through which individual customers’ identities are searched, analyzed, and linked across disparate data sources — is a key facet of learning all that is knowable about a customer.
- Can the CDP accept data from offline and online sources? Many customer data platforms are designed to ingest data only from digital sources. It’s not possible to create a holistic customer view solely with online data. A true CDP accepts data from offline sources just as easily.
- Does the customer data platform accept batch and streaming data? Some data tools can process only batch data or only streaming data, not both. Real CDPs accept, ingest, and process both.
- Can the lines of business easily leverage the CDP? Customer data platforms are not IT-only solutions. They’re meant to make it easier for marketers and other business users to access the cleansed customer information they need in real time and without IT assistance. IT can be that single point of control that manages the data, but, ideally, they won’t need to use the CDP to provide marketers with models and data cuts.
- How flexible is the CDP? A customer data platform that requires expensive updating to accept data from new sources isn’t as robust or useful as a CDP that is built to easily accept new inputs.
- Is it easy to keep the unified customer profile updated? The hallmark of a CDP is that it keeps each customer’s golden record constantly updated. If the solution you’re considering can’t do that, it’s probably not a real customer data platform.
- How quickly is clean, updated customer data made available? Many solutions help marketing build and maintain a golden customer record, but only a true CDP makes that profile available to marketers and other line of business users in one or two seconds or at most a few minutes. “Onboarding” processes that take 24 hours to ingest new first-party data are not the speed of the customer.
If you’re ready to invest in and implement a customer data platform, it’s probably because you’re ready to build a single view of the customer without having to dismantle your current data structure. It’s likely that you also want the ability to easily add data to enhance those customer profiles, as well as reap the benefits of artificial intelligence and machine learning to use that data to personalize each customer’s experience. And you should. Consider: 79 percent of U.S. consumers say they expect brands to show they “understand and care about me” before those consumers will consider making a purchase, according to a study by marketing agency Wunderman. And, 56 percent of consumers polled in that study say they’re more loyal to brands that “get me” as a segment of one; in other words, businesses that show a deep understanding of their customers’ preferences, needs, wants, and past purchases.
You’re only going to show that understanding by responding to customers at their moments of truth with a real CDP. Nothing less will do.
August 20, 2018
How to Maximize the Value of Your Customer Data Platform
By Steve Zisk, RedPoint Global
A customer data platform (CDP) is a powerful way to gain visibility into the customer’s path to purchase. Like any robust technology, however, you can’t just turn it on and expect results. Rather, a CDP presents an opportunity to improve processes, such as the way you handle customer data, which you must do if you’re to gain the most value from the solution. In addition, because a customer data platform is an enterprise solution, it also helps you bolster cross-team collaboration to provide a better enterprise-wide customer experience.
Brands who deploy a customer data platform will be able to:
- Create an always-on, always-updating golden record that is continually available at low latency to all touchpoints and to all users across an enterprise to access when they need it;
- Bring together data from across the organization to know all that is knowable about your customers, allowing you to build progressive profiles and create privacy-compliant unified customer records; and,
- Recognize individual customers across multiple channels and interaction touchpoints, so you can deliver relevant, personalized offers whether customers are in your physical location, online, or on their mobile device.
Once you implement a customer data platform, there are five steps you can take to fully leverage its power:
- Build the single customer view
- Add data to enhance each customer’s profile
- Add in-line analytics and machine learning
- Personalize experiences across channels
- Optimize the CDP’s use across the enterprise
Let’s take a closer look at each.
Build the single customer view: You likely have purpose-built silos of customer data that serve specific areas of the business, such as accounting and marketing. These will need to stay in place, and that’s fine, of course. But you must unify the data from those siloed data sources in the CDP to get a complete picture of the customer. Doing so is the only way to provide truly personalized experiences.
To streamline building a single customer view, your customer data platform should be data agnostic and support all data sources: batch or streaming, internal or external, structured or unstructured, transactional or demographic. Advanced CDPs also allow you to integrate first-, second-, and third-party data and function equally well with Hadoop, NoSQL, and traditional data warehouse technologies. Develop a detailed project plan to prioritize how you’ll start connecting these data sources to your CDP and you’ll see that single view of the customer come to life.
Add data to enhance each customer’s profile: One of the most powerful aspects of a customer data platform is the ability to build and maintain a golden customer record at the speed of the consumer. The golden customer record should include every touchpoint or proxy identity the consumer presents, such as a cookie, a social media handle, or even a smartwatch. Each customer’s golden record should also include transactional and behavioral data, as well as other triggers. The transactional data should include the history of all transactions and interactions that a person represented by a proxy identity has had with the brand. And the behavioral and transactional data should integrate into the golden record in real time to ensure that analytics, personalization, and engagement are relevant and up to date.
Add in-line analytics and machine learning: Build a plan to take advantage of the advanced analytics, data processing, and matching algorithms a customer data platform enables. Take a crawl, walk, run approach. For example, start with features such as name, address, and phone standardization. Next, use the CDP to perform deterministic and probabilistic identity resolution and to build customizable groupings such as individual, household, segment, and business. Then add advanced parsing, normalization, and validation rules. After that test geocoding and spatial analysis. Using your CDP to do this intelligent data cleansing will reap huge benefits in analytics, both by reducing the time data scientists and marketing analysts must spend cleaning up bad data – currently data scientists spend more than half of their time cleaning data – and by improving both data quality and timeliness to produce better customer engagement. Ultimately, aim to use advanced analytics to understand individual and segment-based customer behavior to improve your marketing and customer engagement decisions based on predictions and goal-based optimizations.
Personalize experiences across channels: A staggering 87 percent of consumers say brands need to put more effort into delivering a consistent experience across channels, according to research by customer feedback platform provider Kampyle. The single customer view that a customer data platform provides will give you a deeper understanding of a customer’s preferences and behaviors. Use this insight to power messaging and actions that will align with the customer’s expectations. For instance, create cross-channel campaigns with personalized real-time messaging that respond to customers in their moments of interest and need as they move through the buyer’s journey.
Optimize the CDP’s use across the enterprise: Set up the customer data platform to maintain customer profiles in real time, while also allowing marketers and other line of business users to access that real-time, unified data when they need it. One of the core benefits of a CDP is the ability to continuously maintain clean, current customer profiles created from unified data. Without access to that updated customer data at the moment of need, marketers and other line of business users can’t act at the speed of the customer.
Customer data platforms are a powerful solution that solve a new version of an old problem: how to gain visibility into the customer’s path to purchase throughout the organization. The challenge is how to leverage the power of the solution properly to gain the greatest value once you’ve deployed it. Following these five steps will set you on the path providing a better customer experience and building revenues through the benefits your CDP provides.
August 15, 2018
ABM @ Scale: Meet Lattice Atlas
By Chitrang Shah, Lattice Engines
This is part 2 of a 2 part blog series. In this post I describe how Lattice Atlas, our latest release, enables marketers to successfully scale their ABM programs. In Part 1, VP of Product Marketing Nipul Chokshi highlights reasons why marketers are challenged with scaling their ABM programs and how that hinders their ability to drive impact.
At Lattice, we are obsessed with helping marketers realize the promise of delivering personalized experiences and growing relationships at scale. We thrive on solving the biggest customer challenges in an unconventional and radically simple manner by leveraging cutting edge AI technologies. Today, we are continuing this mission by launching Lattice Atlas, the industry’s first customer data platform for ABM at scale.
As we discussed in Part 1 of this series, marketers are struggling to scale their ABM programs to focus on more accounts, more segments, more product lines, etc. The challenges they face center around getting customer data and insights in a unified way.
Let me give you a simple example. One of our customers wanted to create an ABM program to acquire net new customers that looked like their existing customer base (“lookalikes”). This seemingly straightforward program requires a highly manual effort using ad-hoc tools and workflows supported by a large ops team. Why? Well, let’s take a look the long list of tasks they have to do:
- Aggregate data from four sources - target accounts from CRM, marketing campaign responses from MAP, website engagements from their website analytics platform and 3rd party intent data. In more complex environments, the data sources can be as high as twenty!
- Run the data-set through an AI platform to identify and segment lookalike audiences based on their existing customer profile.
- Socialize the audiences with the business teams to get their buy-in.
- Ensure they have “right sized” the audiences to optimize capacity/budget constraints along with their campaign objectives.
- Ensure any opt-outs (to meet GDPR/compliance requirements) are removed, as well as anyone that’s already in a sales cycle.
- Load the audiences in their various campaign execution platforms (LinkedIn, email SDR channels, etc.).
- Set up campaigns in each of the channels with the right content and other parameters. Content needs to be consistent for all audiences.
- Aggregate the engagement across the different channels (using spreadsheets) to report on the effectiveness of the ABM program, then rinse and repeat from step one.
All this work to support one narrow ABM program for one solution. Imagine the level of effort required if they wanted to run separate programs for various stages of the buyer’s journey or if they wanted to run programs based on customer lifecycle stage across each of their product lines. It is almost impossible to do so with the fragmented data and disparate applications glued together with ad-hoc tools and processes.
So, unless we wanted our customers to limit their ABM efforts to a small scale, we needed to innovate - at a massive scale. We realized that our customers needed a Customer Data Platform (CDP) that unifies all data, enables AI-driven audience creation as well as omnichannel activation & personalization all in one centralized place, and provides enterprise-grade marketing governance. That’s how Lattice Atlas was born. This next generation AI-platform helps marketers scale their ABM programs by removing four major bottlenecks.
Unified Customer Data
Lattice Atlas is architected on open platform principles and the data model is designed to aggregate a wide variety of data, enabling customers to create 360-view of their prospects and customers. It has a standardized data ingestion interface to import 1st-party data from internal systems quickly & easily. In line with our tradition of building everything to scale from day one, the platform is designed to process billions of signals everyday.
Our patent-pending Adaptive Match technology then does the hard job of resolving identities, matching the 1st-party data to 3rd-party data in Lattice Data Cloud with over 20,000 curated insights and automating the data unification. This automated process dramatically reduces the time and effort needed to create a 360-view, eliminating the first bottleneck to enabling ABM at scale.
AI-Driven Audiences
Radical simplicity is in our DNA. We decided to build Lattice Atlas with the goal of enabling marketing teams to build highly targeted audiences with simple point and click in a matter of minutes - not days, or weeks. With self-service modeling and automated rescoring embedded in the platform, every account and every contact is automatically rescored as the new signals are detected, creating AI-driven insights for the next best actions and personalized engagements every step of the journey.
The product experience is designed to let the marketing teams do what-if analyses on the fly, define criteria for the next best actions and “right size” the audiences with just a few clicks. For the always-on campaigns, marketers can define the criteria once and the platform creates the audiences for them automatically and keeps it up to date, enabling personalized engagements at scale. The platform also lets marketers manage brand reputation by assigning engagement thresholds and removing targets that may have reached marketing fatigue.
The AI-driven insights and simple marketer-friendly way to create and manage targeted audiences remove the second bottleneck in enabling ABM at scale.
Omnichannel Activation and Personalization
According to McKinsey, the average B2B customer engages with 6 channels prior to purchase. So how do we support these disparate channels? Well, we opened the Lattice Atlas to integrate with all of them. The platform has real-time REST APIs so our customers can pull audiences and recommendations in any channel in real-time and enable programmatic personalization at scale. We are also investing heavily in building out-of-the-box apps for the most common channels so that customers can scale their ABM programs quickly and easily.
Enabling marketing teams to programmatically deliver the right message at the right time via the right channel eliminates the third bottleneck facing marketers.
“Informatica knows that creating an interactive and personalized buyer’s journey is critical for the success of account-centric programs,” said Steven Shapiro, VP of Digital and the Buyer’s Journey at Informatica. “We’d seen previous success with Lattice and knew that the Lattice Atlas platform would create the personalized experiences we needed across all channels. Our vision is to use Lattice as the AI brain that powers all next-best-actions.”
Enterprise-Grade Governance
Lattice has been the pioneer when it comes to meeting the security needs of our customers. Lattice Atlas continues to carry the baton forward with new data governance capabilities. With GDPR on the horizon, we knew that ABM programs couldn’t get off the ground unless there was an automated and simple way to remove the opt-outs. That’s why we built a self-service interface for marketing teams to remove opted out accounts and contacts from their campaigns. This centralized system to manage inclusions and exclusions globally, for specific channels and/or for specific campaigns makes it easier than ever for driving privacy-compliant ABM programs at scale, and eliminating the fourth and final bottleneck facing scalable ABM programs.
Lattice Atlas was a natural evolution of our platform. Since day 1, our approach has focused on being deeply integrated with each execution application and managing all data under one platform. Because of this, we not only capture the largest amount of data, but also all that relevant metadata that describes it. Lattice Atlas is built on our understanding of these applications and their data to create the first CDP for enabling ABM at scale.
The first application debuting on the platform is Playmaker, which delivers prescriptive recommendations to sales teams adopting play-based selling. Our customers sell many solutions across many audiences. Playmaker lets them quickly identify top products to sell across all audiences and programmatically deliver those recommendations to the sales teams. It also has built-in interactive dashboard to track the engagements (or lack of it) and its impact on the pipeline, enabling out-of-the-box visibility into play ROI measurements and the ways to improve it.
Lattice Atlas is currently in private beta and is expected to be generally available in Q4. This release marks the next big leap in Lattice’s evolution as a marketing AI company. This is just the beginning, so stay tuned for more exciting updates.
August 13, 2018
ABM @ Scale: The Challenge
By Nipul Chokshi, Lattice Engines
This is part 1 of a 2 part blog series. In this post, I explore the key reasons why marketers are challenged with scaling their ABM programs and how that hinders their ability to drive impact. In Part 2, our VP of Product Chitrang Shah describes how Lattice Atlas, our latest release, enables marketers to successfully scale their ABM programs.
Account-based marketing (ABM) has rapidly gone mainstream in the last year. According to ITSMA research, marketers are now allocating 28% of their budget to ABM, up from 20% last year. Yet only 1 out of 5 marketers are realizing the benefits of ABM (Forrester Research study on ABM).
As practiced today, ABM is hard work. It’s especially hard to scale beyond the first pilot program into production across all target accounts. Scaling ABM programs has many facets, each of which is challenging:
- Number of Accounts: From 50 accounts to 5000 or even 50,000 accounts
- Account Focus: From “Strategic accounts” to enterprise and mid-market accounts
- Target Account Profile: From a single ICP to dozens of ICPs.
- Lifecycle Stage: From net-new customer acquisition for a single product line to new customer acquisition, cross-sell, up-sell, and retention across multiple product lines.
In the pursuit of consistent and personalized experiences across all channels, marketers have purchased many marketing and sales applications. Each of these applications has its own data, segmentation engine, activation layer, and reporting structure. Since the applications don’t share data with each other, and segmentation is often built on the limited data they have access to, the end-user experience is completely broken.
Marketers get around this by challenge by either running very manual campaigns using email and SDRs or relying entirely on diffuse channels like display or retargeting. The disconnect between different channels (and applications that don’t talk to each other) leads to a fragmented customer experience.
Figure 1: Comparing ABM @ Scale with Typical ABM Programs Today
In this post, I will dig into the root cause that prevents scaling and propose the emerging solution to this problem. In our next post, our VP of Product Chitrang Shah will describe this solution in greater detail.
Why B2B Marketing Can’t Scale ABM
The good news is that we are capturing more data than ever about our prospects and customers in a range of applications – CRM, marketing automation, web visitor logs, third party intent, DMP, ERP, support systems, etc.
The bad news is that all this data is fragmented and disconnected. Ops teams and IT spend weeks on end cobbling together the data needed for audience creation, activation and measurement. Most of this work is done in spreadsheets or data-lakes. Spreadsheets are error-prone, hard to maintain, and don’t scale. Data-lakes require IT involvement across the campaign lifecycle which impacts agility.
What’s an ABM practitioner to do?
David Raab, founder of the Customer Data Platform Institute, makes the case for a new technology called a Customer Data Platform (CDP) specifically built to help marketers drive personalized marketing. According to David, “Modern marketing requires a unified view of customer data to support coordinated, optimal treatment of each customer and prospect across all channels throughout the customer life cycle. Customer Data Platforms allow marketers to create this unified view.”
Similarly, Gartner says a CDP is an “integrated customer database managed by marketers that unifies a company’s customer data from online and offline channels to enable modeling and drive customer experience.”
Our point of view is that a CDP solves the “data problem” for scaling ABM. Because whether you call it a CDP or not, marketers ultimately need a solution that does the following in order to scale ABM.
- Unified Customer Data: The ability to not just aggregate and consolidate all your customer data, but also to map and organize that data by account, buying center and contact hierarchies. Gartner puts it best in a recent article: “unify customer data from disparate sources, linking identity, behavior, purchase and demographics in a single record.” This also includes adding in all available 3rd party data attributes about your customers.
- AI-Driven Audiences: With the volume and variety of data available at their fingertips, marketers need AI to predict and identify who will buy, what they’ll buy and when. This means being able to not just score accounts and contacts, but also using AI to identify whether buying committees have formed, assess the buying stage and identify the next best offer.
- Omnichannel Activation and Personalization: Being able to take these audiences and run hyper-personalized campaigns against them via every relevant channel. ABM thrives on omni-channel engagement, and the ability to deliver consistent messages to buyers across each channel is critical here.
- Enterprise Grade Governance: The ability to ensure that your customer data is safe and handled with care. As the crown jewel of any business, marketers must ensure that customer data is handled with care - and ensure that the solution appropriately complies with GDPR and all other privacy and security standards.
ABM practitioners have not had a packaged solution that meets the criteria laid out above ...until now. Read our next post to learn why we are announcing Lattice Atlas, the industry’s first platform for driving ABM at scale.
August 9, 2018
What Does a 360° Customer View Really Mean?
By Hadas Tamir, Optimove
Taking all the variables and data and combining them together for a better view of your customers may sound like a no-brainer. But to ensure personalization, the effectiveness of your campaigns, and real money savings, it’s a must. And it’s doable.
Let’s talk owls for a second… Did you know that owls can’t move their eyeballs? It’s because owls don’t have eyeballs at all. Instead, their eyes are shaped like tubes, held rigidly in place by bones.
Since owls can’t roll their eyes around the way we do, they have to move their entire head to get a good look around. They frequently twist their head and “bob and weave” to expand their field of view. Owls can turn their necks almost 360° in either direction and 90° up-and-down without moving their shoulders.
This flexible movement along with their enormous eyes, binocular vision and ability to hunt in the dark (when they are invisible to their prey) makes owls some of the most efficient night hunters on the planet.
So how does this tie back to marketing? I’m not suggesting that you think of your customers as prey and hunt them during the night; however, I do suggest positioning yourselves as marketers with a 360° view of what your customers are doing before defining your marketing strategy.
More variables, more personalization
Most marketers are turning the necessary corners and understand that today’s consumers expect brands to anticipate their needs, understand their preferences and only send them relevant offers and communications. Smart marketers need to “use” the consumers’ expectation to their advantage, tying together all aspects of a consumer's behavior into one beautiful personalized journey, or to be more precise, infinite journeys.
What is this 360° view constructed of?
- Purchase Activity – A goldmine of data on each customer readily available but unfortunately rarely used: How long have they been a customer? What are they buying? How often are they buying? What has their purchasing experience been like (number of returns, number of cancellations, good/bad reviews)? What is their AOV? Etc.
- Web & App Activity – What is the customer viewing online or in-app? What are they purchasing online? This also applies to what people were viewing even before they made their first purchase and became customers – when they were still considered just visitors to your site.
- Campaign Engagement – Which campaigns are customers interacting with? Which channel works best for which customer? (email / paid ads / push) Some may respond better to email campaigns while others to push notifications. Do certain customers have a higher propensity to open or click certain types of emails/campaigns?
On an analysis front, any of the above variables can be mounted together and analyzed to understand a customer’s behavior. Is a specific customer more inclined to purchase one type of item online and another in store? Can we identify strong cross-selling opportunities if a customer is only purchasing from a certain department but viewing items from another? If a customer is clicking through a campaign, adding an item to cart but not purchasing, this shouldn't be viewed as a loss, but rather an opportunity to understand what additional incentive the customer may need to complete his purchase.
The more variables we can utilize in creating our customer journeys, the more personalized we can get as marketers – hitting the exact spot that will bring our customer to not only make a purchase but fulfill their purchasing potential.
The all-inclusive view
Let’s take a few cases that can help us understand the usage of the above variables:
1. Churned Customers:
a. Purchase activity variable would show that these customers have not purchased for a long time (i.e.: in the past year) and are considered churned with a low likelihood of reactivation.
b. Web / App Activity – These customers are active on your website and/or app.
The idea is to identify these customers as “currently interested” because they are actively searching through the site rather than “previously interested” because they have not made a purchase in over a year. It would be up to the marketer to decide what level of communication they would want to include in their campaigns – for example target customers based on past purchases (including items from departments that the customer has previously purchased from), recent products/departments viewed online or web (items that we know the customer is interested in today) or just a generic “10% off just for you” (to all active churned customers) – all are viable options and relatively easy to implement with the relevant systems.
If we want to take this campaign a step further, we can add in the campaign engagement variable. Let’s say that after we sent out our campaign:
• Customer A has opened and clicked through to the website but has not completed a purchase. Our follow-up campaign would most likely include a bit of a stronger incentive since we know that the customer is indeed interested.
• Customer B has neither opened nor clicked the email. He/she may not have seen the email or may have ignored it. Our follow up campaign would most likely be either a reminder campaign (give our customer a 2nd chance to view the email) or a slightly different campaign that might appeal to the customer more in terms of content and graphics (different subject line / different template, etc).
2. One Timers:
a. Purchase activity variable would show that these customers only made one purchase. Consider how many days passed since their purchase. This will determine if they should be regarded as potential or at risk of being one-timers. If a customer completed their 1st order 10 days ago and we are seeing that the average customer places their 2nd order after 30 days, this customer should not yet be regarded as a potential one-timer, but rather a new customer who probably deserves a welcome series of campaigns.
b. Web / App Activity – Are these customers currently active on your website and/or app? Before they became customers, what items were they searching for?
The problem with customers who only purchase once is that the visibility into their line of preferences is extremely limited. The more items they purchase, the more we can personalize our campaigns and our strategy, nudging them to make that second purchase. By adding the variable of web activity even before they became customers, we can identify if they searched for any items/products/categories that didn’t end up in their final cart – thus enabling us to get a more inclusive view of what their preferences are and targeting them with cross-selling campaigns that they are more likely to respond to. In the same manner, we can utilize any current online or app activity to achieve the same results for cross-selling only relevant items.
3. Active Customers:
The same methods for cross-selling that we saw above can be used for determining up-selling opportunities. If a customer had formerly purchased items that average between $10-$20, but now is viewing items that sell for $30-$40, this is a clear invitation for marketers to send out an up-selling campaign that includes items/products that cost a bit more than what the customer is used to paying.
That being said, I wouldn’t recommend up-selling campaigns for inactive customers since our goal with them is to convert/reactivate them. First, let’s get them to start purchasing, then we can get them to start purchasing at a higher AOV.
One more aspect of cross-selling is the platform:
• Where are customers purchasing vs. where are they looking? Customers who purchase only in brick & mortar but are active online or in-app should receive an incentive to complete a purchase online or in-app. The more platforms they become comfortable purchasing through, the higher the probability that they will make more purchases.
• What items are they purchasing on each platform? If a customer’s purchasing activity shows that they purchase shirts online but pants in store, your marketing campaigns should be personalized accordingly. Don’t send a customer 20% off all pants online because you know that this customer prefers to try on the pants before buying them. These campaigns might actually have a negative effect on your customer if they feel like they can’t get the discount just because they don’t buy pants online…
4. Multi-Channel Campaign/Most Effective Channel – taken to the next level…
We all know the effectiveness of multi-channel campaigns and the need for increased visibility. Have you considered, however, that not every customer reacts the same manner to every channel?
a. Purchase activity variable shows what channel each customer is purchasing through. Online, in Store, in App?
b. Campaign engagement shows which channels are most effective for each specific customer.
There may be a customer who only responds to push notifications because all of their purchases are done in-app. Or, a customer who responds best when they are targeted with both email and SMS. The idea here is to enable the marketer to target each customer only through the most relevant channel(s) based on responses to campaigns and purchase activity that we see from each customer individually.
In conclusion: if you can take all of the data points and variables that you have as a marketer and combine them together into one holistic view of what your customers are doing now and what they did in the past, you can ensure personalization, relevance, and effectiveness of your campaigns. Oh yeah… and save a lot of money by not sending promotions when they aren’t really necessary.
August 7, 2018
Removing “Marketer-Managed” from the CDP Definition
by David Raab, CDP Institute
 Back in April 2017, I raised the question of changing the CDP Institute’s definition of CDP from “marketer-managed system” to “packaged software” or something similar. My decision then was to keep “marketer-managed”, largely because the primary buyers of CDPs were marketers.
Times change and I think we're now due to make the switch. So I’ll hereby announce a tentative new definition of: “A Customer Data Platform is a packaged system that creates a persistent, unified customer database accessible by other systems.”
The main reason for the change is that over the past year I’ve consistently found myself substituting “packaged system” for “marketer-managed” when I try to explain CDP. “Packaged system” relates more closely than “marketer-managed” to the prebuilt components and configurability that give CDP its advantages over custom development. The contrast with custom-built systems is the purpose for that portion of the definition.
“Marketer-managed” has also created some misperceptions that have led to unnecessary criticisms of CDPs. These include:
- “marketer-managed” means CDPs are used only by marketers but customer data should be shared throughout the organization. In practice, even a marketer-managed CDP could be used by any department. But there’s nothing inherent in CDP technology that means it needs to be managed by marketers in particular or even by business users (as opposed to IT) in general. CDPs are already sometimes bought by other groups, including both business departments and IT. And the CDP’s core ability to share its data means it’s just as easy to connect non-marketing systems as marketing systems. (Well, almost: CDPs rely on prebuilt connectors to simplify deployment. CDP vendors have built most of their connectors to marketing systems since that’s what their clients commonly want. But, again, there’s nothing to prevent anyone from building connectors to other systems as needed.)
- "marketer-managed” means IT is not involved. This was never possible because IT always controls access to corporate data sources and destination systems. Beyond that, IT is always involved in CDP purchase decisions, at least as group that must approve any choice. But IT usually has a role beyond those bare minimums because it’s the group that best understands company data and is thus best placed to help CDP vendors use it effectively. It’s true that CDPs reduce the technical work needed to deploy unified database (a benefit of being a “packaged system”) and that the CDP vendor’s own technical team or partners often take on much of the remaining labor. But using the CDP to make an end run around corporate IT was never an option, even if anyone wanted it to be.
The change also reinforces a point that’s increasingly important: the distinction between Customer Data Platforms as a class of system (“CDP-as-a-product”) and customer data platforms as a component in a corporate IT architecture (“CDP-as-a-function”). CDP products are stand-alone packaged systems that are one way to build a unified customer database. Other ways include enterprise data warehouses and modules within integrated software suites or marketing clouds. The distinction matters because each approach has advantages and disadvantages and buyers can compare only these if they recognize the difference exists. As developments such as privacy regulations and digital transformation make corporate IT groups increasingly interested in unified customer data, there will be more alternatives to consider and more need to understand how these compare with CDP products. Removing “marketer-managed” from the definition will also help corporate IT departments realize that CDP products are an option they should consider.
The main disadvantage of the change is that it obscures the focus of CDP on marketing applications. It’s not just that CDPs are still primarily bought by marketers. In addition, many CDP vendors have designed their systems with marketing-specific capabilities such as segmentation, predictive analytics, and message selection. Indeed, most of the industry’s recent growth has come from vendors with integrated marketing features. So you might think the definition should be made more marketing-focused, not less. Or, that we should replace “marketer-managed” with a combination such as “marketer-oriented packaged system”.
That’s tempting but I think it would be a mistake. Plenty of software used by marketers isn’t marketer-specific: for example, predictive analytics and business intelligence tools are used by many departments. Other software that definitely is marketer-specific, such as marketing automation or recommendation systems, doesn’t explicitly reference marketers in its definition. In practice, marketers will look at the use cases and features associated with a product and they’ll recognize which CDPs that are aimed at them. The core function of the CDP is creating that unified customer database, which is something that most marketers easily recognize they need without being told it has marketing applications. Vendors who extend that value with marketing features such as personalization need to state that anyway, since those are not core CDP features. In short, putting something like “marketer-oriented” in the definition would add little value and might do harm by reintroducing the misperceptions we’re trying to prevent by removing “marketer-managed” in the first place. So it’s best to leave it out.
This still leaves open the question of exactly what should replace “marketer-managed”. Since the goal is to present a contrast against “custom-built”, the obvious choice seems to be “packaged software”. But that seems to imply on-premises installation, while many CDPs are delivered as a service. So “packaged system” appears to be a better choice.
Some people have suggested that “packaged” itself is problematic because it suggests end-user deployment, which again gives the false impression that IT isn’t involved. A better term might be “productized”, which is how many developers describe a system that started as a custom project and was later converted into something reusable. But “productized system” seems rather vague and “productized product” is just plain silly. “Productized solution” might be a viable alternative, since it implies a contrasts with “custom solution” (to my ears at least). But it’s pretty vague as well.
“Commercial off-the-shelf software” is an older industry term to distinguish custom software from packaged systems. But we don’t hear it much these days and shortening it to “commercial system” might suggest an unintended contrast against “open source system”. “Off-the-shelf” sounds even more packaged than “packaged” so forget that. Other suggestions have included “standalone product” or “dedicated product”. Those are accurate but shift the focus to the fact that CDPs are bought separately from other systems, which isn’t the main point we’re trying to convey.
Given all these considerations, I currently see “packaged system” or “productized solution” as the leading contenders. Of the two, I favor “packaged system” as sounding less like jargon. But I’m still open to alternatives. Please share your thoughts on the subject.
August 2, 2018
The Mechanics of Predicting Customer Churn Series: When the Business Follows a Subscription Model
By Andrew Malinow, PhD and Mimoza Marko, Zylotech
Customer churn is a typical dynamic in any business – for one reason or another, a customer who has previously purchased from a company, no longer purchases. However, to surface potential causes for churn that can inform mitigation activities, we need a more operational definition.
Churn can be defined in several different ways. If a business uses a subscription model (e.g., Netflix, Amazon Prime membership), churn can be defined as those customers who have cancelled their subscription. A subscription cancellation typically exists as an explicit field in a database (cancelled_subscription=True), or it may need to be derived in some way. In either case, there is a specific event, that is either explicitly captured, or can easily be derived from existing data points, that provides the definition for churn.
However, if a business does not use a subscription model, the definition of churn must be derived based on a change in the customer’s transactional behavior (purchases) over a certain amount of time.
There are various methods that can be used to predict churn. When there is a subscription model being utilized, we have a specific indicator available for our analysis – we can ‘label’ those who have cancelled their subscription – the analysis is relatively straightforward. The first step is to take a sample of internal customer data and split into two groups – those who have churned, and those who have not. A number of Machine Learning models (e.g., such as Logistic Regression, Random Forest, or Naïve Baysian) can then be ‘trained’ to learn which ‘features’ are most predictive that someone is likely to churn. A feature represents a piece of information that we know about a customer. Examples of features include age, gender, geographic location, and marital status. A hypothetical analysis might indicate for example, that for Netflix, geographic location is highly predictive that someone will churn, and that zip codes along the Gulf coast are most highly correlated with churn. A possible explanation could be that weather-related issues negatively impacted streaming services in those areas, causing many people to cancel their subscriptions.
So far, we’ve described how to predict customer churn for businesses that have a subscription model, where the definition of “churn” is straightforward – a subscription canceled equals a customer churned. Now let’s look at how to predict customer churn for businesses that do not rely on subscriptions.
The absence of an explicit churn ‘label’ in our data adds an additional level of computational complexity to the analysis – specifically there is need to develop a mechanism to define “churn” (rather than inherit it directly from an existing data element). To this end, we will leverage information about customers’ transactional behaviors to provide us with a definition for churn that we can use for building our model.
The first and most important step in building a model that will accurately predict propensity (likelihood) for a customer to churn is to assign each customer a label indicating whether the customer has churned or not based on their historical transaction data. Since there is not a specific field in the data that indicates if a customer in the database is still a buyer or not, we need to focus on the customer’s purchasing behavior. The most recent six or twelve months of transactions (depending on how much historical data you have access to) should be left out of the initial analysis and model development process and used to test, validate and refine the accuracy of the predictions produced by an initial model.
The remaining transaction data (purchases) will tell each customer’s “story” – specifically, the frequency of purchases and the time interval between purchases. Analyzing this behavior mathematically can be used for a definition for churn of that particular customer. Additionally, we can generate new features regarding customer’s buying attitude that might be helpful in predicting their point of churn in time.
An example of such a feature might be the buying frequency (e.g. customer buys once every 45 days). At the end of this process, the specific event which determines the label will be a statement regarding the frequency of purchases (e.g. "a customer has churned if there are no purchases during the last 45 days"). It is important to switch focus from company churn definition to individual customer level. Doing so will result in higher probability of assigning the right label.
Once we have divided the customers into churned and not churned, we can begin training Machine Learning Classification Models (like Logistic Regression, Random Forest, etc.) and follow the same process as for businesses that follow a subscription model. To evaluate the accuracy of the model, we use the transaction data that we set aside in the first step of our analysis, which allows us to see if the customers we predicted to churn (or not churn) have made any purchases. Finally, the selected model will give us the importance of each feature included in it as a coefficient score, which we can use to determine which piece of information about a customer – either given or derived – is more influential in predicting the churn.
Now let’s focus on how to ‘tune’ your churn definition after building a preliminary model.
When building any Classification or Predictive model, there are always multiple iterations – throughout each, we “tune” the model based on its performance on training data. All of the steps that we take, from segmenting the data, to feature engineering, building the model, and then evaluating the model, are typically repeated several times. For our churn model, it is critical to retrospectively evaluate model performance for each phase of development and identify things that can be modified to improve model accuracy.
The accuracy of a model is simply the ratio of the correct predictions to the total number of cases that have been evaluated. However, to improve model accuracy we need to find where the model missed and why it missed – did it predict someone would churn, but they did not? Or, more importantly for our model, did it predict that someone would not churn, but they did? By rigorously interrogating our model, the data will tell us the missing parts of the story and suggest ways to improve our model.
In churn modeling the first thing we need to check is the misclassified customers, specifically the ones that we were not able to “catch” before they churned. These cases are critical since the purpose of predicting churn is to have this information while there’s still time to do something – customer retention is easier and less costly for a business than the acquisition of new customers.
When we circle back and evaluate how we built the model, we need to look for the unseen – the reason underneath the churning of those customers the model predicted to have a low probability to churn. Performing cluster analysis (e.g. K-Means, Hierarchical Clustering, DBSCAN, etc.) will help us find common patterns for this customer group. It is likely that we will see at least some of the reasons they churned. For example, they might be “irregular” buyers with orders placed in wide and unpredictable time intervals. Therefore, either the mean frequency (e.g. mean frequency = customer buys every 45 days) alone might not be enough to identify the churning point, or it should be calculated differently for these types of customers. Another scenario might be that these customers had bad product experiences or unsatisfactory service. For example, if a group of customers who bought the same product churned, it is likely that customers who recently bought it could be at high risk of churning regardless of their normal buying frequency. Including the data that captures this information into the model will improve the quality of the model.
On the other hand, there are customers that were predicted to leave but didn’t. Although the previous ones are more important to prevent revenue loss, these customers are indeed loyal to the company. Retaining loyal customers is essential. Therefore, a business would not want to target them with aggressive campaigns or too many emails. It would be a poor allocation of resources and even worse, these customers might not like frequent contact at that point, and choose to disengage (e.g. not purchase) in the future. Consequently, loyal customers might need a more flexible calculation for their buying frequency that is leveraged by our churn model.
As we see, there are various aspects to be considered when we investigate customer purchase behavior. While much of this information is noise and complex models should be avoided, we must capture the most important elements in the churn definition.
All these kinds of findings are useful input for model improvement. We go back to the very first step to modify the definition of churn for each customer and assign the zero and one labels again. Then, as in the first time around, we train and evaluate the best model. Finally, we conclude that constant observation and improvement is the key to learn and predict customers’ behavior better. The more we know, the higher the probability is of building a powerful predictive model.
July 30, 2018
How Marketers Can Combine Cross-Device Identification with a CDP to Enhance Their Campaigns
By Anthony Botibol, BlueVenn
It’s indisputable that every customer experience, across every device, should be personalized in real-time! Whilst it once may have seemed to be something of a distant dream for many marketers, personalized interactions across multiple devices is a reality with cross-device identification and a Customer Data Platform.
What is cross-device identification?
These days, marketers are not just looking to connect devices together – they’re looking to manage identity across all platforms and focus on the customer, not on the device. Cross-device identification achieves this by bridging the gaps between a customer’s devices.
For example, let’s say that you are a clothing retailer and a customer is using their smartphone to access your website to browse a pair of shoes. Whilst they may not purchase the item there and then, they may opt in to your mailing list. The next morning they are using their laptop in a coffee shop. Using cross-device identification, the customer will automatically return to the exact same place they were on the website the night before, encouraging them to place an order for the shoes they were looking at.
How does cross-device identification work?
Cross-device identification works by combining data from media companies, publishers, ad exchanges and third party solutions. An identity graph is then built from these data sources and assigns each user a universal ID. This ID links all of the devices, websites and apps used by that user and connects all of the touchpoints within your customer journey. This provides the foundation you need to create a unified, seamless customer experience that is tailored to that specific individual.
However, it is important to note that there are two types of cross-device identification:
·Deterministic identification - This involves recognizing personally identifiable information such as an email address that is used to log in to various apps and websites. This authentication is seen as the more reliable type of cross-device identification.
·Probabilistic identification - This relies on algorithms rather than authentication and tracks a multitude of anonymous data points connected to different digital elements such as screen resolution, device type, Wi-Fi network, operating system and location. However, because these do not contain personally identifiable information, probabilistic identification, whilst more scalable, is less accurate than deterministic.
How does cross-device identification benefit marketers?
Cross-device identification enables marketers to use the data provided by customers to identify and target them across all channels and devices, thereby moving towards a more omnichannel customer experience. For example, a marketer will have the ability to match a smartphone, tablet, laptop, and smart TV to a specific individual as well the information that shows how this customer navigates their way between websites and their purchase journey.
Marketers can activate the insight and information that cross-device identification brings by using it to enhance data in a Customer Data Platform (CDP). Just like other additional data feeds used for enhancements, this improves segmentation and customer profiling capabilities. And when you consider that the majority of website visitors don’t convert, it is highly valuable for identifying consumers.
Why a Customer Data Platform is essential for cross-device identification
A CDP works by blending online and offline data, bringing the information contained in disparate data silos to give you clean, consolidated information. Whilst the real-time information gleaned from cross-device identification can be invaluable to any marketer, it must be stored and handled carefully if it is to be used effectively. You can feed your cross-device identification data into your CDP which enhances what you already know, but will also give you an unparalleled insight to who is accessing your websites or apps, and device preferences.
Cross-device identification will continue to evolve and take centre stage as we enter an age of connected devices and the Internet of Things (IoT). It is in the best interests of every marketer to ensure they know exactly who their audience is in order to execute highly effective campaigns that speak to them personally. As time and technology progress, more and more marketing strategies will revolve around connected devices, meaning that a Customer Data Platform is essential if you’re looking to future-proof your business for upcoming marketing trends and technologies.
July 23, 2018
Foreign Exposure: Reaching the Unreachable Customers
By Moshe Demri, Optimove
 The countless ways the world has changed over recent years has totally modified customers’ habits, making them very hard to influence. But the same changes in our world make it impossible for our consumers to be completely anonymous.
It’s just impossible to keep track. The changes are so vast, and they’re affecting every aspect of our daily lives. They cover all fields and industries: manufacturing, medicine, agriculture, automotive, corporate services, energy efficiency or digital economy. The use of deep learning technologies, big data analysis, cloud development, cyber security and many more, has brought a dazzling number of inventions onto our fingertips, and nothing will look the same again.
And the rhythm is gonna get you. Every year tops and doubles the previous one. 20 years ago when Deep Blue beat Garry Kasparov, the world saw an explosion of ideas that touched and changed our habits, day-to-day routines, caused rifts in our attention and behaviors until the point of no return. From the autonomous car to shoes that tie themselves, from the bionic leg and artificial skin to smart umbrellas. This list is just a drop in the bucket, and it is almost too difficult to imagine the forthcoming drops.
One of the more expected result of this era is the rapid change in customer habits. What was done on Sunday becomes outdated on Monday, and where people went during the summer will wind up completely deserted when winter arrives. One of the clear demonstrations for this phenomenon is this startling study by Omnicom Media Group Agency - Hearts and Science published this week: 47% of adults aged 22 to 45 are consuming absolutely no content via traditional TV platforms. Less than one-third of the TV and video watched by people in this age group (Gen X and millennials) is accounted for by traditional measurements. The rest is being watched on smartphones and other channels like Apple TV and Roku.
That’s not to say that the subjects aren’t watching TV content or viewing ads anymore, but the research suggests that the various ways we consume content has changed, which makes it much harder to reach these groups. These days, effectively reaching customers is a significant obstacle.
Anonymous? No such thing
There are multiple ways to connect with the most aloof customers. Whether it’s scratching the surface layer with a chisel to expose the hieroglyphics or scanning the depths of an ocean for the remains of a sunken ship. If Bigfoot does exist, and you’re probing for his footprint in the Cascades snow, just open your laptop. He would have probably left a digital footprint by now.
Customers can no longer be anonymous. A highly personalized approach through multi-channels, which work across every stage of each customer’s journey, will maximize the marketer’s ability to reach those who seem unreachable. Marketers should reach their customers where they are, and much of users’ screen time is spent on Facebook, Twitter, or surfing the Web. The fact that you have customer’s email address doesn’t mean much if that same customer spends hours every day on Twitter. In addition, consumers today have much more control over the buying process than marketers do. As a result, marketers must constantly develop and coordinate highly orchestrated touch points and micro-campaigns that fluidly span multiple channels in a way that the customer finds meaningful and trustworthy. Keep in mind that different types of messages work better over different channels, and what fits an email, won’t automatically translate well for SMS.
Mix and Match
Below are some approaches for using multi-channel marketing to improve campaign results. There are countless others of course, and that is where creativity and resourcefulness come into play.
Sequential Messaging Across Channels – Run campaigns that present a sequence of messages that tell a story across multiple channels. For example, we can use the 97% open rate of SMS messages to deliver a short teaser regarding a subsequent campaign message via email, and thus dramatically increase the open rates of that email when it comes half an hour later.
Message Blast– Send the same offer on every channel at once. This works best for a great time-limited offer that is tailored to a small segment of customers.
Channel Competition – Run campaigns with the same message/call to action via different channels, to different random segments of a target group to see which ones work best. Known as A/B/N testing, this approach can reveal which types of messages work best on specific channels for certain customer segments.
The Pros and Cons
Using all available channels comes with a price. In the table below, you can see the relative strengths and weaknesses of some commonly-used channels, according to these attributes:
Cost – how expensive is it to communicate with each customer through this channel?
Attention – how effective is this channel at grabbing the customer’s attention?
Design – how much flexibility and creativity does this channel provide when crafting the communications?
Annoying – how invasive or irritating do customers perceive this channel to be?
In conclusion - In the ecosystem described in this article, the ever-changing-dynamic era, marketers are required to closely analyze customer behavior, loyalty and retention. They need to communicate with their customers wisely, in all sorts of ways and tools according to their audience’s particular preferences.
Using different, less traditional channels, and combining them with more recent platforms, is really the right path to success. Examining consumer’s level of receptiveness for various communications, not only guarantees you won’t leave a customer behind, but also provides them with a tremendous amount of added value in terms of content, offers, and messages, delivered exactly where they're at.
July 5, 2018
A Touch on The Creative Side: Why It’s Too Soon to Fear AI Capabilities
By Pini Yakuel, Optimove
In one of his last public appearances, Prof. Stephen Hawking expressed real fear that AI improvements will lead to world chaos. But as AI capabilities are still very much limited, it's up to mankind to start harnessing its benefits to our favor. Are we capable of doing so?
In one of his last public appearances, Prof. Stephen Hawking, who passed away in March, expressed a real fear of the possibility that AI will bring disaster upon mankind. In a speech during the Lisbon Web Summit last November, Hawking stated that well-harnessed AI abilities could lead to the end of poverty, eradicate diseases and even stop the damage we’ve inflicted upon Planet Earth. But in his mind, it depended too heavily on people who hadn’t exactly proven themselves in the past. “AI could develop a will of its own," Hawking said in his signature computer-generated voice. “The rise of AI could be the worst or the best thing that has happened to humanity.”
It’s not the first time that scientists have tried to protect the universe from itself. But Hawking wasn’t talking about possible risks a generation or two away – it’s a matter of decades. Despite the notion that AI is on the fast track to rapid evolution, this is not the case. We’re talking about a much slower and more premeditated pace of development. Is this a breakthrough? By all means, yes, but not the kind that is going to change our lives on a large scale any time soon.
Unlike environmental issues, where the impact of near-term solutions on future generations is more concrete – such as reducing the pace of climate change or reducing our over-consumption of red meat and fossil fuels – horror scenarios involving AI originate in places where imagination is high, such as sci-fi movies and books. A brilliant New Yorker cover portrayed a homeless man lying in a New York street surrounded by robots and a cute robo-dog. High impact, definitely, but still science fiction. No less, no more.
The even spookier story around AI – and don’t get AI confused with automation or even ‘computers’ – is about its effect on the labor market. It’s important to stress that this ‘automation’ started ages ago and will continue to happen on a much larger scale as a natural part of the technological revolution. 200 years ago there were cotton pickers, and 50 years ago there were milkmen, and today everyone is a computer engineer. The ‘robots’ are already here. AI and its derivatives will open more job opportunities, while other professions will vanish altogether – perhaps cashiers and taxi drivers will be the first to go. But we’re not talking about a large-scale unemployment crisis for mankind.
The main issue surrounding the development of AI is the fact that computers still don’t have the capability to imagine, to guess, to create. They simply don’t address the creative side of things. Computers can’t build a narrative. Computers don’t even have the intuition of a three-year-old.
Imagination and intuition are at the core of our unique power as humans. In this context – when machine learning scientists are dealing with these generalization abilities – there are mathematical proofs. The most famous is Rice’s Theorem, which expresses the idea that there are absolute limitations on what a machine can learn.
Humanity has disappointed itself many times throughout history. Do the atomic bombs that are well kept in unknown shelters around the world, which many hold responsible to the relative peace in the last decades, say otherwise? Will we know how to channel these massive capabilities to our benefit? We can already see evil forces trying to take advantage of AI’s many shortcomings, and as more decisions and calls will be made by machines, the will of these forces will grow. Another heavy load on the shoulders of mankind. Like we haven’t had enough of that already.
July 2, 2018
How to Design Your Data Supply Chain for GDPR
By Julie Graham, Tealium
While the dawn of massive change approaches with GDPR, it’s critical for brands to be transparent and ethical with the customer data they’re handling. They must ensure that the data being collected is only used for the purposes of transforming interactions and creating better customer experiences.
And as brands scramble to orchestrate and execute their compliance plans before May 25th, many are unsure which tools, processes, internal groups and roles their organization should employ. Sound familiar?
Ted Sfikas, Director of US Engineering at Tealium & Jason Koo, Developer Evangelist at Tealium, recently did a webinar on “How To Design Your Data Supply Chain For GDPR.”
Watch the on-demand webinar and learn:
- 4 critical components of GDPR that every brand needs to know
- 3 key GDPR roles every company should assign internally
- Best-in-class solutions to top mobile data challenges with GDPR
- Real-world examples of how employing data privacy efforts has led other brands to increased customer access to privacy controls and increased transparency to 3rd party tools
It’s crucial that data, marketing and analyst professionals focus on overall data governance, rather than just legal ramifications, and the effect that leakage and security events can have on the brand itself when it comes to GDPR compliance.
Watch the on-demand webinar and safeguard your brand and consumer trust by ensuring your compliance today!
June 25, 2018
What To Do When You Don’t Have A Data Layer
By Ty Gavin, Tealium
Tag Management requires a little effort up front, but the returns are worth it. The main bit of work is adding a clean data layer into your web page or app. But what if you can’t? You’ve developed a large set of static content pages and updating each individual page is next to impossible. Or you need to get a data layer into your content but have no one to assist. (Has your entire web development team just decided to use their unlimited vacation days?) Tealium provides a way to make possible what was previously impossible.
History
The Hosted Data Layer (HDL) feature provided with Tealium iQ was originally called Data Layer Enrichment (DLE). Recently, the Tealium visitor service that provides visitor-specific Data Layer Enrichment was updated to be called “DLE.” Although they both are similar in that they will effectively bring data from an external source (CDN or service) to the data layer on the page. The main difference is that while DLE retrieves visitor-specific data, the HDL is designed to retrieve data about anything except a visitor.
Architecture
The HDL is composed of both an API to upload JSON data and a set of .js files hosted on a public-facing CDN location for download and execution. In addition to hosting JavaScript files, the feature is currently being expanded to provide hosting for JSON-format files as well. The reason JavaScript (.js) files are used is to allow for all browsers including legacy Internet Explorer to retrieve the data. This is done using JSONP. Modern browsers can directly retrieve JSON object from a third party domain. With support for JSON format, we expect Progressive Web Apps or Google AMP HTML configuration directives can be hosed in JSON format and provide additional flexibility to our clients. Regardless of how the data is retrieved from Tealium’s CDNs, it is uploaded in JSON format using Tealium’s API.
Web Page Usage
Tealium iQ provides an Extension to configure how/when a web page will retrieve (execute) an HDL JavaScript file to add data to the web page. In the case of retrieving product data, the extension can be configured to add product detail data based on the “product_sku” value currently showing in the visitor’s web browser. This could be additional metadata about a product that might not be available on the page or might not be easy to get to on the page. Once retrieved, this information becomes a part of the data layer and can be passed along to a tag vendor or even used as conditions to load additional tags for that specific product.
The Tealium iQ Extension is called “Hosted Data Layer.” The typical use case on a web page would be to configure this extension with a variable that contains the “MD5” string of the current URL. That means it will lookup/retrieve all the additional data for this URL in one request. This allows for easy ‘deployment’ of Tealium iQ on a site with many static web pages and not a lot of developer resources to update each and every web page with a data layer. Best practice is to only request the minimum number of files per page to reduce HTTP requests and increase the likelihood of tracking with your tag vendors. The more data files to retrieve, the more delays are added to tracking calls that want to fire with the data.
Single Page Apps Usage
While the HDL feature was originally designed for the use case of a website with many static and difficult-to-modify pages, this feature can also be leveraged on Single Page Apps. For this scenario, we recommend that you do not use the Tealium iQ Extension. Instead, create a wrapper function to retrieve the data form HDL service before calling utag tracking functions (utag.view or utag.link.) This allows for your app developer to decide what is required and what is optional data. Required data should “block” (retrieve data and call callback) before executing utag.link or utag.view. Optional data can be asynchronously requested.
For example, if you have currency conversion data, you might want to retrieve that data in order to fire a purchase event with the adjusted amount at the time of the transaction. However, you may also want to pull data around general stock market shifts. This data is interesting, but not required. You would like to send the recent stock trend data to your analytics provider, but it will be OK to send that information on the 2nd or 3rd tracking event in your app.
Choosing to retrieve some data asynchronously allows for increased tracking (firing pixels sooner) and increased information (additional data is eventually retrieved, but user experience takes precedence.)
Conclusion
Tealium’s best practice is to place the data your tag vendors need in your data layer in the page. During your implementation process, the Deployment Engineering team will ensure you have the best data layer in the business. This provides the most powerful, intuitive, and performant solution. However, the Hosted Data Layer feature provides a framework for those cases where this is either technically not possible or just not practical. Many enterprise clients have complex implementations or internal processes.
We’re looking forward to some additional applications of HDL with added support for JSON files hosting. This allows for CORS support out of the box.
We’d love to hear about how you’re using this feature!
June 18, 2018
Refining the Marketers’ Aptitude: Jumping over the Second Purchase Hurdle
By Eilon Morgenstern, Optimove
Lowering the ratio of our one-timers should be a key approach in every company’s marketing strategy. This DIY will help you optimize your business and increase your KPIs.
We all like to think of ourselves as spontaneous creatures, casual, cool, easy like Sunday morning. The kind of people that can hop on a plane at the spur of the moment. Well, that’s optimistic. First try to order your lunch half an hour later than usual, and maybe take it from there?
You get the point. We are, regardless of how much we don’t want to be, predictable beings. For us marketers, that’s a comfort. And an advantage, that we must know how to use in our favor, like in the case of the most important purchase of all – the second one.
The Door is Already Open
In most e-commerce brands, the majority of resources are spent on bringing in new and additional traffic and only afterwards they invest in converting the existing leads. Succeeding in converting the lead to a customer is great, but oftentimes we are stuck with about 70-90% of first time customers who never make it to a second order. That’s a huge waste of potential.
One of our tools to measure true growth and true retention is to lower our one timer rate by growing our second timer rate. And think about it – it should be easy; these customers already like us, they already spent money on our brand. All we need to do is improve our personalized marketing outreach, that is based on their unique behavior, and we can significantly increase our second timer rate.
In this blog we’ll teach you a simple DIY method to help turn these one timers into “more timers”. We’ll learn how to do that by analyzing the patterns of our returning customers and categorizing the relevant marketing campaigns to send to your one timers, based on our knowledge of existing customers.
Every company has their own way of characterizing their products. If we know what characterized a specific first order that led to a second order, we can better understand how to personalize campaigns to first buyers with similar characteristics, that have yet to make a second order. Let’s begin:
Step #1 – Collecting
This DIY process can be applied to various types of companies. In our example we use a fictitious online clothing store.
For this analysis, we will collect data from all customers who made at least two purchases in the last year. The data needed from the first order is: department, category, brand, product name. For example: Shoes, boots, Timberland, light heritage cashmere flat boots.
We need the same data from the second order as well: department, category, brand, product name.
If a customer ordered more than 1 item in either one of the orders, we will create a line for each order, and in part 2 we’ll compare each item from the first order to each item from the second order.
Step #2 – Comparing
For each characteristic, we will create an Excel chart. For each customer, we’ll define 2 columns (column B and C in the example below) in each chart: The first column will be the first order department (B), and the second column will be the second order department (C). In the fourth column (D), enter the formula = IF(B2=C2,1,0)
In this fourth column we’ll see the result of “1” if the value of the first order is equal to the value of the second order. Otherwise, we’ll get the result of “0”. See this example below for the department characteristic:
Step #3 – Schema
As we mentioned above, we’ll create an Excel chart for each characteristic. Then, we’ll divide the total in column D (Is second order equal) by the number of rows in the specific chart, and that answer will be the percentage of customers who have ordered twice from the same characteristic.
Step #4 – Characterizing
For this step, we’ll take a look at the percentage result we got in column D (above) for each characteristic. If the result is between 40%-60%, we’ll recommend ignoring this characteristic when we begin creating our campaigns, since the results aren’t significant enough to create an action plan that will definitively have an effect on the correlation between the first and second order.
Now, we’ll be able to analyze the results we got from the 4 characteristics (department, category, brand, item) and understand the behavior of our customers during their second order. In accordance with our results, we’ll personalize the campaigns we send to customers who have yet to make a second purchase.
Here are a few examples:
Department = 80%
Category = 70%
Brand = 40%
Item = 20%
In this example, we’ll send a campaign with items from the same department and category as their first order, but we’ll recommend different brands and items.
Department = 40%
Category = 30%
Brand = 85%
Item = 10%
In this example, we would send a campaign with items from the same brand, but different categories.
Going the Extra Mile
After mastering that, we suggest you start drilling down into the numbers and charts. Here are two more vital ways to use the data:
1. If your company offers a wide variety of departments and items, we recommend doing this process within the department. For example: Choosing the woodwork department and noticing the category characteristic yields a 15% result, which is quite low, or going with the Gardening department, where the category is 85%. According to this example, we’ll send different kinds of campaigns for those who have purchased woodworking and those who have purchased gardening in their first purchase.
2. Regardless of the items your company offers, we recommend taking the process a few steps forward by creating sub-characteristics that will be able to give you a more granular perspective. For example: Analyzing first and second purchases for customers by looking at their geographical location, sex, age, or other attributes, that would help us have a deeper insight, and converting more first-time customers into second purchasers. Here's a good demonstration:
In Conclusion
With Optimove’s vast experience across many ecommerce brands, we’ve been able to understand the importance of learning more about the behaviors of your current customers in order to better approach your new customers. This blog examined a simple way to practically understand the behaviors of your second timers in order to convert more first timers. Applying this method and evolving alongside it will help turn your one timers into loyal, lasting customers. Want to know more? Don't hesitate to give us a call.
June 8, 2018
Interpreting Customer’s Loyalty: There’s Definitely A Logic to Human Behavior
By Maya Shaanan, Optimove
The world of retail shopping is changing rapidly and with it the level of loyalty of your customers. See what are the changes you should expect, and how to deal with them.
 These days, consumerism is not only an inseparable part of our lives, it’s an essence within it. The modern way of living and the vast developments we’ve experienced in the last decade make purchasing an item as easy as reaching your hand forward and tapping once with your index finger.
As the assortment grows and the possibilities are endless, the competition raises. It’s a race for survival for every brand. And the purchasing habits of the new generations – the millennials and even generation Z, whose oldest representatives are now in their twenties – are making sure it will be even harder. When you have tons of brands and products to choose from, and trends are changing all the time, it is quite clear why the issue of customer loyalty is becoming more and more crucial for companies, why they are fighting for every customers, trying to keep them content and happy.
In the following analysis, we tested some of the changes that occurred in recent years in customers’ behavior and what these changes mean to a retail company that wants to keep as many loyal customers as possible. The analysis was based on data gathered from 6 brands with more than 7 million customers.
Number of one-timers
The most basic thing from which we can learn about the current state of customers’ loyalty is the rise of one-timers. One-Timers are customers who bought only once and didn’t return to make another purchase. In order to compare between the different years, we looked at the percent of customers who bought only one time in the six months after their first order, out of all the customers who bought that year. It’s easy to notice a trend – the percent of customers who bought only once in this time period goes up with the years.
More than that, the uplift between 2014 to 2017 is 13%. That's a significant number. Another way to notice on this graph is to realize that in 2014 we had 30.6% - almost a third – of customers who bought more than once, while in 2017 it went down to less than a quarter - 22.8%.
The chart below drills down with this data. The left columns show the percent of customers that bought 2 times (all in the six months after their first order), the columns in the middle are customers who bought 3 times, and the right columns show the percent of customers who bought 4 times and more. All, again, by the years.
From this table we can learn how customers have a much higher chance to keep buying from a brand after the first purchase, not only a second time but more than that. This strengthens the point of the previous chart we presented – customer loyalty is decreasing. Even the most loyal customers, who have bought 4 times or more, aren’t maintaining their loyalty – a decrease from 6.6% in 2014 to 4.5% in 2017.
Abandoned Shopping Carts
Another good indicator for customers’ loyalty is the customer’s commitment to proceed to actually making a purchase and not just browse the site. The chart below shows the percent of customers who abandoned their shopping cart, meaning they visited the site and the ‘check cart’ page, but then left and did not buy. The percent displayed here is the number of customers who visited their shopping cart but didn’t proceed to complete an order, divided by all the customers who visited the cart page. The customers are divided into age groups, as the age factor is crucial to understanding purchasing power.
Again, the trend is as clear as can be. In the older segment (Baby Boomers), the chance to abandon the shopping cart is the lowest, and for the younger generation (Gen Z) the chances are the highest. The difference between the two is 20%. There are many reasons for this gap, some from the field of behavioral psychology – from simply the fact that younger people are more indecisive due to the amount of data and options they are familiar with compared to older generations. Another way to interpret this is that the older customers are used to a different, “old time” behavior, when showing your intent to buy really meant something. If someone puts an item into his shopping cart he intends to buy, in compare to the modern way of going into a company’s site to check the options, see what’s new, what’s in style, and then move on to the next site without buying. This is another explanation to how today’s customers and their shopping habits make it harder for companies to maintain customer loyalty.
Survival Rate
Customers’ survival rate represents the number of customers that made at least one purchase in the months following their previous order. In the graph below, the numbers on the horizontal axis are the months after the customer’s first order, and the percentage on the vertical axis is the percent of customers who purchased (out of the all the customers who made a first order one month prior).
For example, in 2015, 11.3% of customers made a purchase one month after their first order. This number decreased to 8.5% in 2017. When looking at the time span of six months after the first order, the gap between the years is smaller: in 2015, 3.7% returned to buy after six months, compared to 2.2% in 2017. We see that the trend is quite similar in different years, and it makes sense since the chance of making another order a month after our first order is higher than making an order six months after the first one. But here we also see a consistent gap between the years: each year has a lower chance of having returning customers – the survival rate has gone down.
Nowadays, companies have to work harder to gain customer loyalty. As these indicators we presented here show a clear decrease, the companies are suffering by receiving a much lower share of their customers' wallets. The growing competition, not only in terms of the products themselves but also in terms of buying experience and AI solutions around to make the buying easier and more enjoyable, promises that share of wallet will decrease.
The point here is to make marketers and retailers more aware of these facts and learn how to overcome this obstacle. Companies need to be more attentive to their customers, monitor their actions closely, use campaigns wisely, use more emotional intelligence and basically try harder to get their customers to come back and keep buying.
So, in conclusion, what should you do?
Learn who your customers are - A few things companies can do to keep their customers loyal is making them feel special and treating each customer differently so that the customer won’t be tempted to look in other places, a “we have exactly what you need” strategy. Birthday campaigns, as old as they sound, are usually effective in making the customer feel special and know that you are a company that cares.
Use promotions and offers wisely - Companies tend to give special discounts to new customers to try and make them come back, but returning customers should be rewarded as well, and it is important they are aware of the fact that they are treated differently than others due to their loyalty. One way to do it is to give your loyal customers a special discount – for example, if you are having a big sale, let your loyal customers get the discount one day before the rest, and have “dibs” on the items they want without fearing those items go out of stock. Another way is to give them special offers using promo codes and letting them know they got this discount thanks to their loyalty.
Address their problems - Another example is a customer that is unsatisfied with the order or service he got. It is crucial that the company is aware of that, and is doing their best to handle this customer carefully, and resolves the issue for potential future customers as well.
Find their rhythm – A company must find the right way to communicate with the customer. The first question we need to ask is 'when is the right time to contact the customer?' - what time of day, what day of the week and what frequency. It might be every two weeks and maybe once every three months, depending on the customers' activity. The second question is what channel: some customers never open emails and prefer text messages, others will think text messages are too “pushy”.
June 5, 2018
For Hospitality Brands, Becoming Customer-Centric is Becoming Guest-Centric
By Amy Cross, NGDATA
Hospitality  marketing departments face many challenges: a lot of processes are still following a slow batch rhythm, there is a lot of data fragmentation (data silos), guest data is often created and owned separately by marketing, customer service, physical retail and sales departments, guests’ usage of digital channels is growing, and will increase even more in the future with the rise of ever more devices.
Those digital touch points produce a wealth of behavioral data, making the challenge to connect all the data even bigger. But, at the same time, the loyalty of guests is harder to earn, and there is very little time to learn about the guest, find out what he or she is interested in and to react to opportunities or risks in a consistent, timely and relevant manner. Improving the guest experience and being able to deliver new types of integrated services on top of that will be the key differentiators in the coming years.
The “integrated resort” is on the rise in popularity for the complete and multi-faceted experience it offers its guests. This kind of resort is the epitome of convenience and matching expectations – something people want for their valuable breaks from everyday reality. Everything you need is under one roof, steps away from your room. Restaurants, shows, casinos, recreation, spa services and retail shopping – guests don’t have to leave the facilities to have an all-inclusive experience.
With the combination of activities and full services, your resort can collect customer data in all sorts and forms: What are they spending money on? Do they favor room service or dining in the restaurants? Who are night owls vs. early risers? Do they prefer pampering or fitness sessions? Do they plan their activities in advanced or more spur of the moment? What are the trends in how, when and from which devices they conduct their bookings?
Because of the abundance of guest data, marketing and customer experience departments in large hospitality brands face some important challenges:
- There’s a lot of data fragmentation (data silos) since guest data is often created and owned separately by reservation systems, marketing, customer service, physical retail and sales departments
- Many campaign processes are still following a slow batch rhythm, which are sent to broad segments with impersonal and irrelevant offers and messages
- Guests’ usage of digital channels is growing, and will increase even more with the use of various devices
Guests are becoming more demanding of quality service and their loyalty is harder to earn, forcing you to react to customer opportunities or risks very quickly. The more you know about your guests and their history with you and all your properties, the better you can serve them. This knowledge is not concentrated in one channel (e.g. the online channel, the mobile channel, the call center…) but should be based on insights gathered across all channels and devices.
Improving the guest experience and delivering new types of integrated services will be key differentiators in the years to come. It’s time to take advantage of all the data that you have on your customers, from all sources. You need to connect and engage with each and every guest, based on all of the intelligence that your brand collects, and put it into action.
For further interest:
May 25, 2018
Keep Your Losers Close but Your Winners Closer
By Omer Liss, Optimove
In the gaming industry, companies tend to focus their attention on players who produce most of the revenue: the biggest losers. A new Optimove research tested what happened on the second side of the scale – the players who won big – and uncovered how important they are.
We all know that 80%-20% pareto principle - 20% of customers will produce 80% of the income. This magic occurs also in the gaming industry - the benchmark shows that the top 5% of a brand's players will produce 60% of the brand's income. These so called 'VIPs' are the focus for most brands - trying to figure out how we can increase their number, and how we can get them to lose even more.
This way of thinking is right for many reasons. These big losers are the core of our income and we should invest more in this segment to make it grow. These players usually have high retention rate - they are willing to spend a lot of money on our brand and in addition they are loyal and visit us often.
Many companies tend to create marketing plans and strategies to retain the players who generate a lot of revenue with a variety of bonuses and offers, but forget, or even exclude, players who win big and generate negative net revenue. And the question we want to answer in the article is should we even treat our winners? And if so – how? We analyzed the behaviors of ‘winning’ players from 12 leading gaming brands across Europe during 2017 to come up with answers.
Winner, loser, everybody
Smart marketers already recognized the importance of understanding their players’ experience with them. First, in order to identify a good or bad experience, thresholds are being defined – e.g. players who won more than X amount are winners and players who lost more than Y are losers. Or, in another example, using measures such as payout ratio - the total bet amount a customer won divided by the total bet amount that customer deposited. i.e. If a customer won $50 and deposited $100, his or her payout ratio is 50/100=0.5. As the number is lower the players is more of a ‘loser’. Now, the rationale behind compensating a loser make sense, but why should we give something in addition to our winners?
So, we checked. The first piece of our research shows the impact of new players experience during their first days since their initial deposit and analyze their payout ratio and their survival through the next days.
The pie charts above show that players with better payout-based-experiences have much more chances to be retained as active players - therefore, though it makes sense to compensate our losers, we can learn that we have a strong base to engage with our winning players and come out with a successful retention plan around them.
Real Impact
Let’s dive deeper. Take for instance the average gaming company who wants to build a VIP CRM plan. First, it needs to define who these VIPs are. In many cases, a threshold is being defined (again) by deposit amount or revenue to flag the most valuable players. In our analysis, we didn't set up a fixed threshold, due to variance between brands, but flagged the top 5% of the players who generated the most revenue in each brand as VIPs.
At first, we checked how many of those 5% of players will remain at the top of the players list after several months. The results showed that VIPs had a very high retention rate as VIPs – 43% of them reached the top 5% again after just a month, 58% were back on the leader board after two months and 65% achieved the 5% threshold after three months.
We thought it might be useful to run the same analysis with a minor change. Now our starting point would be players who reached the lowest point of the pareto chart – the lowest 5% of players who generated the most negative net revenue of the business. With the help of this analysis, we would be able to understand what the chances are of a lucky/skilled winning customer to end up as a VIP:
From the bar chart above we can learn that our most negative players in terms of revenue, have a high chance to make it into the top 5%. It lays at 18% after a month and reaches 30% after only three months. What does it mean? That many of your big winners will end up as your big losers nevertheless.
As we pointed out, many companies exclude these kinds of segments from their marketing plans and communication strategies. But in order to have a continued communication with our VIPs we must understand that some of them, sometimes, will win big. And also, on that note, that some of these winners will end up as losers who generate big revenues for your company. It’s important to understand this fluctuation. Here a good example, looking on that chart below: 13% of our top 5% will downgrade into the bottom 5% in just one month.
The recognition that our bottom 5% is made up of former VIPs and future VIPs will help us treat this segment with a higher respect (and with more generous offers).
The contribution
Another method that can help us understand the importance of our big winners is the net revenue that they generate. As we understand, the net revenue of the bottom 5% is the most negative it can get on each period – but what happens next? Dose this segment holds negative value after another period of time?
To answer this question, we defined our bottom 5% in net revenue in 2 weeks. This group, in most of the cases, is responsible for a very low distribution of the total net revenue of the company as it holds most of the companies' winners. (The revenue distribution is of course negative as we are dividing the negative net revenue of the group by the total positive net revenue of the business).
After segmenting this group, we are analyzing the net revenue from it on the next period (the following 2 weeks) and again, calculating the net revenue distribution of this group among the entire business net revenue. Surprisingly, the results show that not only does the former bottom 5% group generate a positive net revenue, but that it is equal to 40% of what the former top 5% generated in the following period.
We must mention that there are still players who generated negative net revenue in the bottom 5% group at the following period, but as a group, it overcame the players wining by the additional losses.
Wrap up
The entire industry relies on players losses which drive the most important KPI - the net revenue. Nonetheless, when building CRM plans and communication flows, companies tend to forget that sometimes, our players win, and, in many cases, it is not a bad thing. Players wining drives higher retention, higher engagement with the product, reliability in the platform and at the end of the day - generate positive net revenue in the long run.
Creating funnels of communications with our winning players is not less important than target players who generate net revenue. The momentary negative value from a customer shouldn’t drives estrangement but vice a versa - a tap on the shoulder can cause positive emotions among our winning players - sharing the fact that even when they take some money from us - we are happy for them.
May 23, 2018
The Data Layer: A Catalyst For Digital Transformation
By Julie Graham, Tealium
Global heads of technology and marketing know that having access to high quality customer data is the key to making strategic business decisions. Data collection affects everything a brand does – from customer experience, applications, reporting, retention, conversions and more. And having a solid and strategic data layer in place has downstream benefits for it all and truly becomes the catalyst for overall business transformation.
Jay Calavas, CIO at Tealium and Nick Bennett, President at Napkyn Analytics, recently did a webinar on “The Data Layer: A Catalyst For Digital Transformation”.
Watch the on-demand webinar and learn how to standardize data collection by way of a data layer as well as see the 6 key ways to use a data layer to drive digital transformation and evolve your martech stack.
Top best practices and example benefits provided under each of the key ways during the webinar included:
Governance & Data Control
- Best Practice: Enforce the use of the data layer for access to all real time data required for martech. Use the data layer as a real time test case against expected values, in real time or in aggregate, for better data governance and control.
- Example Benefit: Organizations that use a data layer for this purpose can benefit from a significantly faster pixel deployment time.
Internal & External Policy Compliance
- Best Practice: Use standard naming conventions to identify the sensitivity of data in a data layer, and track the exposure of specified data within user sessions.
- Example Benefit: Lockdown sensitive information about revenue or the customer to prevent it from leaking to third party marketing.
Infrastructure Transformation
- Best Practice: Use the data layer before and after a digital transformation as a self-documented test scenario and for success criteria.
- Example Benefit: Launch a brand new website (front and back end) with zero concern for data quality, and for understanding improvements – i.e did this make conversion rates better?
Watch the on-demand webinar and digitally transform your martech stack today!
May 21, 2018
3 Obstacles to Maximizing the Value of Customer Data
By Buck Webb, RedPoint Global
 The true value of customer data lies in its ability to provide context. This can only occur when that data is connected across silos. Unfortunately, 85 percent of marketers are unable to unify online and offline customer data, according to a study by Conversant. Further, only 15 percent of marketers in the study are confident that they know their customers well based on unified online and offline behavioral and purchase data.
This lack of customer knowledge blocks marketers from reaching their full potential in terms of personalizing communications and driving revenue. Additionally, having extensive data locked inside functional and system-specific silos hampers productivity. Only 51 percent of marketers polled by Forbes Insights say they frequently make sufficient use of data in their marketing decisions. Of those, 59 percent say their marketing initiatives exceed their goals.
To draw the full value that customer data can provide, marketers must resolve three central challenges: data access, data analytics, and data activation. Doing so will create a connected data value chain – a direct path from connected customer data to increases in marketing performance and revenue.
Challenge #1 – Data Access
Businesses have an overabundance of data stored in myriad siloed systems. To unlock the value of that data, marketers need more than just access to it in its existing silos. They need connected data that will provide the context necessary to optimize customer outreach, and they need access to that connected data in real time so they can meet, and even surpass, customers’ expectations. Currently, just 24 percent of marketers use real-time customer activity to tailor their digital marketing, according to the Conversant study.
Most companies need their many data siloes to support specific functional areas. So, creating one massive store for customer data is rarely the ideal solution. Instead, data-savvy companies use technologies that bridge those siloes to create a holistic view of the customer. These technologies pull data from relevant sources based on the specific information or insight marketers need, when they need it.
As helpful as those tools may potentially be, they’re only useful if marketers can access them directly for connected, real-time insights. It’s essential to find tools built not just for data scientists and data analysts, but also for business users. Sixty-three (63) percent of marketers polled in the Forbes Insights study say that access to connected data provides them with greater insight into the customer experience across channels, which, in turn, allows them to craft strategies that convert this understanding into positive results.
An equally important aspect of data access is data quality. Marketers should have access to data that is complete, consistent, and current. This starts with creating systems and processes that block bad data from getting into their systems in the first place. It also includes repairing existing data by cleansing, appending, and deduping it. And, it necessitates data governance.
Challenge #2 – Data Analytics
Once marketers have access to connected, real-time data, they need the know-how and tools to conduct the right analyses. In some cases, these analyses should be predictive (e.g., what are customers likely to do). According to Forrester Consulting, 54 percent of marketers polled have adopted predictive and real-time analytics to optimize next best actions.
In other cases, the analyses are descriptive (e.g., measuring campaign performance). Currently, according to the Conversant study, only 18 percent of marketers capture “actionable measurement” to support their digital marketing goals; just 35 percent measure online and offline sales and 34 percent measure the impact of each channel independently, based on click data.
Yes, there are times when marketers should hand the reins to a data scientist or analyst. But there are also times when marketers can get the most value from data by having the ability to analyze the relevant data themselves – especially when they need to get and act on insight in real time.
Getting the most from data analytics means that marketers can use their connected data to resolve identities, and view items such as conditions, variables, and events that give context beyond basic customer profiles. It’s this context that allows marketers to improve the results of their predictive analyses, support their prescriptive analyses, and optimize their marketing performance.
Marketers need access to tools that provide them with access to connected, real-time data if they are to conduct the types of contextual analyses that will enable them to squeeze the maximum value from their customer data. But, according to the Conversant study, 75 percent of marketers don’t have the technology, or don’t use it effectively enough, to deliver one-to-one digital marketing.
Challenge #3 – Data Activation
Consumers want information in an instant, but that doesn’t mean their next action is a purchase. With the buyer’s journey looking more like the path of a bumble bee than that of a jet liner, marketers need to be able to respond to customers’ interests and behaviors in the moment. Only access to connected, real-time data can enable this in a way that will allow marketers to deliver the optimal, contextually relevant message at the best time to individual consumers through their preferred interaction channel.
Unfortunately, nearly half of marketers (46 percent) struggle to understand customers’ interactions across channels, according to Forrester Consulting. Yet, 44 percent say that responding to customer interactions with speed and relevance is critical to the future success of their marketing programs.
Marketers aiming to capitalize on the consumer’s moment of need – and, in the process, get the most value from their customer data – require processes and technologies that can support a real-time approach to marketing. Marketers need to rethink their processes, including adding more automation, so they are structured to respond to customers’ actions in real time. And, they need technologies that not only provide a holistic view of customers, but also enable business users to conduct the analyses they need to act in real time. However, only 49 percent of marketers have adopted technologies that “activate customers with in-line decisioning at digital touchpoints,” the Forrester Consulting study finds.
The Connected Data Value Chain
Marketers who clear these three hurdles – data access, analytics, and activation – are the ones who can create a connected data value chain. Business-user-friendly technology, clean data, and agile processes are essential links in that value chain. Without them, marketers lack access to the contextual data that enables them to meet customers at their moment of need.
Being where customers are with contextually relevant messaging is the optimal way to maximize the value of customer data. Yet, only 36 percent of marketers say that generating customer insights through analytics and data mining is critical to the future success of their marketing campaigns, according to the Forrester Consulting study. This presents an opportunity for data-savvy marketers who maximize the value of their connected customer data to outperform their competitors. Now is the time to get connected.
May 18, 2018
Rob Glickman to Marketers: You’re in the Customer Experience Business—And the Responsible Use of Data is Key
By Rob Glickman, Treasure Data
In late April, thousands descended on Silicon Valley to take part in the annual MarTech West Conference. Featuring an impressive lineup of keynote speakers and presenters, attendees spent three days networking, getting the scoop on the latest trends, and learning how to effectively integrate the latest technologies into marketing and business operations.
Data was the key theme throughout the conference, as marketers continue to wrestle with the exploding MarTech landscape, now at over 6,800 solutions. The Treasure Data team was on-hand interacting with customers, prospects, media and influencers around the topic of helping marketers delight their customers through the responsible use of data across the journey. And our very own Chief Marketing Officer Rob Glickman presented as part of the “Discover MarTech Theater” track.
In his spotlight performance, Rob covered the turbulent and largely unexplored ocean of data, imploding trust and what GDPR means for marketers. But perhaps his most central message was simple: Marketers need to remember they’re in the business of creating experiences—and customer data holds the key.
Getting Back to the “One”
According to Gartner, there will be 20 billion connected devices globally by 2020—all collecting data. And as time goes on that number will only continue to rise, creating a greater deluge of data and making it more difficult to get back to what Rob says really matters.
“The only number that matters is the number that has always mattered before any [devices] existed,” Rob explained. “One.”
“The one person you’re trying to influence at any given time … the one decision your customer needs to make … or the one initiative that will make you more successful,” he continued. “We all want to get back to the one.”
And now more than ever, sifting through exploding data signals to get to the one decision you need to make can be the difference between success and failure.
Of course, all that largely unexplored data—most of it sitting across or streaming into multiple systems—holds key insights to help marketers get back to their “one.” The ocean of data is a turbulent, unrelenting environment that serves to create confusion, not clarity. So the challenge is to pull actionable insights that create memorable, personalized experiences using the right data, at the right time, while not abusing consumer trust is the new imperative for marketers.
Why? Because yes, consumers want personalized, customized experiences that delight and engage, but not at any cost. And as Rob said: “You’re in the business of experience … and the respectful use of data creates great experiences.”
Connecting the Data Dots
We’re at a watershed moment as the tension between heightened trust and delivering ever more personalized experiences come to a head in 2018.
Get insights and tips to help you walk this fine line by checking out our new eBook, Connecting the Customer Data Dots: How Marketers Can Unlock Insights to Transform the Customer Experience. The piece features insights from data-driven marketers and customer experience experts Shep Hyken, Sameer Khan, Tamara McCleary, Ronald van Loon, Chris Penn and Bob Egan.
Check out Rob’s full presentation in this Facebook Live video.
May 16, 2018
Are Customer Data Platforms an Engagement Silver Bullet?
By George Corugedo, RedPoint Global
The modern customer can appear in any channel at any time. Because consumers can show up anywhere, brands need to have a presence everywhere. More than that, this always-on brand presence needs to be consistent so the customer receives the same experience no matter where they engage.
Many brands have failed to deliver the necessary consistency in their customer experience. Not from a lack of ability, but because they face substantial challenges to achieving their goals. These take the shape of organizational roadblocks from internal departments who are loathe to cede any “turf,” and technical barriers arising from the organic growth of marketing technology stacks – and resulting data silos – over the past three decades.
Customer data platforms (CDPs) arose to counteract the technology fragmentation that prevents brands from fully understanding their customers’ needs, behaviors, preferences, and intents. CDPs are a new type of operational data environment that ingests an enterprise’s data from all sources and provides an always-on, always updating golden record that is continually available at low latency to all touchpoints and users across the enterprise.
With the rise of CDPs came the inevitable idea that the platform could solve all the problems facing customer engagement professionals. In doing so, deploying a CDP would simplify engaging the omnichannel consumer and allow brands to meet customers where they are with contextually relevant messaging. But is this true?
How a Customer Data Platform Works
Customer engagement technology stacks have evolved organically over the past three decades, with brands adding new point solutions in reaction to consumer trends. Brands adopted email marketing solutions in droves in the 1990s, and social media management platforms went through a similar upsurge in popularity in the early 2000s.
Each of these new point solutions collected and stored data differently, including using distinct identifiers and varying levels of structure. Because the underlying data structures are different, these solutions tend to not “talk” to each other or share data. The lack of interoperability in legacy point solutions means brands risk sending the same message to the same person through different channels. If this happens enough times, consumers are apt to switch to a competitor who can better understand the relevant context.
A customer data platform eliminates that problem. CDPs are designed to unify customer data of varying sizes, types, and cadences through a combination of processing rules and analytics to craft a single, composite view of the customer and operationalize it. Customer data platform users gain visibility into the entire interaction history of a customer across all their point solutions, which means that they can see when a customer interacted via social media alongside their response to a recent email, etc.
More importantly, customer data platforms function as a single point of data control, empowering business users with the ability to directly access and act on customer data in the moment of need. This can be transformative, enabling business units to react with the next best offer at the speed of the customer.
Customer Data Platforms Can’t Do It All
At its core, a customer data platform operationalizes the insight that brands collect on their customers. By operationalizing customer data, business users can deliver contextually relevant messaging at the moment of need – meeting customer expectations for a personalized experience. The brands that successfully personalize their marketing stand to benefit from an $800 billion revenue shift over the next five years, according to Boston Consulting Group, which will accrue to the top 15 percent of companies.
Providing a centralized window into all that is knowable about customers and operationalizing it is only the first step. To truly make the CDP valuable, it needs to be tied into an intelligent orchestration engine that enables you to act on your unified data and deliver contextually aware messaging to the right customer at the right time through the right touchpoint. The intelligent orchestration engine, which we call a customer engagement hub (CEH), leverages the single view of the customer created within a CDP to deliver on customer expectations. The ability of a customer engagement hub to deliver contextual interactions to customers is powerful, especially given that Frost & Sullivan recently found that companies lose $300 billion annually from poor customer experience.
The Customer Engagement Silver Bullet?
So, are CDPs the “silver bullet” that marketers have waited for? In a marketplace facing digital disruption, which McKinsey predicted has already shaved 45 percent off incumbents’ revenue and 35 percent off their pre-tax earnings, it is imperative that you demolish the barriers keeping data captive in functional and channel-specific silos to survive and thrive.
But deploying a CDP alone will not be enough. You need intelligent orchestration capabilities to make the unified customer profile actionable and deliver relevant messaging to consumers in the moment of need. Without the ability to act on the unified data, then you miss the opportunity to deliver relevant interactions to your increasingly omnichannel customer base. CDPs may not be a silver bullet for customer engagement, but they are certainly a vital piece of your brand’s success.
May 14, 2018
Increasing Customer Engagement and Insight with Mobile Location Data
By Julie Graham, Tealium
The average adult is estimated to have spent almost two hours a day on their smartphone last year, and by 2020 there will be 20.4 billion connected devices in use, up from 8.4 billion in 2017. And what’s crazy is that predictions are stating by 2020 personal devices will know more about an individual’s emotional state than their family does.
From the always-on modern consumer, a new type of data has emerged – mobile-derived location data – and it’s expected to grow 14% over the next two years.
Business and technology leaders are now seeing how imperative it is to understand how to leverage this mobile location data, in real-time, to alter engagement and garner greater customer insight. But how can brands create thumb-stopping experiences with this real-time, contextual and passive behavior?
David Morris, Director of Solutions Consulting, EMEA at Tealium, recently did a webinar on “Increasing Customer Engagement And Insights With Mobile Location Tracking.”
Watch the on-demand webinar and learn how to take advantage of mobile-location data, and more specifically:
- Fundamentals of mobile location tracking
- How consumer attitudes towards privacy are driving new data collection
- Differences in data insight between cookies and mobile location tracking
- Tips and strategies for optimizing your brands mobile channel(s)
Start increasing customer engagement and insights with your mobile location tracking data today!
May 11, 2018
Four Ways to Prepare for the Upcoming GDPR
By Colleen Conti, SessionM
 The General Data Protection Regulation (GDPR) is a hot topic. Going into effect May 25, 2018 in the European Union, the regulation will greatly change the way companies must handle the personal data they collect. In May, consumers will have substantial and powerful control over how organizations collect and use information about them, with potentially massive penalties for violation. The severity of this regulation should not be ignored or taken lightly for any brand, including US brands with a global footprint.
What is it exactly?
The GDPR replaces the Data Protection Directive and as a “Regulation” rather than a “Directive” the GDPR creates a single overarching set of rules to be implemented by all EU member states. The GDPR gives consumers the right to access and change the way companies are collecting data about them. The major components are:
- Right of Data Access and Right to Data Portability: When asked, a company must be able to confirm whether it is collecting personal data and provide that data to the consumer for viewing and/or in a portable format.
- Right to Data Rectification: A consumer may request the rectification of inaccurate or incomplete data.
- Right to be Forgotten: A brand must be able to quickly forget a consumer and confirm that data has been forgotten and will not be processed any longer.
- Right to Restrict Processing: A consumer can ask to have processing of their data halted.
- Right to Withdraw Consent: A consumer has the right to request only specific forms of contact from a brand - and can withdraw consent if it has previously been given.
So, how can you prepare?
The first step is to identify how and where personal data is collected and used. Personal Data often resides in multiple silos, such as your data warehouse, transaction data from POS systems, web and ecommerce activity and transactions, etc. It is also important to recognize how these systems share data with other systems and/or partners.
Spread awareness. The more aware your entire organization is of the GDPR, the easier it will be to have a combined and well-executed plan of attack. There’s a good chance that Personal Data is spread throughout many systems and may create an incomplete picture of each customer. As a result, it will take time to unify all data you’ve collected about each customer. This may spread through multiple departments who separately collect data about customers: for example - customer service representatives may have different information about the same customer as the loyalty and marketing teams.
Designate a data protection officer. Determine if the GDPR requires that your organization assign someone (or a team of people) the role of a Data Protection Officer. It is important to have this role to take responsibility to ensure your organization is prepared and executing a plan to be ready for the GDPR and for implementing a sound, lasting privacy program.
Unify customer data in one place for quick access. Customer data platforms (CDP) are in the hype phase - and for good reason. A CDP allows you to continue using systems you already have -- ecommerce platform, social tools, data warehouses, email service providers, point of sale systems -- by fitting into your current tech stack and acting as one central place where this data lives. When all information you’ve collected about a customer lives in one 360 degree profile, handling data privacy related requests becomes a manageable and documented process. The SessionM Platform will be releasing clear, powerful functionality which will allow brands to take action on GDPR requests quickly and effectively.
An added bonus - with a CDP, data is not locked away and managed by the IT department. With a sound CDP, the data protection officer can have real-time access to collected data on a client when a specific request comes through.
May 9, 2018
3 Reasons Brands Need a Customer Data Platform Right Now
By Steve Zisk, RedPoint Global
 Customer data platforms (CDPs) are designed for one purpose: to unify customer data across functional and channel-specific data silos into an always-on, always processing customer profile available throughout the enterprise. Many IT departments have had this capability for decades, but CDPs add the twist of making the unified data easily available to business users.
This accessibility is a massive departure from traditional data management tools. With a CDP, business users such as marketing teams can access customer data in real time without putting in a request to the IT department. This ability to access data at the moment of need is transformative. With a CDP, marketing can more easily target consumers because they don’t need to wait for IT to pull the necessary data. More than that, business users in service and sales can easily see relevant information that could help them close a sale or respond to a service issue.
Data accessibility is just one of the reasons brands need a CDP. Three other reasons are that CDPs:
- Break down data silos. Customer data platforms unify data from multiple point solutions into a central portal. This eliminates functional and channel-specific data silos that have developed organically over time as new point solutions were added to the marketing technology stack. Because CDPs break down data silos, they enable access to information across the organization and streamline the day-to-day work of customer engagement professionals.
- Provide visibility into non-linear customer journeys. The modern customer journey is dynamic, flowing from channel to channel and back again in a dizzying array of steps that may not end in a purchase for months – if ever. Because the journey is dynamic, interacting with the customer through multiple disconnected point solutions won’t provide a full picture of the purchase journey. The ability of CDPs to provide a complete, coherent view gives the marketer a complete picture of all customer behaviors, preferences, and interactions with the brand.
- Enable contextually relevant interactions. Customers expect a one-to-one engagement experience with their favorite brands, and don’t care about the complexity involved in delivering this experience. A customer data platform provides the single point of data control that’s required to personalize interactions across channels, providing the right customer information at the right time to drive personalized customer engagement based on data-driven insights.
The modern, connected customer has upended the traditional dynamic between brands and consumers. Brands now find themselves seeking ways to meet customers through their preferred channels with relevant messaging. Customer data platforms are foundational to achieving this goal. The insight into consumer behavior across engagement touchpoints of a CDP empowers marketing with the intelligence they need to personalize interactions and provide relevant messaging in a crowded field.
May 7, 2018
Do I Have Enough Data to Optimize My Campaigns?
By Tal Kedar, Optimove
 Is there a minimal amount of data required for campaign optimization? We have a definitive answer.
And that answer is always yes; an affirmative, absolute and positive yes – regardless of how much data is available.
To best see why, consider Pequod Inc., a supplier of fresh organic, locally sourced whale milk and dairy products. The marketing team sent out two email variants last week, promoting their new premium white whale crème fraiche. Each variant was sent to a hundred clients. Five of the clients who received variant A made a purchase, versus seven who received variant B.
There’s no conclusive winner here. Assuming a non-informative prior, the conversion rate posterior for variant A is plotted below in yellow, with its mode – the most likely value for the underlying conversion rate – close to 5%. Variant B is in blue, with its mode close to 7%. With so little data available, many other possible conversion rates are also likely, so both distributions are quite broad.
This large variance in the inferred underlying conversion rates has a devastating effect on the precision of the difference between the two variants, namely B’s performance minus A’s, which comes out way too wide for deciding on a winner:
Clearly, even fairly low credible intervals include both sides of zero in this example; similarly, alternative hypotheses - that variant A outperforms B or that the underlying conversion rates are actually identical - cannot be rejected at any reasonable significance level.
Nevertheless, the probability that variant B outperforms A clocks in at over 72% (the area under the curve to the right of the dashed line at zero). Ignoring this fact altogether means Pequod will sell less of their lovely cream when the next batch of emails gets sent. Looking at the campaign and the variants as a multi-armed bandit problem, a simple strategy - Thompson sampling - presents itself as a natural way to strike a balance between exploiting the fact that variant B seems to perform better than variant A, versus exploring the possibility that, given some more data, variant A will prove itself superior. In this case, the expected improvement to the overall conversion rate of the campaign is a very respectable 7.4% when compared to continuing a 50-50 split between the variants.
Using such adaptive strategies means optimization starts long before “conclusive” results can be made. In fact, the optimization starts immediately with the launch of the campaign, and the probabilities of each variant outperforming the others are continuously updated with each new data point arriving. Even better, such strategies self-correct when variants performance changes over time, converging quickly on the new optimal split.
In other words, no matter how much data is available, one can always calculate the best next split between variants such that the expected performance is maximized throughout the lifetime of a campaign. Taking advantage of early results, neither ignoring nor overstating their uncertainty, is an easy way to boost results of any campaign - requiring no additional resources from the marketing team. So, do you have enough data to optimize your campaigns? The answer is always yes; an affirmative, absolute and positive yes.
May 4, 2018
Practical Machine Learning: The Use Case in Retail Banking
Kristen Carlson, QuickPivot
 Despite all of the buzz about machine learning and AI, it’s still relatively rare to see concrete examples of practical machine learning in action, much less driving real business outcomes.
This is not to say that progress isn’t being made. One of the most intriguing aspects of machine learning is that the algorithms are in a state of constant adjustment. In a retail setting, this means that a machine learning system becomes better at predicting customer behavior with every data point it collects; it lives in a perpetual state of trendspotting. When used to its potential, machine learning gives every single customer interaction the power to provide insight across an entire customer base.
Since the launch of Ada, our machine learning suite for real-world marketing applications, we’ve received a ton of interest from customers in putting it to use. Today, we’re sharing a new example of Ada in action.
In the notoriously fickle world of retail banking, one customer is working with us to develop five concrete models for machine learning that drive concrete business outcomes and deliver ROI. While these models rely on advanced technology, the applications are intuitive and the value is clear:
Contact Attrition Risk Model – Designed to predict the likelihood and timeframe in which a contact will close all bank accounts and cease doing business of any kind with the bank. This gives our customer the ability to adjust messages to these customers and make overall business changes accordingly.
Account Attrition Risk Model – Predicts if and when a client is on the verge of closing a given bank account, but would still maintain other banking accounts and services and continue doing business with the bank.
Next Best Product Model – This model can recommend the products or services (checking, savings, mortgage, etc.) a contact is most likely to use, which is incredibly valuable when it comes to personalizing marketing offers. The model can be programmed to get as granular as necessary, even getting down to recommending the specific type of debit card a customer should be marketed.
Loan Default Model – This model is able to identify which contacts are most likely to default on a loan or credit card payment, which has applications that go far beyond the marketing channel.
Clustering Model – This model looks at all available customer data to find hidden structures and relationships and develops contact groupings for deeper analysis. Those contact groupings are presented visually and described statistically through an unsupervised model report. With this model, a retail bank could finely tune its customer acquisition strategy around specific clusters of ‘look-alike’ customers and improve their chances in the race to win more customers. This model is also extremely valuable for customer retention.
The ultimate goal of machine learning is to produce revenue growth more efficiently and forecast more accurately. In the world of retail banking, it also has the power to enhance the overall customer experience with “just in time” communication and a personal touch, as well as provide more intelligence to the business overall.
One of the most exciting things about launching our machine learning suite is seeing how customers put it to use. The creative ways that predictive algorithms can be applied to business challenges are virtually endless, and we’ll continue to share the coolest examples on this blog.
If you’re interested in learning more about what the QuickPivot machine learning modules can do, we’d love to hear from you.
May 2, 2018
6 Key Stages to Successful Data Orchestration
By Jay Calavas, Tealium
 The concept of Data Orchestration is a new one and has yet to clearly be defined to the industry. I have my own unique view and want to provide a simple guide to achieving this goal. Data orchestration is when a brand is receiving real-time data and insights on a user no matter the device, tool or technology they may be interacting and engaging with. Data orchestration helps to solve the challenges of data fragmentation, organizational silos, technology integrations that don’t speak the same language, and ultimately, poor customer experiences.
So how does a brand looking to find solutions to those challenges implement data orchestration within their organization?
I believe there are 6 key stages to successful Data Orchestration:
- Collection
- Transformation
- Enrichment and Stitching
- Audience Association
- Action
- Ownership
Each of these stages contains some significant requirements and as we know not all technology is created equal. So let’s take a look at each stage in detail:
Data Collection
While it may seem simple on the surface this is one of the biggest Achilles heels of most technologies. Any system can put a tag on a website or upload a file but data collection is far more complicated than that. Let’s talk about where data is created. A customer might be engaging with your brand on your website, mobile app, call center, in-store, kiosk, chat, smartwatch or any number of channels. Each of these interactions in each channel results in data being created and put into silos. To check the box for this stage; you need to be able to collect data in real-time from all of those sources.
Transformation
With data being collected from all of these different sources and channels you can assume it’s not in the same format or language. As data is flowing in, we must apply transformations to the data to cleanse and blend it into a single data layer. Remember, preparation is just as important as any other stage of the process! Matching data and making it ready for the next step is essential and, without it, the rest of the process is broken.
Enrichment and Stitching
Once we have a fully correlated omnichannel data layer, we can begin creating a single view of the customer. The key is to do this in real-time at the point of data collection vs. after the fact. Doing this takes some technical savvy, but if you choose carefully, some platforms can run inflight calculations and models on inbound data and enrich the visitor profile right there and then.
Furthermore, identify resolution comes into play if we are genuinely doing this sort of real-time enrichment. Upon identification when we find a device that belongs to another profile we need to stitch them together. The key here is not just to ‘link’ profiles but to recalibrate and calculate all of the enrichments to account for everything we know about two different devices. Tricky but achievable!
Audience Association
If we have a real-time profile that is addressable for in-the-moment engagement we need to qualify this profile in and out of audiences, so we know what actions and campaigns to apply to them. In reality, this is just another level of enrichment but is also the final profile based orchestration before we turn to other tools. This isn’t a segment, it is a ‘current state’ of that profile and can dynamically change as fast as the data flows in.
Action
Any platform playing in the Data Orchestration space must have real-time integrations into the tools that you use to analyze and engage your customers. Think API’s and marketing tags to start. Number, scale and depth matter here, don’t just look at a logo slide – dig into the details.
Ownership
All of this heavy lifting should result in the syncing of this data to your Data Lake and Data Warehouse. You will get a standing ovation when you tell those teams that they are going to get a real-time feed of fully correlated cross-device, omni-channel customer data.
These 6 stages are key to successful Data Orchestration. I believe these are core competencies that remove all of the batch processes from legacy approaches to data. Real-time in the realest sense – companies are entering the golden age of data.
Wanting more information on Data Orchestration? Watch our on-demand webinar to learn more about what it is, how it helps achieve the single view of the customer, the difference between Campaign Orchestration & Data Orchestration and so much more.
April 30, 2018
Household Marketing: Finding the Decision Maker and Avoiding Blunders
By Yoni Barzilay, Optimove
 One of the principles of one-to-one relationship marketing is understanding the individual customer entity that you are interacting with. Defining the customer entity is important not only for the sake of outreach, but also for accurate reporting and analysis.
Nowadays, marketers are putting a stronger emphasis on personalization than ever before. And the simplest form of personalization – using the first name in an email template (i.e. “Hi Rob,”) – might prove tricky or even completely wrong in certain types of businesses.
In financial services businesses, the question of classifying “who the customer is?” is rarely straightforward. Should they consider each individual contact as a customer? Should they group individuals under a single household? This issue may also be relevant for subscription businesses (e.g. does a household need more than one Netflix or Blue Apron account?), for certain retailers such as online supermarkets, and for companies with a B2B business model. These companies may have several contacts for each of their clients, and it’s a common challenge to identify the decision maker within the organization.
You Can Never be Too Insured
Marketers might prefer a wider outreach within a given household, to increase the exposure of their marketing content to more relevant individuals, thus increasing the probability of response. In financial services, a wider outreach is especially important since the “circle of influence” plays an active role in the decision-making process. According to research conducted by McKinsey & Co., two-thirds of the touch points during the active-evaluation phase involve consumer-driven marketing activities, such as word-of-mouth recommendations from friends and family.
Although the size and shape of the average households has changed throughout the years, households with multiple individuals are still dominant and should be therefore addressed as a whole. According to the latest U.S. Census, households of more than one-person account for more than 70% of all households. Family members, who are participants of this circle of influence, may have a significant role in the financial decision making of the household, especially when it comes to choosing products such as insurance or banking.
The major caveat with this approach begins after the fact, in the assessment and analysis of the marketing campaign.
Here is an example to clarify:
- A marketer at Y.B Insurance Inc. sends out an email to 10 potential prospects.
- Out of these prospects, Rob & Jenny Johnson are married and live in the same house.
- Rob & Jenny decide to request a quote for a home insurance together.
- The marketer at Y.B Insurance Inc. decides to finalize the campaign and analyze the results.
- He sees that 2 out of the 10 prospects responded and requested a quote. A 20% response rate is a great result.
In reality, this 20% response rate figure is misleading since Rob & Jenny are responsible for themselves as a single household, thus – 11% response (1 of 9).
The campaign analysis would potentially be even more skewed if the marketer would have excluded a control group to measure uplift. If Jenny would have been in the test group and Rob in the control group, the results would be completely distorted.
In the example above, the analysis would have produced a 50% response rate for the control group and a 12.5% response rate for the test group.
Targeting a single individual in a household may also prove to be problematic. Who is the decision-maker? Who is the influencer? What if one individual is the primary contact for a life insurance while their spouse is the decision maker for the auto insurance?
Household marketing may also be effective for reducing redundant ad spend. For example, if someone in a household has already purchased a home-insurance policy —then based on the customer database, the marketer should stop presenting home-insurance ads to all individuals in that household. This kind of irrelevant content needs to be taken into special care in these cases, as it might have the negative effect, and influence the decision making in a wrong way.
Group Ahead
Ideally, we would suggest mixing both approaches, to consider the household as the primary entity for reporting and analysis, while communicating with all household participants to maximize the potential effect of the campaign. Here’s how to do it:
The first step will be to create a “single household view” of the data – aggregating different behavioral metrics on a household level (e.g. Total number of accounts – In total and per line of business – Total Spend etc.). The prerequisite of achieving this is to be able to identify the individuals of a household or workers of a company. In financial services, developing the logic for grouping individuals into households is not a simple task. These individuals may have separate accounts which may or may not share the same surname or mailing address. For B2B companies, identifying the individuals of their organizational clients is an easier task, by either requesting the company name as an input or by matching email address suffixes.
Raw data:
Single Customer View:
Single Household View:
From the illustrations above, you can understand the issue of solely maintaining a single customer view while not referencing a household view (and vice versa in some cases).
The next step would be to create the list of households who should receive the marketing communication, making sure to exclude irrelevant households based on the single household view data (e.g. excluding households with 2 life insurance policies from a life insurance campaign), and break these down to the individuals of each household.
Finally, after the campaign is sent/exposed to the individuals, the response metrics should be aggregated back into the single household view to prepare the campaign analysis.
In Conclusion
Understanding your customer entity is crucial for measuring your marketing efforts on one hand, and for building loyalty and a meaningful relationship with your customers on the other. Different messages might be relevant for specific individuals, while other messages might be holistic and prove to be valuable for the household. For example, transactional emails to renew a subscription or policy should be directed to the primary holder of the account while messaging with a marketing emphasis (such as new offerings, deals or information) will probably relevant for the household as a whole.
Marketers should first reach the awareness that there is a “household” component in their business and customer base and if so strategize their messaging accordingly. Once you know who your audience is, you will be able to measure your success in an accurate manner.
April 27, 2018
What Should You Look for in a Customer Data Platform?
By George Corugedo, RedPoint Global
 Interest in customer data platforms (CDPs) has exploded in the past five years. With good reason, because CDPs solve perhaps the most pernicious problem facing marketing: how to unify siloed customer data into an easily accessible and regularly updated golden customer record. The problem is that although interest has increased, there is a lack of clarity about what capabilities a CDP needs to be worthy of the name.
Part of this confusion is intentional. There are many solutions already in the marketplace, like CRMs and data warehouses, that can integrate and manage some portion of a customer’s data. Early definitions of a CDP called it a data management platform (DMP) plus a CRM. This definition misses the mark. The reason it does is because the definition only focuses on one part of what makes a CDP a CDP: data ingestion and unification.
The reality is that a customer data platform does far more than ingest and unify customer data. It does those things too, but the core difference between a CDP and other solutions lies in its ability to make complete and linked customer data easily accessible to the business and ever-expanding digital requirements. With analysts going on record as predicting 2018 will be the “year of the customer data platform RFP,” it’s important that companies understand what they should be looking for when they request proposals from vendors.
What a Customer Data Platform Really Is
A customer data platform is unlike other solutions on the market today. Traditional data management technologies, such as data warehouses, are backwards-looking and collect only summary data. This limits marketers and other business users in their ability to engage with consumers. CDPs are different because they ingest, clean, and link data at real-time speeds. In some cases, deploying a customer data platform can reduce the time to receive clean customer data from days down to mere minutes or seconds.
CDPs also ingest all types of data, regardless of source. What this means in practice is that a customer data platform accepts streaming and batch data equally. It also operates as a single point of data control, meaning that CRMs, data management platforms, tag management solutions, social media, and other data sources can all tie into a CDP.
This data linkage makes a customer data platform the central clearinghouse for everything that is knowable about a customer. Visibility into the entire customer lifecycle is the key for better engagement. With all the customer’s data in one place, companies are now prepared to engineer exactly the experience they want their customers to have in real time.
What Capabilities Should a Customer Data Platform Have?
For a solution to really be considered a customer data platform, it needs to have specific technical capabilities that support the growing and challenging requirements of digital engagement. Specifically, a customer data platform must:
- Ingest both streaming and batch data. Customer data platforms are designed to accept any form of data, at any pace, in any volume. This flexibility in type and volume of data is crucial to unifying data across silos.
- Manage customer data in real time. One of the most powerful aspects of a CDP is the ability to build and maintain a golden customer record at the speed of the consumer. The “golden customer record” includes every touchpoint or proxy identity the consumer presents, as well as a transactional record that includes behavioral and other triggers. A proxy identity could be a cookie, a social media handle, or even a smartwatch. A transactional record is a history of all transactions and interactions that the person represented by the proxy identity has had with the brand.
- Maintain the golden customer record. Canonical customer records are only good if they’re kept updated. Any CDP worth the name needs to have the ability to keep each centralized customer record up-to-date at any pace, whether that is in minutes, seconds, or on demand.
- Allow easy access to unified customer data. One of the key benefits of a customer data platform is accessibility. Most companies limit access to their data stores. This practice keeps data secure, but it also makes it harder for marketers and other end-users to react to customer inputs. CDPs allow business users to more readily access customer data when they need it. This accessibility is a key facet of any customer data platform.
Customer data platforms are a powerful solution to a new version of an old problem. But because CDPs are comparatively new, there is still rampant confusion in the marketplace. This is problematic because CDPs have a starring role to play in ensuring companies create transformative experiences for their customers. It’s for this reason that companies need to understand the capabilities of CDPs and how best to judge whether a solution fits their needs or not.
April 24, 2018
Customer Data Platforms Help Transcend Omnichannel Uncertainty
By Fred Maurer
 Having a Wonderful Customer Journey, Wish You Were Here
“Though this be madness, yet there is method in it.”
In his famous line from Hamlet, Shakespeare describes a situation in which the main character’s chaotic behavior conceals his true intent. With the proliferation of channels and devices today, including the growth of IoT and virtual reality, the way users engage with brands and products appears to be trending toward chaos. Marketers are tasked with understanding and leveraging a vast continuum of user behavior. Meanwhile customer journeys continue to push the boundaries of complexity. Companies are more challenged than ever to discover the intent behind omnichannel behavior and leverage it to drive measurable marketing success.
Being omnipresent for omnichannel is worth the time and effort. According to a recent Wunderman study, as we zigzag between devices and touchpoints, 87% of us measure our brand experiences against digital leaders like Netflix, Uber, and Amazon. Omnichannel users spend and engage more with brands and products both online and off-line than single channel customers. The more channels customers use, the greater their lifetime value.
The takeaway for marketers? Revenue and loyalty are increasingly won by effectively masking complexity across channels and devices, and delivering simple, streamlined, consistent, personalized customer experiences. Touchpoints where brands should be present with engaging content and messaging but aren’t can adversely impact marketing outcomes. This experience gap is a risk to effective engagement and omnichannel marketing success.
Closing the Experience Gap
How can companies understand and capitalize on omnichannel behavior when haphazard customer journeys are increasingly the new normal? Omnichannel marketing success requires a comprehensive understanding of user behavior as well as the ability to transform behavioral and demographic data into timely engagement and conversions.
To future-proof marketing effectiveness companies need to define and implement data-driven approaches to marketing that are continually informed by analytics. It’s imperative to engage users across touchpoints with timely, personalized messaging. This calls for cohesive data strategy and marketing technology solutions to help close the experience gap.
A customer data platform (CDP) can help forward thinking marketers organize their data, enhance their audience segmentation and campaign planning, increase content engagement, streamline cross-channel marketing orchestration, and optimize analytics efforts. Strategy and cohesive technology solutions are vital to long term success. With the right strategy and CDP solution, marketers can transform omnichannel complexity and uncertainty into competitive advantages and measurable results.
CDP Benefits and Advantages for Omnichannel Marketing
How can CDPs help enable marketing success in the complex, uncertain world of omnichannel?
Benefits to explore and acquire
- Open, extensible data architecture
- Flexible enterprise data integration
- Data accessibility
- Feature & function rich marketing execution
- Unified and streamlined marketing orchestration from planning through execution
- Business friendly UI/UX
- Continuous marketing process improvement
Omnichannel advantages to be gained
- Unified customer profiles with cross-channel behavioral attributes
- Identity resolution, device stitching, data enrichment
- Ease of audience analytics, precision segmentation and targeting
- Increased conversion rates from more and better engagement across channels
- Machine learning readiness (for content personalization and next-best actions)
- Seamlessly coordinated cross-channel engagement with fast activation of customer segments across channels
- Timely, contextually relevant, and optimally formatted messaging across touchpoints
- Closed loop analytics that inform segmentation and campaign strategy
- Agility and overall speed to value
- Streamlined marketing execution across the extended enterprise/partner ecosystem
- Cohesion of paid, earned, and owned marketing platforms and point solutions
- Streamlined reporting and analytics
- Rapid response to changes in user behavior across channels
- Effective data governance
About the author
Fred Maurer is a Chicago based marketing technology consultant with over twenty years of digital & data driven experience from strategy through execution. His hands-on customer data platform (CDP) experience includes business strategy, vendor platform evaluation and selection, data strategy, data integration, platform integration, business deployment, and stakeholder engagement. He can be reached at fredrick.maurer@gmail.com.
April 18, 2018
What to Consider Before Implementing Predictive Modelling
By Vasudha Khandeparkar, RedEye
 The database marketing arena has been hit with a lot of buzzwords in the past few years. Even when faced with new entrants to the arena like artificial intelligence and machine learning, predictive analytics has held its own ground.
A lot of marketers and businesses are realising that predictive analytics is a great building block to feed into automated marketing campaigns and decisioning systems. However, implementation is still proving to be a challenge. Issues with the quality of data held has led to questions about the best practices for predictive analytics implementation.
In this blog I am going to take you through the five key areas to consider before implementing a marketing campaign powered by predictive analytics.
Do I have the right predictive model?
As with any marketing campaign, predictive models should be used to help support the business case, rather than a business case being built around a model. As an example, we had a client who wanted to target the lowest value prospects in their customer database. Rather than using just the prospect conversion model, we combined this with the lifetime value (LTV) tracker. The RedEye LTV tracker predicts scores for prospects by looking at the value of customers with a similar profile. Given the range of values for prospects, it was easy to isolate those prospects with a lower than average LTV. This was then combined with the prospect model to identify the prospects who had the highest likelihood to convert. This allowed the targeting to be far more precise and the marketing campaign almost doubled the values of the target cohort.
Should I test?
Let us consider a model which tells you which of your prospects are likely to make a purchase in the next 30 days. The only thing the model is providing you is the likelihood that an individual is going to make a purchase. It does not tell you whether they need an additional marketing message, whether they need an incentive or whether they will be influenced by your social messaging over the period. There are two ways to answer those questions – use an additional model to predict the likely response to events further down the chain or the simpler one, test.
How big should my segment be?
The number of recipients in a campaign is always a sticking point, especially when you factor in control cells for testing. However, as with every new campaign plan, testing helps to optimise campaigns and identify the best performers. The potential loss in revenue from an untested change or incorrect predictive implementation is much larger than what is lost from not sending the campaign to the control set.
When splitting your campaign pot for testing, if you have a small volumes (anything less than 50,000 recipients), split your pot 50% test and 50% control. This will give you the best shot at achieving a significant different. For larger segments, you can have fewer in the control. If in doubt I recommend using a significance calculator to check if your splits are likely to give you a significant result.
How do my models need to change over time?
Any predictive model being used in production needs to change. If a prospect has made a purchase, they are now a customer and progressing round the customer lifecycle. Now they should receive scores for how likely they are to make a second purchase and be excluded from the prospect model.
A new campaign or message could change the way in which individuals interact with your brand. People also behave very differently during peak periods. If this daily and seasonal change in behaviour isn’t being considered and scores are static for many days, then it is no longer a true predictor of future behaviour.
When should I not use my predictive models?
All predictive marketing is geared to telling you to use predictive models to improve performance of you marketing campaigns. However, there are some instances where you may choose to not use the models. Let us consider Black Friday, lots of retailers have a separate microsite encouraging people to sign up to receive event specific offers. Unsubscribe rates post peak period, especially with a targeted opt in, can be almost double what they are for regular campaigns. Using a model looking at the likelihood that individuals will unsubscribe will tell you that a lot of people who have opted in over peak are likely to unsubscribe. This would then exclude these individuals from receiving the very campaigns they have signed up for.
Explore the RedEye Predictive Modeller, which directly feeds into the customer lifecycle, helping you move your prospects and customers further down the funnel.
April 13, 2018
How We Make Customer Support Seamless Using Treasure Data
By Toru Takahashi, Treasure Data
The Treasure Data enterprise Customer Data Platform (CDP) helps the digital marketer segment audiences for vastly better personalization by uniting siloed data to achieve a 360° view of the customer. Marketing teams have been able to increase their KPIs and enjoy ownership of the data platform in ways never before possible. But marketers aren’t the only ones enjoying a better life in the united states of data.
In this blog post, I introduce another use case for the Treasure Data CDP that seamlessly and efficiently improves our customer support. This is how we sync data between systems to enable the automatic triggers for support even when some data are missing and/or needs unification.
Background: Treasure Data’s Customer Support Model
Treasure Data Support uses Zendesk for support ticket management to provide Email / Chat / Support Form to our customers. (If you are our customer, you might have chatted with us before.) We are on Salesforce for customer relationship and account management.
In order to deliver great support to our customers, we want to know a requester’s background, such as their pricing plan, computing resources and subscription status quickly when we receive a request.
Furthermore, in Treasure Data, there are several customer roles defined to represent their particular deployment phase; sales, solution architect, or customer success. Our support is always available for all customers even if they are in the early stage of on-boarding with their solution architect. Thus, we handle support tickets based on a customer’s status. If you are on-boarding Treasure Data, your solution architect should know all conversations between the support team and you, our customer.
Enabling Zendesk’s Trigger for Information Sharing
Simply said, we want to do the following actions when we receive an inquiry:
- Case 1 Transfer the inquiry to the sales rep and the solution architect automatically if a user belongs to an account under contract less than 3 months.
- Case 2 Transfer an inquiry to the customer success rep automatically if a user belongs to an account near the contract renewal.
- Case 3 Alert a service level agreement (SLA) when an emergency ticket from a user belonging to specific contract plan is received.
For example, in Case 3, the trigger setting in Zendesk can be as follows:
“sfdc_mrr Greater than xxxx”.
In order to run the trigger in Zendesk based on contract information such a “sfdc_mrr” in Salesforce, we have to sync data between Zendesk and Salesforce.
Build Support KPI Dashboard
Zendesk has a built-in KPI dashboard, which is called Zendesk Insights, to see how several KPIs related to support works. But, it’s difficult to dig into KPIs associated to a user activity based on the following conditions:
- Customer’s Support Activities per MRR Basis
- Customer’s Support Activities per Sales Rep
Also, we’d like to share such a usage to the Treasure Data sales team via email.
Problem: Sync data between Zendesk, Salesforce, Chartio
In order to complete these goals, the following data sync issue between Zendesk and Salesforce is present:
- Zendesk User is based on an email basis, but SFDC is managed on an contract basis. We couldn’t link email between Zendesk User and SFDC Contract.
- Even if we use the Zendesk SFDC add-on, there were a lot of cases where the correct information could not be acquired. For example, a Contact in SFDC may not have a valid email due to an old mailing list, alias, etc.
In order to unify these data identities, we need master data, which is stored in our service database (MySQL).
But, the difficulty comes in how we can load and unify data from multiple data sources.
Data unification solutions and resolutions
We can resolve this data unification issue by using Treasure Data. Additionally, the Treasure Data Service uses Treasure Data to manage log analytics to store all service logs in Treasure Data. So, we can enrich a customer’s information anywhere with their history of service activity and contract and customer support activity and more.
Data loading from all data sources
Quite conveniently, Treasure Data has several connectors to pull data from several data sources easily. You can simply use the connectors by following these docs:
You’ll see the data on Treasure Data as the following:
– Zendesk User
Alternative way: Data loading with Embulk
If you are not our customer (unfortunately), you might feel this article is not helpful. It’s not true.
We love open source, and we love to help people who need to do data engineering in the world. In this case, we recommend Embulk (http://www.embulk.org/docs/), which is an open-source bulk data loader that helps data transfer between various databases, storages, file formats, and cloud services. Our Data Connector is also based on Embulk. And, a lot of connectors are the same source code as open source plugin.
You’ll get almost the same result using Embulk with the following plugins:
Enrich user fields in Zendesk
Now, you have all your data from multiple data sources in the Treasure Data platform.
Next, I’d like to push these data to User Fields in Zendesk. Unfortunately, for now, we don’t support a connector to export data from Treasure Data to Zendesk User Fields.
As I mentioned, Embulk supports a pluggable architecture. That means it’s easy to develop a plugin for integration to Zendesk User Fields. So, I have developed the following plugin.
With the following plugin, you can pull data from Treasure Data, and push it to Zendesk User Fields by using Embulk.
The configuration can be like this:
view raw example.yml hosted with ❤ by GitHub
Before Embulk is executed, you need to define User Fields in Zendesk.
And then, you can update the defined user field by Embulk.
Now you can get any information from SFDC and etc… into Zendesk User Fields.
Finally, you can do the following conditions with Zendesk User Fields in Zendesk Trigger!
Build a KPI dashboard on Chartio
Through the above processes, you already have support ticket information from Zendesk.
Next, Treasure Data provides connectivity to BI tools. So, we also use Treasure Data and Chartio for our data analytics platform with customer support Information. For now, we can do information sharing more easily without any special work.
The following graphs show examples of our dashboard in Chartio.
This is a support activity dashboard per quarter for the support team.
We can build these dashboards easily because all related data is stored in Treasure Data.
Conclusion
In this blog post, I introduced how the Treasure Data support team improves our internal analytics and tools. In order to integrate with several services, we use Treasure Data as the complete underlying data unification platform.
For an enterprise SaaS company, support is an important factor for growth. Having support seamlessly data-driven for our customers is critical. I hope his blog post helps you achieve similar results.
BTW, we’re a growing global team. If you’re interested in joining our support team, please check this page!
https://jobs.lever.co/treasure-data/833b2423-93df-4136-9a21-7a8cf65ca02a.
April 6, 2018
Going D2C? A Primer for Marketing
By Kristen Carlson, QuickPivot
 Brands selling direct to consumers (D2C) is not a new concept, however in the past few years we’ve seen explosive growth in this strategy. For decades, large consumer brands have executed hybrid selling strategies by operating both traditional distribution channels and direct-to-consumer channels such as ecommerce websites and self-branded stores. What has changed in the past few years though is the ease and speed in which a brand can now create a direct-to-consumer channel, and how modern logistics make it easier than ever to get a product into a consumer’s hand.
These digital and logistic advances have harkened in a new generation of disruptive brands that are changing the retail ecosystem. Although these new brands are admittedly small, they are growing at an amazing speed, while many of the legacy incumbents who have been slow to adapt are facing slowdowns in growth. Just look at the grocery business. While grocery store growth is projected to be about 1 percent annually through 2022, growth for meal-order kits is expected to grow 10x over that same period.
The advantages of D2C are stark. Not only does a company assume more control of their brand and how it’s represented, but it also reaps the instant benefit of higher margins.
Executing a D2C strategy, however, is not without its challenges. The onus is now on you, the brand, to understand your consumers, connect with them and create the best possible experience for each user, every time.
We’ve outlined the fundamental elements for any company – big or small -- considering ‘going D2C.’ These steps will prepare your marketing department to brace against the challenge, while supporting a vibrant direct-to-consumer channel.
1. Get your customer data in one place – and make sure it’s owned by marketing not IT
Even if the marketing department is well-aligned, if IT owns and manages master customer databases, the procedural hang-ups can significantly hamstring your efforts to engage with customers quickly, which is the heart and soul of D2C.
When building lists of customer segmentations is contingent on an IT ticket that takes days to fulfill, or specialized data management skillsets not typical of marketing professionals, the resulting delay makes even the most basic marketing campaigns that much more difficult to produce, and the speed required for well-oiled D2C virtually impossible.
2. Learn the difference between useful and ineffectual customer data – clear the noise
Your business most likely already has a CRM system that houses a lot of customer data, but how much of that information is useful, and organized in a practical way, for marketing to use? CRMs were built to manage and track customer transactions, and while some of this information is useful, other data fields such as SKU numbers and payment cards used, just get in the way of marketers being able to do their jobs.
Marketers looking to implement a D2C strategy need to review their entire marketing tech stack and first identify the systems that are generating customer data. From there, they should review each of the systems to pinpoint the data that can be beneficial for marketing purposes and ignore what can’t.
3. Break out of the channel mindset – your customers are not thinking in terms of channels and neither should you
When it comes to reviewing all of the systems in your marketing stack, rather than having each marketing group review the technology specific to their group, create a cross-functional team to perform this exercise and look across the entire marketing department.
To-date, many marketing teams have been organized by channel, but modern marketing teams need to tear down these silos and instead begin to think in terms of the customer journey. As a team, marketers should consider the following questions before developing their cross-channel marketing strategy:
- What are the behavioral pathways and attitudes people take at each stage of a purchase journey? Where do they get inspiration and information, and where does that flip over to actual shopping?
- What kinds of products and product attributes do customers prefer across all productlines and categories. These insights come from analyzing website touches and product-purchase history and can help you develop key preference indicators for your products.
- What are the incremental results from various types of offers?
- How does a customer behavior change during life moments (such as having a child, getting a new job, or relocating) and seasonal events? This analysis provides a better understanding for what’s underpinning purchase frequency.
4. Make sure your entire marketing team has access to the same data -- knock down the silos
Marketing teams cannot work in harmony to expose the above insights if they do not have access to the same information or the means to communicate openly among one another. Meaningful collaboration starts by spreading data access, along with appending privileges, evenly across the team.
5. Finally, future proof your technology
Technology is evolving at such a rate that in five years, a brand’s most valuable source of customer data might be a channel that we can’t even fathom right now. D2C marketers need to prepare for all types of data, data sources and data formats, and have technology in place that is able to ingest that information.
Additionally, evolving technology like machine learning can continuously analyze purchase trends and call out a wide variety of behavioral patterns. These systems can prompt the marketing team to take appropriate action, whether it means preventing a customer from going away, predicting a follow-up purchase in the near term, or target consumers that look like they’d be an ideal customer.
No individual can ever know for certain what’s coming next, and sustained D2C success hinges upon having as much information about current and future customer inclinations as possible.
Learn how a Customer Data Platform can bolster your move to D2C. Contact us and we’ll connect you with one of our CDP experts.
March 30, 2018
How Customer Data Platforms Improve the Sharing of Data within Your Business for Better Marketing
By Anthony Botibol, BlueVenn
 Businesses consist of separate departments – finance, sales, marketing, account management, and so on. In most instances, each of these departments accumulate valuable information about customers. Yet these teams rarely share the data they collect.
While this is not a deliberate act of selfishness, a failure to make data available to everyone in the company who needs it is holding you back in a number of ways. Not least marketing’s ability to communicate with customers. In fact, a report by BlueVenn found that 82% of businesses are not yet bringing multiple data sources together in a single view of the customer – meaning that the knowledge they have on their customers is not being fully utilized.
A lack of data sharing limits your potential
As different departments make use of various technologies and systems, it’s not uncommon for the customer data within them to reside in siloes. Departments cannot readily refer to the information, leading to missed opportunities for business development and improving the customer experience.
For example, say your customer service team receives a complaint from a customer who feels they have been let down by your organization. Shortly after this, your marketing department (who are unaware of this complaint) contacts that customer with a promotion. This perceived ignorance could further damage your relationship with them – and it could prove to be the final straw for that customer to stop using your service.
Say you’re a clothing retailer measuring the lifetime value of your customers based on their online transactional history. If they buy a lot of expensive items, you’ll want to single them out for special treatment, right? What if in reality they’ve returned two-thirds of those purchases in-store because they were the wrong color, wrong size or changed their mind? Any analysis you perform is going to be inaccurate without the whole picture.
Alternatively, say you’re an automotive dealer. Your finance department knows a customer is soon to repay their finance on the car your business sold them three years ago. However, does your marketing department know that this means they might be looking to trade it in for a new vehicle and now is the right time to advertise your latest offers? Did the parts department record let marketing know when customers’ annual inspections or services are due?
By centralizing and unifying all your customer data, from all your sources, a Customer Data Platform (CDP) reduces the likelihood of oversights such as this, and reveals new opportunities for better marketing communications.
Different systems, different data
In order for data to be unified effectively, it must be prepared and organized in a legible manner. Yet this can be a substantial challenge in itself, with over 40% of marketers having 21 or more sources of data.
When customer data is held across multiple siloes, it is stored in various formats that suit its purpose for that department. Product SKU codes, internal naming, different address and naming structures – there are many ways different databases might be referring to the same thing or person. A Customer Data Platform, however, takes all of this data from the different sources and brings it together where it is cleansed, standardized and unified into one, easy-to-read Single Customer View (SCV).
Speak the same language with a Customer Data Platform
While a Customer Data Platform is primarily a marketer-driven project, this unified data is valuable to many different areas of the business. Indeed, a CDP can create multiple SCVs that bring together the most useful datasets for their requirements. Importantly, the whole organization is contributing to these views, and all are working with information that is reliable and up-to-date.
As a result, your customer service team can immediately access a disgruntled customer’s individual profile generated by the Single Customer View and ensure that the marketing team does not contact them with promotional material. Your LTV analysis is more accurate. Your marketing team is sending timelier, relevant communications.
Use a Customer Data Platform to improve your department’s data sharing
Breaking down data silos and sharing business intelligence is key to improving customer engagement, building more consistent customer experiences, attaining high satisfaction scores and, ultimately, boosting your bottom line.
All customer information, from the moment they first engage with your organization to when they make a purchase, is valuable – and it should be fully utilized with a Customer Data Platform. This will ensure that important data is shared between internal teams to give every department a working, up-to-date knowledge of each customer.
March 26, 2018
Get Marketing Data Requests off Your Back(log) With a Customer Data Platform
By Rafael Flores, Treasure Data
 Today’s digital economy is a data economy – where differentiation and success depend on the insights companies glean from their data and the actions they take in response. In this data economy, one of the busiest, most demanding jobs of all is that of Data Analyst. Is this you (or someone you care about)? If so, what you need is relief. The workload for data analysts can be crushing – and any way to offload some of this workload is a welcome development.
Marketing, Again?
As you no doubt know, one of the main sources for data requests is marketing. To help your company stay ahead, your marketing team is engaged in an on-going contest to deliver better experiences, more personalization, and improved overall outcomes for your company’s customers.
Delivering great customer experiences requires access to data – whether the data comes from social media, web analytics, points of sale, loyalty apps, IoT devices or wherever. But when marketing needs to go through you and your team for the data they need, it creates a never ending stream of requests, driving up internal frustration and delaying great customer experiences. The marketing team has a business need and a data problem. They need to improve the customer experience, but their problem is lack of data access, and it just so happens that the solution to their problem is also the solution to yours.
The Enterprise Customer Data Platform
A customer data platform (CDP) brings together all the sources of data important to marketers – providing a single view of the customer across channels and interaction points. An enterprise-grade CDP provides direct access to a comprehensive set of data to marketing teams themselves.
With the mounting pressure on IT and lack of specialized resources, companies have moved toward cloud services that offer the benefits of new technologies without heavy lifting by IT. This self-service approach is the new norm. While IT needs to sign off on the approach and incorporate it into the existing infrastructure, once an enterprise CDP is up and running, the marketing team is off to the races. With intuitive data connection tools, marketing can even bring in new data sources on its own.
For Marketing and For You
For marketing, this is big news. Now with up-to-date profiles they can execute on marketing campaigns with greater precision and deliver offers that matter most. They can automate marketing tasks at scale with readily accessible customer data that is complete, secure, and reliable. And they can deliver personalized customer experiences down to the “segment of one” with detailed insights into customer history, preferences, and behavior.
But what does this all mean for you, Data Analyst? First off, it reduces your repetitive, mundane tasks dramatically. Workflow orchestration enables you to automate the flow of data for real time data delivery. While IT remains in control in terms of data security, corporate policies and governance, the self-service factor means that data pipeline chores are minimized. This effectively allows marketing to rework segmentations to their heart’s content. You now are left with more time to add value in a higher-level capacity.
More importantly, perhaps, it also turns you into the IT hero of sorts. By empowering marketing to do their jobs better and faster, you earn a place at the table as a valued business partner working to help solve problems and advance the capabilities and performance of the organization as a whole. Marketing still needs you for sophisticated analysis using machine learning and artificial intelligence. What’s more, you can use the same self-service capabilities for these analytics capabilities. Then extend the same customer data capabilities to sales, customer service, product development, and countless other departments and business units. Here at Treasure Data, we’ve seen it happen time and again. To learn more about how we can help you with an enterprise-grade CDP, ask for a demo.
March 19, 2018
The Evolution of Customer Data Management: DMP vs. CDP
By Reza Safa, Treasure Data
The question of how a customer data platform (CDP) is different from a data management platform (DMP) comes up regularly, leaving me to believe that the many blogs on this topic have missed the mark. Most blogs list the definitions or technical differences, which doesn’t really address why you should care. Then there’s the acronym issue: CRM, DMP, CDP, ABC. If you don’t live in the world of vendors working with these acronyms, keeping them all straight can be a challenge. Especially when the various vendors define each in a slightly different way and give you the same benefit of a better understanding of your customer.
For the user of the CDP or DMP, it’s about two perspectives: Scope and scale of the data and the flexibility to collect, analyze and utilize that data. For a clearer understanding, let’s look at how these systems evolved on our quest to leverage customer data.
I find it helpful to start with the customer relationship management (CRM) systems. CRMs came into being for the purpose of collecting lead, account and customer information. CRMs collect clearly defined details for 1st party data (your data) about known customers and prospects. With the development and pervasiveness of the internet, DMPs arrived for use in advertising. DMPs are generally cookie-based and use anonymous IDs for the profiles. Even as capabilities for DMPs increase, they are not able to create a single view of your prospects and customers across systems.
After a couple of decades of companies adding CRMs and DMPs to their marketing arsenal, they now have data silos everywhere. The explosion of data compounds the problem. Many companies have custom loyalty applications, point of sales systems and Internet of Things (IoT) devices. Today the customer expects companies to have a handle on all this data and to treat them as individuals based on all this data being collected. As the next solution in the data evolution, enter the enterprise CDP.
The enterprise CDP brings together all of your data sources, including CRMs, DMPs, loyalty applications, point of sale systems and IoT devices for a single, actionable view of your customer. A CDP unites customer profiles into a single personal identification number (PII). A CDP has the flexibility to collect raw, event level data without the need to predefine fields. This allows you to later query on data that you didn’t predefine.
There are more complicated definitions out there comparing features and capabilities. However as the technological features of each system expand, the differences become blurred. For clarification look to the original purpose and intent of the base data platform. An enterprise CDP was developed purposefully to deliver a single view of your customer. Data from CRMs and DMPs become unified by an enterprise CDP.
More important than understanding the differences between these systems, check out how our customers are using an enterprise CDP today. Recently we helped one of our enterprise customers by bringing together data from 21 sources, including 9 CRM systems, and creating a single profile from 8 different customer IDs. Get more examples here.
March 15, 2018
The Customer Data Platform is an Essential Component of One-to-One Personalization
By Karl Wirth, Evergage
 Imagine you’re searching for information on a martech solution for your company. After doing a little research, you land on Company A’s website. Your goal is to quickly assess whether this solution is appropriate for your needs. But you find the homepage full of marketing language you can’t quite decipher and the navigation menu cluttered. There’s a lot of content on the site to sift through, and you’re having trouble working out whether Company A’s solution is right for you.
Then you land on Company B’s site. The homepage explains Company B’s unique selling proposition for your specific industry. It directs you to a whitepaper to answer some of your questions. From there, you are able to quickly locate case studies that show you how Company B was able to solve the problem you’re currently facing for other companies like yours. You leave the site feeling confident that Company B is a legitimate option for your team.
The difference between the two experiences is personalization, and it’s becoming an imperative for companies across industries. Companies like Netflix, Amazon and Spotify have made it clear that delivering personalized experiences is a critical business strategy, not just a marketing tactic. But it’s one thing to recognize the value of personalization; it’s another thing entirely to execute it successfully. For many organizations, what’s standing in the way of their execution of successful personalization is the data.
To create a great experience for a person, you must understand the person
Data is at the core of a great personalized experience. The more you know about a person, the more individualized and accurate the experiences you deliver will be. There are multiple different data sources that can potentially be used for personalization, depending on your business. Some of the key areas to focus on are:
● Web attributes such as geolocation, source (search, email, social, paid ad, referring site,
etc.), industry, company, company size (revenue or employee count), technology stack, time of day, and browser/device type
● Database attributes from CRM systems, email or marketing automation platforms, data warehouses, point-of-sale (POS) systems, call center solutions and other systems
● Site- or app-wide behavior such as number of site visits or logins, time spent per session, time elapsed since last visit, etc.
● Page visit behavior such as specific pages viewed and number of times viewed
● Campaign engagement such as personalized experience views and clickthroughs, email opens and/or clicks, push notification dismissals or clickthroughs, etc.
● Deep behavior such as time spent per page, mouse movement, scrolling, hovering, inactivity, etc. (to understand depth of engagement)
● Context of each visit or session based on metadata such as category, brand, price, author, topic, etc. (to understand what a person’s behavior says about his or her interests)
● Lifecycle stage such as first-time visitor, current prospect, regular customer, loyal advocate, potential churn risk, etc.
As you may have noticed, this is a lot of data. To complicate matters, this data often lives in silos across the organization — within CRM applications, email marketing solutions, web analytics tools and more. So even if it exists within the organization, it often can’t be used effectively. But with a Customer Data Platform (CDP), the organization can bring it all together in one place with a single profile for each individual person (and account for B2B companies). That single, comprehensive view of an individual or account is absolutely essential for personalization.
Of course, you need more than data alone
Once you have all this data, you need something that can effectively use it to deliver personalized experiences. Most importantly, you need:
Machine learning guided by humans: Machine learning helps you sift through the data and all the different experiences you could display to select the best one. Whether it’s selecting the best products, categories, brands, promotions, etc. to recommend to a visitor; reordering or changing the navigation to help her find what she’s looking for; sorting the search results to be relevant to her, etc., machine learning can make it possible.
Segmentation and rule-based targeting: While machine learning allows you to deliver personalized experiences at the individual level, there are still many opportunities to deliver experiences to segments via rule-based targeting. Any content or experience that is relevant to a specific industry, geolocation, persona, stage of the funnel, etc. can and should be delivered with rules.
Triggered messaging: Triggered messages allow you to send more timely content to prospects and customers across channels. For example, you can instantly send messages to specific customers; deliver experiences to particular visitors or users; send alerts or schedule tasks for internal stakeholders; or pass data to external systems – all based on a person’s behaviors, external factors like weather or geography, or even changes to product and content catalogs.
Real-time processing: All the data you collect is essentially useless if it can’t be used in real time to affect someone’s experience. You can’t rely on a person to return to your site or app; you need to be relevant immediately. So if someone on your travel site indicates that he’s researching ski vacations, you can’t wait until he returns to incorporate that intent into his experience.
Testing and attribution: Personalization is not something you can set and forget. You need to be able to test the effectiveness of each campaign, identify the impact it has on your business, and iterate to continue to improve your KPIs and the overall experience for each customer or prospect.
Final thoughts
Good data is absolutely essential to creating great personalized experiences for customers and prospects. But you need more than data alone. You need to consolidate lots of disparate information, interpret that information to make sense of it, and store it all in individual profiles that provide a complete picture of each person and company your organization interacts with. And all of this needs to be centralized and instantly actionable in a real-time capable CDP. Without such a CDP, your personalization strategy will be flawed. With it, you’ll be poised for success.
I wrote about Customer Data Platforms, machine learning, real-time processing, testing and much more in my recent book, One-to-One Personalization in the Age of Machine Learning. Download it for free on the Evergage website.
March 11, 2018
Update on the CDP Vendor Comparison Report
By David Raab, CDP Institute
 The Vendor Comparison report was published nearly two weeks ago, so it’s worth reporting a bit on its reception.
Reaction
In terms of popularity, the report was an overwhelming success. There were more than 200 downloads the first day and the total is now close to 500. It’s already the second-most popular item ever and will likely overtake the leader (the Industry Update published more than a year ago) within the next week.
Comments on the substance of the report have generally been enthusiastic. There’s been some sniping by vendors about ratings given to their competitors – possibly justified in some cases, although in specific instances we’ve reviewed so far, the existing ratings seemed correct. Some vendors have also argued for changes in their own ratings. This has yielded a few changes but, again, mostly confirmed the original choices. Now that the list of questions is set, it will be easier to ensure we get answers to all of them when doing our own vendor research, which should improve the accuracy of the published report over time.
There have also been some complaints that the Yes/No approach lacks depth and nuance. I agree. But we accepted those limits because the Yes/No approach seemed better than alternatives. This was discussed in several blog posts before we published the report so I don’t won't rehash the subject. I’ll simply point on that I’ve tried the other methods in the past, including detailed numeric ratings (the VEST report on B2B marketing automation systems rated 200 features as complete, partial, or missing) and extensive narrative descriptions (the original Guide to Customer Data Platforms in 2013 gave detailed answers to eleven questions per vendor). Both methods are highly labor intensive and neither seemed to give users what they needed. After publishing reports like this for twenty years, my fundamental conclusion is the best any vendor comparison can do is help buyers build a list of vendors to explore. Buyer needs are too varied for a general report to answer their specific questions in advance.
Rankings
The core philosophy behind the report was that buyers should look for the features they need. Still, human nature being what it is, it’s inevitable that people will count the Yes answers for each vendor and treat the result as a ranking.
The notion of such ranking is fundamentally flawed: users have different needs so it’s impossible to create a list of "best" or "leading" vendors that ranks them for everyone. Feature-based rankings have the additional problem that they reward systems with the most features. I’ve countered this in the past by noting that unnecessary features add cost and complexity and therefore reduce value. In fact, my VEST report deducted points for unnecessary features when ranking systems numerically. We did this by building separate rankings for different user types with different feature weights for each ranking.
I’ve modified the Vendor Comparison introduction to suggest something similar: if you can’t must rank vendors numerically, then assign one point for each Yes on a feature you need and subtract one point for each Yes on a feature you don’t need. As a bonus, this forces you to think about your own requirements. But, I repeat once more, the purpose of the report is to screen vendors, not to rank them.
Engagement Use Cases
Many of the vendor questions about ratings related to the Engagement use cases: content selection, multi-step interactions, and real-time interactions. These are not core features of a CDP but several vendors felt they should be rated as providing them, especially after seeing that some other vendors were. This is an admittedly confusing topic, so I’ll do what I can here to clarify it.
Customer engagement is ultimately about selecting messages for individual customers. There are six items in the report that relate to it. These form a hierarchy of capabilities. They are:
- API/query access: this means data in the CDP can be read by an external system, which might use it as input to an algorithm to select a message. Although the CDP data might actually include something to indicate the appropriate message, this item doesn’t require it.
- Real-time access: this means CDP data for a single customer can be read by the an external system in real time. This is almost always done through an API call, but not all APIs covered by the previous item will include this capability. It requires looking up an individual based on identity information provided by the external system and returning the result quickly enough to support a real time interaction. As with the previous item, this is only about data access, not choosing messages.
- Segmentation: this means the CDP can extract a set of records for customers who meet a set of user-specified characteristics. It’s quite possible the characteristics will describe people who belong in a certain marketing campaign or hould receive a particular message. But this item doesn’t require that the output specify a message, so any connection between the selection logic and messages is external. Note that every CDP in the report meets the segmentation requirement.
- Content selection: this is where we indicate that the CDP can decide who should get which piece of marketing or editorial content. Content selection requires awareness of which content items are available, what the qualification criteria are for each item (such as geographic or language constraints, product ownership, or status level), and the customer’s previous content history (used to exclude items already offered or consumed, to limit message frequency, to distribute selections among content categories, etc.) In other words, true content selection includes features well beyond the generic rules needed to select a list. Things do get murky here because it’s at least theoretically possible to create those functions with a generic rule builder. Informal criteria we apply in assessing this item include whether marketers are actually using the CDP for content selection, whether the system includes a content library, and whether there’s a content selection interface in place.
- Real-time interactions: this means the system can do content selection, as defined above, during a real-time interaction. It specifically requires accepting data during the interaction and using it to guide the content selection. This distinguishes it from real-time access, which means the CDP can return current data but doesn’t require incorporating new information in real time.
- Multi-step campaigns: this is defined very carefully as having “a user interface to set up a single campaign including a series of marketing messages for individual customers over time.” The real use case is delivering a sequence of messages over time, but any system with basic segmentation features can do that if the user is clever enough. We added “user interface” and “single campaign” to limit this to systems that are truly designed to run this particular type of campaign. There’s a good argument that such multi-step campaigns, usually entered through a branching flow chart interface, are a bad idea because they’re too hard to manage. I don’t necessarily disagree. But many users want to do things this way, so this item is intended to help them find systems that meet their needs.
What's Next
One way we justified limiting the report to Yes/No answers was by promising to link to more detailed explanations from the vendors. I have just a couple of these available and will put those links in the report some time soon. I'll also remind the other vendors to send them. We'll be adding more vendors as new Sponsors join the Institute and continue to review the current ratings to ensure they're accurate. I have no current plans to add more items to the report, but it's always a possibility.
Quick reminder: we post updated versions of the report as we make changes. You can always download the latest version fro the same URL: https://cdpinstitute.org/DL966-CDPI-CDP-Vendor-Comparison
March 6, 2018
With Data Ownership Comes Data Responsibility
By Jim Kelly, BlueVenn
 The ability to access, manipulate and use customer data for marketing purposes is a key goal for organizations, yet hard to achieve. A BlueVenn report from 2017 found that nearly 60% of marketers believes their company does not invest enough into tools or platforms to manage and analyze customer data. Another found just over a quarter (27%) still rely on the IT department for their customer data and analysis. It’s part of the reason (what with ‘marketer owned’ being a core principle) why Customer Data Platforms are seen as such a desirable solution to these problems.
Marketing teams want to iteratively analyze customer data and put it into action to create targeted, relevant campaigns. More importantly, they want to be able to do this in the space of hours – not wait days for their requests to be processed by someone else. A CDP can meet these needs.
Assuming that ownership of the Single Customer View schema is included as part of the CDP (as opposed to having to hand the IP for its creation back once a contract ends or an organization decides to move to another vendor), it can also make such a database a tangible business asset.
However, there is something else that is arguably much more serious to consider, and that is whether a ‘marketer owned’ database also means ‘marketer responsible’.
There are risks that come with the handling and processing of customer data, and strict policies for ensuring the privacy, security and governance of it. If marketers have total access to customer data, this also means they are exposed to the significant perils associated with this ownership.
The trouble is, some still early on their path as a data-driven marketer can be ill prepared for this responsibility. First, the risks of legal infringements can lead to costly punitive action from data protection regulators. Second, the misuse of personal data can cause significant damage to customer relationships and company reputation.
Coincidentally, this is a similar topic covered recently by David Raab in his Customer Experience Matrix blog. He noted that the impending General Data Protection Regulation (GDPR) could potentially be both good and bad for CDPs.
On the plus side, accurately linked ‘golden records’ mitigate some of the problems with establishing consent to marketing, and ensuring that communications do not get sent where they are not supposed to go.
The downside, as Raab points out, is that “greater corporate interest in customer data means that marketers will not be left to manage it on their own.”
This, in my opinion, is not necessarily a bad thing. Marketers can achieve great things with modeling, analyzing and putting customer data to action – even with a safety net of governance that keeps sensitive information off limits.
If you think this somewhat goes against the spirit of a ‘marketer owned’ database, wouldn’t it be better if this data were ‘marketer built’ and ‘marketing prepared’? They say that power is nothing without control, and I think that a data layer of usable, permissible data for marketing that is fit for purpose should be the answer.
When looking at Customer Data Platforms, you may want to investigate solutions with the appropriate safeguards that mitigate the data ownership risks. For example, with a Single Customer View process that adheres to internal and industry data management policies and requirements, with a time-stamped audit trail that provides full visibility of all actions, queries, errors and results that can be traced back to source if required.
While it’s great to have the power to use data, and clearly not enough marketers have it, it’s equally vital that there is a level of control. That means having the safeguards to ensure this customer data is used respectfully and responsibly.
March 5, 2018
Top Recommendations for Improving Website Performance with Tag Management
By Ty Gavin, Tealium
 Often in website optimization, you don’t exactly need the Artificial Intelligence team to identify the quick wins that contribute to a better user experience. If your “Next Step” button is impossible to find, then a few reasonable design adjustments will help get your visitors to the next step. The same is true when you first address improving your website performance.
What are simple best practices to follow that will give me the greatest impact on my site performance?
It turns out that with the single step of implementing a tag management system (TMS), you can address most of the page speed best practices giving you the greatest improvement to your site performance.
Google and Yahoo have excellent resources on this topic that we can use to measure up against:
From the above lists, Google has 10 best practice recommendations for page speed and Yahoo has 35. A number of these items are application-specific (how to handle CSS the most effectively) and thus outside the scope of a TMS, but a surprisingly large chunk of improvements are covered.
Out of these best practices, a TMS(we specifically used Tealium iQ Tag Management for our evaluation, of course) helps address 7 of Google’s 10 recommendations and more than 10 of Yahoo’s recommendations in a single step.
Let’s dive in and see how a Tag Management System (TMS) helps you optimize website performance:
Google’s Recommendations
- Enable Compression: Tealium automatically gzips content delivered from CDNs
- Leverage Browser Caching: Tealium’s JavaScript caches by default. Only modified tags will be retrieved (and then re-cached).
- Minify Resources: Tealium’s “minify” checkbox is checked by default
- Prioritize Visible Content: Tealium’s setting is to default tag execution wait until after DOM Ready. Your content-displaying logic always runs first (before tags).
- Remove Render-blocking JavaScript: Your first step with Tealium is moving tags off of your page and into Tealium’s non-blocking JavaScript container
- Use Asynchronous Scripts: Tealium’s default mode has always been asynchronous
Yahoo’s Recommendations
- Minimize HTTP Requests: Tealium’s tags can be “Bundled” into a single .js file
- Use a Content Delivery Network: Because one CDN just isn’t good enough, Tealium uses multiple CDNs
- Add an Expires or a Cache-Control Header: Check
- Gzip Components: Check
- Put Scripts at the Bottom: Default setting for tags is “Wait = True” (tags will be set to run after DOM Ready)
- Make JavaScript and CSS External: Tealium hosts your JavaScript in our tags domain
- Reduce DNS Lookups: If adding one more domain is too much, Tealium provides the option to host tags on your own CDN
- Minify JavaScript and CSS: Check
- Remove Duplicate Scripts: Controlling your tags in a TMS reduces chances of multiple script includes in your page source
- Split Components Across Domains: Your tag components are hosted on Tealium’s CDN
- Minimize the Number of iframes: Tealium typically will provide the option to use the Image pixel for a vendor (over their iframe pixel)
- Reduce Cookie Size: Tealium’s Universal Data Hub (UDH) Visitor Service uses Local Storage to store persistent data for that visitor in their browser
- Use Cookie-free Domains for Components: The automatic benefit of using Tealium’s CDNs to host JavaScript means that your 1st party cookies are *not* sent with every request
With Tealium’s Universal Data Hub you can also leverage cloud-based event delivery, which enhances your ability to “Minimize HTTP Requests” (Yahoo’s list) to improve site performance.
Combined with the capability that Tealium iQ will “Prioritize visible content” (Google’s list) by delaying tag pixels from firing until after the DOM Ready event, this setup gives you ultimate flexibility to tune website performance as needed. Consult your Tealium Account Executive (for new clients) or Account Manager (for existing clients) for the best solution for your site.
The above concepts highlighted by Google and Yahoo are typically the best practices for web page/application development. With respect to your tags, you’ll get all of these best practice features automatically with Tealium iQ Tag Management System (TMS). Tealium provides a solution to get the foundational things done right for overall improvement in your website performance.
For a more in-depth look at this topic and specific strategies you can start implementing today for greater website speed download our latest whitepaper on “Best Practices for Optimizing Website Performance With Tag Management.”
March 1, 2017
Postcards from The Client Side: Set Business Stakeholder Expectations Early and Often for CDP Success
By Fred Maurer
Shift stakeholder focus from capabilities to outcomes
 The CDP is going to convert our anonymous web visitors to known contacts. The CDP is going to unify our customer data across legacy silos and create a master record. The CDP is going to resolve user identities across devices and optimize engagement across channels. The capabilities and value potential of CDPs are amazing. But is a CDP really going to do all this singlehandedly? Could this narrative be a case of missed expectations just around the corner?
The answer depends not only on your organization’s unique definition of success but each stakeholder’s unique definition. Helping stakeholders focus on business outcomes rather than technical capabilities can improve CDP value perception and help make deployments more successful. Like people, CDPs often don’t get a second chance to make a first impression.
A CDP can provide strong competitive advantages and measurable results when effectively integrated and deployed. It can add tremendous value to achieving ambitious data-driven marketing goals. But ultimately it’s people, planning, communication, and execution that make the difference. Organizational readiness and clear, realistic business stakeholder expectations can help drive CDP success and progress.
Socialize the CDP and make dependencies clear
Even if they’re aware a CDP is coming and know the overall value proposition, not all business stakeholders understand the technical realities surrounding integration and deployment. It’s a process with dependencies that can impact stakeholders very differently. Organizational readiness and data readiness go hand in hand. The right data has to be collected and integrated before the CDP can effectively enable customer/audience segmentation and activation. Data readiness requirements and impact can vary widely between stakeholders.
Help stakeholders understand that an audience is only as actionable as the data collection and integration strategy behind the CDP. Identity resolution is one example. Simply importing known contacts into a CDP doesn’t make them identifiable or digitally actionable. With the right planning and execution a CDP can identify anonymous visitors when they engage with CDP-integrated digital touchpoints. But to do so the CDP must have access to the data it needs to resolve their identities. Only then can the CDP begin to merge known and anonymous profiles.
Business stakeholders can benefit from continual reminders that there’s no identity resolution magic inside the CDP. It requires precise technical strategy and tactics such as cookie syncing, third party data services, and user self-identification strategies. Business stakeholders don’t need an in-depth understanding of the difference between deterministic and probabilistic matching, but they should be aware of major data dependencies that impact them, the CDP deployment roadmap, and its impact on their business goals and plans.
Use an MVP approach to narrow scope and prioritize business needs
Sharing prerequisites and dependencies with individual stakeholders not only sets realistic expectations but also helps identify business priorities in the context of the deployment roadmap and technical realities. Leverage clear expectations to drive discussion and definition of a “minimum viable product” (MVP) for each major stakeholder. The MVP is a set of baseline CDP capabilities and outcomes the stakeholder can expect at launch, as well as metrics for measuring progress. This baseline can go a long way toward helping stakeholders understand how their capabilities will be enabled and grow as the data integration strategy matures and new CDP features are delivered.
A digital sales stakeholder’s MVP may be the ability to sell advertisers on targeting ads and messages to behavioral audience segments. A marketing stakeholder’s MVP may be the ability to acquire more email newsletter subscribers. An editorial stakeholder’s MVP may be the ability to serve targeted content recommendations. These small, narrowly scoped business wins can help establish traction for the CDP and demonstrate measurable results to stakeholders.
More is more
The benefits of proactively setting reasonable stakeholder expectations?
- · More satisfied and open minded stakeholders
- · More stakeholder patience during work on data integration and other technical dependencies
- · More effective stakeholder priority roadmapping and enabling
- · More traction for the CDP deployment
- · More stakeholder buy-in and adoption
About the author
Fred Maurer is a Chicago based CDP consultant with over twenty years of digital and data-driven marketing experience from strategy through execution across the technology and business spectrum. His recent hands-on customer data platform experience includes enterprise business strategy, vendor platform evaluation/piloting, vendor selection, contract negotiation, data strategy, data integration, CDP platform integration, business deployment, and (of course) stakeholder engagement. He can be reach at fredrick.maurer@gmail.com or 630-790-0836.
February 27, 2018
Make Your Customer Data Platform a Corporate Asset
By Rob Glickman, Treasure Data
3 rules to help you not build yet another customer data silo
 Who “owns” the customer experience? For companies like yours – seeking to deliver outcomes that keep customers engaged and loyal – few questions are more important.
The problem is that responsibility for the customer experience is so often spread out across different business units who touch the customer across their complicated journey: point of sale, online, mobile, customer apps, internet of things (IoT), customer service – the list goes on.
Let’s say you head up one of these business units. You want to be effective. You need to understand your customer. And you understand that this requires data visibility. So, you set out to solve this problem by either stitching together multiple systems or try to build your own customer data platform (CDP) for a single source of truth for all customer information.
You’re not alone. Chances are there’s someone at your company in another department trying to do the same thing as we speak. Databases of customer information are everywhere – homegrown, best-of-breed databases that someone, somewhere attempting to finally solve this ‘single view of the customer’ only exacerbated the problem with yet another data silo.
How, then, do you implement a solution that actually serves as the single source of truth for all your customer information? The key is to approach a CDP as a valuable corporate-level asset that is sanctioned by IT and the business, yet owned and operated by the marketing team. A system designed to yield visibility into your customers for the entire organization, regardless of the data source.
Here are your three rules for success:
Rule #1: Get support from the top
It’s important to start from the highest levels of your organization. The goal is to get everyone working together, aligned to the same goals of customer intimacy, loyalty and ROI. Many customer data platforms on the market today emphasize quick and fast, out-of-the-box functionality – but that just won’t cut it in 2018 with the explosion of customer data sources. No way. And unless the entire organization is on the same page, you’re looking at another data silo that gives you half-measures and half-results.
This is an important insight, but it’s hardly new. Recognizing the need for a holistic approach, some organizations have even developed C-level positions such as Chief of Customer Experience. Whatever your specific approach, executive engagement is key. If your company is to be truly customer-centric, it needs to be customer-centric from top to bottom and you need to measure meaningful KPI’s that cut across your teams.
Rule #2: Involve IT – and empower users with self-service
Data projects that don’t involve IT are problematic on multiple levels. Chief among them is security. If you’re striving to improve the customer experience, the last thing you need is a security lapse that will put your customers – and your company – at risk. Best to have IT on board from the ground up.
The IT department is also important because of its reach. A customer data platform extends across the entire enterprise – that’s the whole point. Leaving IT out of the picture will be an exercise in futility.
Not that IT needs to do all the heavy lifting. Rather, they should be your supporter and champion because you have done your homework and given them the peace of mind to let you run your business, engage with your customers in meaningful, holistic ways. Your customer data platform should be cloud-based – which alleviates IT of the complexity involved in an onpremise implementation and maintenance. Be sure to look for offerings with a comprehensive library of data connectors that help speed data consolidation. Also critical: professional services for tasks like building custom connectors to proprietary sources such as customer apps.
Ultimately, your goal is to empower business users throughout your organization with powerful self-service capabilities. The role of IT is that of checkpoint to validate the technical approach taken. Once you’re up and running, IT should feel confident enough to take a step back and allow people in marketing and elsewhere to take it away. Even connecting new customer data sources as they emerge should be the responsibility of business-level data stewards because you have done the heavy lifting early on, and implemented a system built for scale.
All of this requires that you strike the right balance between IT and business users. Security is critical, as is proper integration into your environment. But platforms that require IT involvement at every twist and turn simply will not keep pace, and your project will fail.
Rule #3: Start with a proof of concept
An enterprise-wide customer data platform does not mean that you have to boil the ocean by doing everything at once. To get off the ground (and get support from the top - Rule #1), start with a focused proof of concept (PoC) to identify low-hanging fruit – something likely to yield high value quickly.
To demonstrate value, a PoC should unite a minimum of two data sources such as web site interactions and in-store point of sale transactions. This will help jump-start the data-driven, cross-business collaboration required to deliver a holistic customer experience.
Good, quick PoC projects – virtually limitless in variation – tend to focus on a key KPI. Here are some examples for inspiration
Whatever PoC you choose to pursue, don’t underestimate its importance as a showcase. It will show what’s possible with a proper enterprise-grade customer data platform. It will demonstrate to your colleagues the power of a consolidated, holistic view of the customer. It will generate more than buy-in – it will generate enthusiasm and inspire collaboration.
The Treasure Data Difference
The Treasure Data enterprise Customer Data Platform enables a single, actionable view of your customer for the first time. We handle the scale, security and complexity required by a global enterprise to deliver a superior customer experience based on data-driven decisions. We’re a platform, we’re applications, and we’re services for expertise on demand. To achieve a truly enterprise-wide CDP, you need all three. For a deeper dive on what’s needed for CDP success, read our Top Ten Checklist blog.
February 26, 2018
CDP Vendor Comparison is Here!
By David Raab, CDP Institute
 The CDP Institute has released its long-promised vendor comparison report (download here), capping a project we started last September. The final version closely resembles the approach I described in January . We did drop the entry for incremental attribution (too complicated) and add one for on-premises deployment (important and simple).
This is a good moment to circle back and compare the final result against the original project objective. That was to help buyers understand the differences between different types of CDPs. The original blog on this topic proposed defining clusters such as “digital-only” and “messaging” CDPs. We ended up defining more specific use cases, such as supporting Web data or providing probabilistic identity resolution. This abandoned the idea of vendor clusters and attempted instead to guide buyers directly towards systems that could meet their specific needs. We still do conceive of three very broad CDP system types: those that focus on building the database, those that offer database plus analytics, and those that offer database plus customer engagement (with analytics usually included too). Those categories don't add much value to granular information about use cases, but they do allow some broader analysis of industry trends in our other reports.
As I’ve discussed in previous blog posts, the final report gives Yes/No answers to the question of whether each vendor provides each feature. We’ve tried to define the features in ways that Yes/No answers are meaningful but readers still need to understand that this still hides a great deal of complexity.
To take a random example, one item is “SDK load”, which says a system has an SDK to load customer-linked data from a mobile app but doesn’t specify what functions the SDK should provide. This doesn't tell buyers whether the system meets their needs, but it does identify systems that lack any mobile app integration whatsoever. This lets buyers exclude those systems from further consideration and then dig into the remaining products in more detail. We fully expect each buyer will find many of those systems are inadequate, but that's something they can only decide for themselves. Buyer needs are simply too different and the issues are too subtle to hope to capture them in a general purpose document such as this report. The brutal simplicity of the Yes/No approach is intended to make clear that we’re not providing this more detailed information.
The other big limit to this report is it only covers Sponsors of the CDP Institute. This was a painful choice but I think a fair one. Putting together the report and running the Institute are expensive projects that are only made possible by Sponsor fees. Yet our efforts benefit the entire industry, including non-Sponsors. Including only Sponsors in this report gives them an exclusive benefit that helps to justify their investment. Fortunately, the Sponsor list includes almost all the major industry vendors so we’ve only left out a couple I would have otherwise included. And I like to think that it will be easy for buyers to ask our standard questions to non-Sponsors, making it fairly easy to to extend the comparison.
We’ll be updating the report as new Sponsors join and as vendors revise their systems. We’ll also be linking to more detailed explanations of the items provided by the vendors themselves. So feel free to download the current edition of the report but do check back for the latest information when you’re making a specific decision.
As always, feedback is welcome. Enjoy!
February 20, 2018
Feed Social Data into Your Customer Experience Strategy
By Rafael Flores, Treasure Data
 Social media is a powerful tool in digital marketing. As companies continue to scramble to shape and iterate their respective marketing strategies to promote a personalized customer experience, social media remains ingrained in the conversation.
Facebook, Twitter, and Instagram are often channels that many of us, including myself, interact with on a daily basis and open the gates for us to be exposed to thousands of digital ads promoting a new product, services, and at times social movements. While on a few occasions we are attracted to something of interest – perhaps the latest technology gadgets in the market – it is more often the case that we are annoyed to have to dismiss the “cookie browsing” alert at the top of the screen or exit out of an ad that has been poorly placed on a website.
This interaction between us, as users, and them, as vendors, is natural as individuals have preferences, behave differently, and even react to news in no standard way.
The question then remains, how can companies target us in a more organic way, that speaks to our preferences, behaviors, and overall human DNA? The answer is not ideal: there is no silver bullet.
However, data plays a key role between a good and a bad customer experience strategy. I am not alone in this feeling as marketers continue to deploy Treasure Data as their customer data platform of choice to ensure they can collect, analyze, and drive action based on their social insights.
This process begins with being able to capture the right data into a single source of truth; what is often referred to as a ‘data hub.’ Most recently, my team developed direct integrations with Facebook, Twitter, and Instagram to ease this integration pain and make data actionable.
Facebook and Instagram Insights
While you can view all Facebook and Instagram insights directly within your Facebook Analytics dashboard, the reality is that this does not offer a 360 degree customer view.
Your users also live outside of social media. You have data coming from website traffic, an active pipeline inside your customer relationship management tool, and key performance metrics across all marketing automation/campaign management tools. All of these data sources need to be united into a single source of truth.
By being able to unify such data into a single view, you are able to better target your Facebook and Instagram audiences and offer a personalized customer experience.
Most notably, you are able to quickly iterate your customer strategy as you can schedule your social data insights to flow into Treasure Data’s Customer Data Platform to have a clear view of who is engaging with your content, whether that is by liking your Facebook page or sharing your latest Instagram post.
Then Comes Twitter Custom Audiences
While I’ve focused around bringing data into a centralized location, the truth is, data does not stop there. Instead, it is the actions that are powered by data that prove to be meaningful.
Our integration with Twitter Custom Audiences is proof of this. While I’ve covered the ability to ingest data from social media giants such as Facebook and Instagram, we also help you fill the funnel with clean data.
No longer do you have to upload a clunky CSV file into Twitter to create an audience and then run a campaign. Treasure Data’s enterprise Customer Data Platform now allows you to automate this step and instead focus around catering to your Twitter base with a personalized message.
If your Facebook influencers are also on Twitter, yet have not engaged with your latest tweets, it may be time to drive new behavior and maximize your brand’s social media presence.
I recently sat with a former co-worker who heads all marketing efforts at a series B start-up. I mentioned to her that I was writing a blog around how social data can help drive customer experience, of which marketing is key. Soon enough, we found ourselves deep in the trenches discussing how our integrations actually work, typical user stories, amongst other things.
She quickly turned and happily interrupted my spiel, “You are telling me I do not need to look at so many Excel sheets every day?” To which I responded, “No, you don’t.”
You don’t either! Give Treasure Data’s Customer Data Platform a go and you will soon find yourself catering to a happy social media base.
Read how to combine your Facebook Custom Audience and Salesforce data to optimize your Facebook advertising based on the complete customer journey.
February 16, 2018
What is a Customer Data Platform (CDP)? Interview with Treasure Data’s VP of Product
By Pankaj Tiberwal, Treasure Data
 What is a Customer Data Platform (CDP)?
It’s a question that we often hear as companies search for a more innovative, scalable way to collect and utilize their customer data. We sat down with Pankaj Tibrewal, Treasure Data’s new VP of Product, to outline what a CDP is, what it does, and how it helps companies address the crucial need to better understand and engage with customers.
Q. What exactly is a Customer Data Platform?
A. A customer data platform lets you bring together your customer data from all different silos to create a unified 360-degree view of your customer. As we all know, that challenge today is tougher than ever with the incredible growth of marketing channels, applications, devices and data itself.
Q. How does a CDP differ from technologies that companies use today, like marketing automation, web analytics, CRM or even a database?
A. Indeed — there is no shortage of technology out there that has promised a single view of the customer! The reality is that there’s not been one platform out there that can bring all that data together in a single place to enable analysis, insight and action. A CRM doesn’t store event-level data (i.e what users, customers, and consumers do and when they do it) and typically most data management platforms delete data after a certain number of days — 90 days typically. Today’s modern marketers need that granular level of data to personalize web content, do multichannel marketing, target advertising to specific demographics and so on. The days are long gone that you can rely on a sample of data, just from one or two channels, and hope to stay relevant to your customer. A CDP is designed to stitch together a single customer view across a growing number of touchpoints, both online and offline.
Q. What’s changed that makes a CDP so important?
A. When you think about the landscape today, businesses interact with customers through websites, mobile apps and point-of-sale systems in physical stores. They have call centers, multiple email campaigns, a social media presence and a network of partners. Data is captured in devices when consumers interact, and then additional data is stored in transactional histories or maybe a CRM system, and the company might be bringing additional data from business partners, affiliates and third-party data sources from data aggregators.
It’s just an unmanageable knot of data that isn’t being put to use to deliver a better customer experience. Most companies are literally paralyzed by this challenge and simply have no ability to make sense of this customer data — let alone use it to drive customer value. A CDP zeroes in on that problem by integrating and standardizing data, making it ready for segmentation and personalized actions that can execute in real time. And it can do that in tremendous scale that matches today’s complex environments.
Q. What sort of customer engagement issues is a customer data platform meant to solve?
A. Let me share a story. A few months back, I was shopping for a new SUV. I visited the auto company website, selected a model, entered my information, and soon enough started receiving emails from seven or eight local dealers. I visited a couple of them, but their systems didn’t get any smarter because of my visits. All the emails were generic. No one ever contacted me to say, “Thanks for your visit, we just received two new models, in these colors.”
After I bought the car, I continued to receive generic sales emails from all the local dealers — even from the very salesperson from which I purchased the car. Their systems didn’t recognize that I bought the car, and in fact, if they had stitched the data together, they could have been upselling me upcoming service promotions, or add-ons. But they were still trying to sell me a car I had already bought! That sort of uninformed marketing is unacceptable to me, and to most consumers today. I end up trusting the dealership and company less, because I know they are not managing my data in a holistic, customer-first manner, and it makes me think, what else will they do with my data across their fragmented teams?
Those missteps don’t happen with the right CDP solution. A CDP is designed so that companies can orchestrate interactions in real-time, taking into consideration the history of interactions and up-to-the-minute behaviors across all channels.
Q. As your story suggests, it sounds like the need for a CDP is even greater because of how customers want to interact with companies.
A. Absolutely. Customer expectations are much higher today. They no longer buy based just on price and selection. Customers value relationships with their favorite brands and they expect companies to be smarter and respond intelligently. For instance, the whole retail and CPG sector is struggling to reinvent themselves in the face of threat from Amazon. The media and entertainment sector is trying to compete with Netflix.
What’s common across all companies like Amazon and Netflix is that they have a much better understanding of their consumers based on data. They serve their customers very well across all touchpoints — for instance, with recommendations based on a customer’s real-time activity on a website.
Q. Do organizations generally recognize what a CDP is and how it differs from traditional approaches?
A. A marketing executive once asked me, “We have Salesforce, so we have a Customer Data Platform, right?” The answer is no — they have an application that contains some, but not all, customer data. Salesforce and other applications are the problem silos that a CDP solves.
The term Customer Data Platform is relatively new, but the problem it solves is certainly not new, and mindshare around the need is growing rapidly. The Customer Data Platform Institute is instrumental in that. The CDP Institute was founded in late 2016 in part to educate marketers around CDPs and help them take advantage of CDP capabilities. They recently issued an industry update showing that the number of CDP vendors globally more than doubled in 2017, from 24 to 52.
Treasure Data is one of the founding sponsors of the CDP Institute. One metric that jumped out at me in CDP Institute’s industry update is that employment at the original 24 vendors has risen 27 percent year over year, clearly indicating a fast-growing industry. That’s certainly the case at Treasure Data.
Q. What’s the underlying technology of the Treasure Data CDP?
A. We are convinced that there is a need for an Enterprise-grade Customer Data Platform, and that is our primary focus. Our platform consists of modern technologies for data integration and ingestion, stream processing, data warehousing and activation. We pride ourselves in our ability to deal with any type of data, in any format, across any time period. When we speak of any type of data — we sometimes refer to it as schemaless, which means organizations can ingest both structured and unstructured data and evolve their data models on an ongoing basis. Bring it all in first, in any format, and then deal with it later. That is a huge benefit for customers in this new world of exploding data sources. We also have a large number of integrations for various data sources and activation applications.
We’ve also been a big proponent of open source technology from our founding in 2011. Our technologies like Fluentd, for streaming data collection, is used by more than two million users at marquee companies.
We are the second-largest committer for Presto after Facebook. Our data warehouse is based on Hadoop and built for cloud, with separation of compute and storage.
Learn More. Find out how businesses are putting Treasure Data to work to solve the customer data challenge. Read our case studies on how Subaru and the global beauty brand Shiseido are transforming customer experiences with Treasure Data, or learn more at www.treasuredata.com.
February 13, 2018
Disruption in Retail: Customer Relationship Management Intersects the Internet of Things
by Luke Saville, SessionM
 We are living in the breakout period of big data and the proof is all around us. Every day the digital devices we interact with become smarter and more personalized, improving how they anticipate our needs and desires. The technology leap of utilizing unimaginable amounts of data has modified consumer behavior; customers expect interactions with brands to be as natural and intelligent as the interactions with their phones and apps.
The challenge and opportunity are immense. Retailers’ assets include supply chains, physical stores, ecommerce, mobile commerce, and marketing channels. Retailers’ headwinds include changing consumer expectations, market disruption, legacy technology, and financial pressure. There has been a rapid upheaval of the old world order in retail as giants have fallen and nimble players have quickly stepped up to fill the vacuum.
Competing starts with understanding your customer and responding to their needs. In today’s vastly connected world this concept translates to opening the spigots of data from digital endpoints and using it to obtain knowledge and improve the customer experience. This requires brands to place bets on technologies at various degrees of market adoption.
At SessionM, we believe a fundamental element to modern brand-consumer interactions is the customer data and engagement platform. Customer engagement can flow into such a platform from any digital source, update models, and trigger response engagements in real time. This unlocks a world where brands can interact with their customers across channels in the moment. For example, a customer walks into a store and is greeted by an associate who knows their product preferences. Content is streamed to the customer’s phone based on where they are in the store and their past shopping experiences. Follow up emails tie these experiences together and maintain the relationship between visits. Ecommerce and mobile commerce that personalizes based on cross channel interactions. Having a customer data and engagement platform ties all of these experiences together and creates more meaningful relationships between a brand and their consumers.
Putting in place this fundamental element of customer data and engagement, a brand can “turn on” and “turn off” engagement channels. This provides brands with a way of tying together customer cross-channel interactions while testing new technologies and responding to customer behavior.
On top of this, SessionM measures Key Performance Indicators to determine the impact of a particular program, channel, or campaign. Brands can make intelligent decisions more easily about which bets are paying the highest dividends by testing and reviewing results to KPIs within customer segments. This insight is crucial to surviving and thriving in today’s market.
SessionM is elevating brands to compete with a focus on customer relationships through better engagement and brand loyalty. Baked into the platform are tools to build rewards and promotions, which are targeted towards customers based on past behavior. These incentives become more personalized over time, as the platform learns from the individual consumer, the consumer group segment, and the broader brand consumer population. This leads to stronger affinities as the brand begins to build and strengthen a 1:1 relationship with the consumer.
The challenge and opportunity facing industries is as daunting as it is exciting. SessionM provides the experience and the technology to enable brands that seek to leapfrog their technology for better customer engagements at scale.
February 9, 2018
Are You Ready for GDPR in 2018?
Angela Stringfellow, NGDATA
 On May 25th 2018, the General Data Protection Regulation (GDPR) will become enforceable. The regulation aims to give control over personal data back to individuals by strengthening and unifying data protection regulation within the European Union. The impact of the regulation will be far-reaching, as it applies to any company that holds or processes personal data of individuals residing within the European Union.
The penalty for GDPR non-compliance is up to €20M or 4% of annual global turnover. The cost of ignoring GDPR is too high, forcing corporations to reevaluate the way they handle consumer data, and to install new processes and technologies enabling the consumers right to “own” their data.
Great customer experiences require a solid understanding of each customer first, so you have to put a “face” to your data. Corporations looking to meet the requirements of GDPR could simply anonymize all of their customers’ personal data. Yet, in a world where delivering superior customer experiences is at the top of everyone’s list, this approach will never enable true customer centricity. Today’s consumers expect products and services to be uniquely tailored to their needs, which in turn, requires an actual understanding of those needs before being able to act. This understanding lies in each individual’s personal data.
Marketers: Why You Need to Care About GDPR
Marketers, specifically, need to be “in the know” on GDPR because it will affect the entire customer experience of your brand.
GDPR enforces the data privacy that your customers demand. A transparent corporation that acknowledges the sanctity of personal data by putting data privacy at the forefront of their customer-centric strategies, is poised to perform better than those companies who don’t. Knowing whether and how your customers would like to receive messages, actions and offers is an opportunity to deliver better, more engaging and less intrusive customer experiences.
Your customer data is precious, and if managed properly and efficiently, it can deliver a significant competitive advantage. If customer data is abused, however, you risk the customer backlash that can cripple your brand’s reputation. Consumers place a great deal of trust in the brands they love. You can reward that faith by displaying competency and transparency concerning your data privacy policies, and by providing them with the most relevant and seamless customer experiences.
What Marketers Can Start Doing Today
There are three different ways you can respond to GDPR:
1) You Can Do Nothing: Non-compliance with the regulation will lead to hefty fines. Companies in breach of GDPR can be fined up to 4% of their annual global turnover, up to €20 million. While this is the maximum fine, and one that will only be imposed for the most serious infringements, a lot can and should be done for a much more palatable price tag. Even if it wasn’t for the fines, the reputational risk and competitive disadvantage should not be underestimated either. Ultimately, customers will no longer want to do business with a company that does not respect their data privacy. With a regulation like GDPR, doing nothing is not really an option.
2) You Can Do It Yourself: An alternative response to achieve GDPR compliance is to “do it yourself.” As with most regulatory compliance projects, the first step is an assessment of your current state and an estimate of the effort it will take to update your current environment. The assessment will require answering questions such as:
- What customer data do you hold?
- Where is that customer data stored?
- Who can access the customer data?
- How secure is your customer data?
- For which purpose am I using the customer data?
- How do I control the customer data?
Companies that have undertaken this assessment, in preparation for GDPR, will come to realize that their customer data flows through a complex and fragmented eco-system of systems, tools and applications, including channel applications, CRM and marketing systems and analytics applications. Remediating the totality of those systems to ensure that you operate in a demonstrably GDPR-compliant way will most likely be a complex, costly endeavor.
Even those companies who have centralized their customer data in a data warehouse will encounter challenges, as data warehouses are not intended to support the operational processes at the customer level that GDPR requires. Companies who are considering master data management (MDM) solutions to address GDPR-compliance will realize that MDM systems can certainly assist in partial compliance, but they, too, will fall short in managing the operationalization of GDPR.
3) You Can Implement a Customer Data Platform for GDPR Compliance: Adding a Customer Data Platform (CDP) to your current technology eco-system could vastly help you gain operational control over all over your customer data in one place, thus allowing you the ability to better organize and understand your data to be complaint with GDPR. Gartner defines a CDP as, “an integrated customer database that unifies a company’s customer data from marketing, sales and service channels to enable customer insight and drive customer experience.”
It’s the CDP’s ability to centralize all the customer data in your company – structured and unstructured, factual and behavioral, from digital online and offline source systems, as well as from your multiple channels and devices – that’s the key to effective and diligent operational customer data management, a pre-requisite for GDPR compliance.
A Next Generation CDP Supports GDPR Compliance and Beyond
It’s safe to assume that as GDPR goes into effect and begins to be enforced, the specific technology requirements will present themselves. While many companies are waiting to see how GDPR will be enforced, laying a nimble data privacy foundation is the best course of action to adjust your treatment of personal data to meet the shifting regulations on data privacy in the EU.
GDPR shouldn’t be a hindrance to customer centricity and the customer experience. Managing GDPR compliance across a fragmented stack of disparate technologies that process personal data is a backwards approach. Effectively managing the privacy of personal data requires a single source of customer record, and a secure system giving granular context to all personal data being processed. You need a single platform to manage customer consent of their data being stored, processed and permission for how that data is used.
NGDATA’s Lily™ provides key features and functionalities that will support your company’s GDPR compliance as a Data Controller and a Data Processor that will and guarantee your customer’s Data Privacy by Design with:
- Customer DNA
- Advanced, Purpose-Scoped Execution of Consent Registration
- Advanced Data Management
- Advanced Security
- Data Monitoring and Audit Support
NGDATA is supporting our customers through preparation for the impending GDPR deadline of May 2018. Our next generation CDP, Lily, solves the marketer’s dilemma in the face of GDPR by helping companies manage customer information in one place – for ease in turning information on and off, and using data properly based on consent of the customers. Through Lily, you can shift GDPR from a cost-avoidance issue to a revenue-generating opportunity that has the customer at the center of both privacy and utility.
February 6, 2018
Top Requirements for Achieving Continuous Data Integration
By Rafael Flores, Treasure Data
“Data” has become more than just a filler word, and, instead, an engraved term in the minds of recruitment teams as they launch searches for the next data-driven CMO or Digital Marketer at their respective organizations. With a growing urge to iterate marketing campaigns in real-time, marketers are relying upon larger tool stacks and an endless set of dashboards full of performance metrics to monitor any sudden changes. This is no easy feat.
You may ask yourself, “How can a marketer do this and possibly succeed?” The answer is not simple, but it is possible. Sleep? Not so much.
Over my tenure at Treasure Data, I’ve managed all integration related efforts. From getting campaign metadata into Treasure Data from Salesforce Marketing Cloud, to how to export a list of users into the Google DoubleClick Data platform to activate a campaign. I’ve heard it all. The knack for getting data into and out of a centralized data hub is ever-pressing across our standard product persona of a digital marketer.
While this remains true and essential in their day to day, there is a key use case being forgotten: continuous data integration.
As digital marketers are eager to get data in and out as quickly and seamlessly as possible, they continue to make a distinction between input and output integrations. “What systems does Treasure Data integrate with?”, “Are they for data coming in or out?” Common questions during routine sales call eavesdropping that I often stumble upon.
This also remains a common misconception and product development principle applied at many companies seeking to tackle the “integration issue.”
The truth is, there is no business added value in simply viewing data as a single flow or direction.
To best explain this concept, I will use a real life example. Say you are a Data Analyst tasked with analyzing the data living within your company’s marketing automation system. You are asked to bring into the Treasure Data Customer Data Platform (CDP) all lead activity data. Your marketing team is then going to use this data to create an audience of leads that have been stale for over 90 days. Their hope is to find hidden gems to target via their next campaign. However, this will lead to the same outcome…stale leads.
In an ideal world, you should be tasked with building an audience of stale leads, while also capturing back all campaign performance results to ensure the data is actionable.
This is continuous data integration, or viewing an integration as more than just an in or out, but a bi-directional movement of data that has a direct business impact. By applying this concept to your marketing efforts, you’d understand that your task is far greater than just moving data from one system to another. It is to add value by acting upon data in real-time to attain the highest marketing performance results.
This goal sounds great on paper, but not easy to implement without the proper tools. While I’d hate to turn this blog into a sales pitch, I will simply highlight some of the key functionalities your system of choice should have.
A secure authentication method, preferable OAuth. With access to data comes the responsibility to protect that data from outside attacks. Having an OAuth flow in place allows you to connect your systems without much of a worry of the data being compromised. Take it a step further and review your vendor’s certifications, SOC 2 as an example.
Flexible schema ingestion. Getting data in and out of a system may prove to be a lot of work if much of the data preparation has to happen outside of that system. You’d want to ensure that most of your time is spent on analyzing and acting upon the results, and not preparing the data. Most notably, a schema-flexible system allows you to save time as you continue to add other tools onto your technology stack.
Incremental loading. As much as you’d like to avoid doing data preparation to ingest your data into the right schema, you would also enjoy not having to dedupe your data daily. With incremental loading, you can simply do an initial historical data dump, and then schedule all future transfers to load based on your last update. Take Salesforce CRM as an example, with incremental loading you don’t have to bring in all millions of contact records into Treasure Data daily, instead, only ingest the last 100 records added to your CRM since your last upload.
Mode flexibility. This may sound highly technical, but it is not. This simply means whether you can append, replace, or update your data as it lives in a table. While this may be a nice to have for some, I recommend this as a must have. You’d hate to not be able to update existing contact records in bulk after cleansing your CRM or marketing automation system.
While I am sure there are plenty of functionalities you may be considering for your Customer Data Platform, keep these in mind as essentials.
Most importantly, understand the value of seeing an integration continuously versus simply getting data in and out of a system. The latter can lead to poor results and just a day-to-day activity. The former can lead to significant ROI as you iterate in real-time. Plus, wouldn’t it be great if they said your name during the next round of promotions?
Recommended reading: https://www.treasuredata.com/integrations/recipes/.
February 2, 2018
3 Steps to Take Before Deploying a Customer Data Platform
By Kris Tomes, RedPoint Global
 Customer data platforms (CDPs) are powerful solutions that break down functional and channel-specific data silos endemic to most organizations. Eliminating these silos can power improved business results through deeper visibility into the customer lifecycle and greater accessibility to data in the moment of need. As with most powerful solutions, customer data platforms are often viewed as a cure-all. When seeking one out, it’s likely you’ll hear advocates preaching the solution as the cure for what ails your customer data and the magic solution to streamline reaching customers in the right channel at the right time. And CDPs do those things.
But as with all powerful technologies, you can’t deploy such a solution without a fully fleshed-out strategic plan. The concept of a CDP must be evangelized at all levels of the enterprise and, as with any new software deployment, there must be preparation done internally before seeking out a solution. Before deploying a customer data platform, you should:
1. Gain organizational alignment from top to bottom – This sounds like a step that should occur well before a CDP is even selected, much less deployed. It’s included because there are often still organizational holdouts on the need for a CDP in the first place. Data silos are intractable, as my colleague Buck Webb explained in a recent post, and this means that organizational alignment is an important first step on your road to deploying a CDP. Disparate data sources contribute to a host of problems, and everyone – from top to bottom – must agree that a customer data platform is the solution. If you can’t gain the buy-in of business users and stakeholders in your company, you risk project delays or de-scoping very easily.
2. Identify which data to include – CDPs are powerful solutions that can ingest data of all varieties, velocities, and volumes. Because customer data platforms can handle all types of data, you need to identify which data are going to be included. Part of building a unified customer profile, which CDPs excel at, is choosing what will go into that profile. Do you want a physical address? Only an email address? Maybe you’re looking for only certain pieces of information in that profile. Deciding what a unified customer profile looks like is a key step in the equation.
3. Determine your business rules – Before deploying a CDP, you need to decide what rules to judge your data against. Business rules play as big a role in customer data platforms as actual data. Your business rules can include picking the most current record in one case, choosing to use a more trusted input source over others in case of a tie, choosing the most complete record in another, and so on. Determining these business rules is a foundational step because they should align with your organizational priorities and your intention for the unified customer data profile.
Customer data platforms are powerful solutions for the modern business user. By providing an always-on, always-processing unified customer profile, CDPs allow marketers and other business units to gain a centralized point of data visibility into the entire customer lifecycle. In a marketplace where consumers increasingly leverage multiple avenues to interact with brands, this visibility into their behaviors, preferences, and interaction history pays substantial dividends.
January 31, 2018
Top 5 Customer Data and Engagement Trends for 2018
by Lindsay Bloom, SessionM
 Customer data and engagement strategies have evolved dramatically in the last few years and will continue to accelerate in 2018. According to Martech Advisor, the customer data platform industry is expected to grow at least 50% per year in the near future, reaching over $1 billion total revenue by 2019. Before your 2018 budget is allocated, check out our predictions for the five biggest difference makers in customer data and engagement.
Single View Technology
Many brands are struggling to meet customer expectations for brands to know who they are and anticipate their needs. The challenge for brands is that technology is evolving so quickly and changing how consumers are capable of engaging with a brand as well as their perception of a brand’s ability to engage with them. With far more channels to service than ever before, brands have to manage a complex environment with high potential for error.
Many brands are dealing with slow and static environments and historical, static views of the customer, slow data moving in batch between systems, manual list-based segmentation, and scheduled campaigns. With the right technology, brands can move to an environment where their view of who the customer is and what that customer is doing across different channels is updated in milliseconds by streaming data from all source systems into one operational profile in real time, so that when customers act you can react not only with marketing campaigns, but also with opportunities to drive the customers towards particular goals you might have around customer experience, as well as triggering off of behaviors.
Artificial Intelligence & Machine Learning
Artificial intelligence gives marketers the ability to turn an abundance of customer data into valuable insights on behavior to drive effective loyalty and engagement programs that are personalized to each customer.
Leveraging a customer data and engagement platform with AI capabilities enables marketers to to tap into key customer metrics such as customer lifetime value, probability to churn, RFM metrics (recency, frequency, and spend), along with propensity scoring to segment audiences. Is a customer likely to churn within the next fourteen days? Automatically send her a special discount on her favorite product as an incentive to get her back in the door. With AI, you can start to determine and predict customer behavior and send offers that motivate and impact their future behavior.
Customer purchases can occur at any time, making it difficult to understand which customers will return and how often they will purchase. Machine learning models can predict an individual customer’s number of expected future transactions, likely spend per transaction, and the probability the customer returns. From these predictions, you can estimate customer CLV ,or the future value expected for the customer’s relationship with the brand.
Security
Data security was likely already a major consideration for most brands when selecting third-party vendors; however, now more than ever the protection of customer data is of paramount importance to brands. According to The Forrester Wave™: Customer Identity and Access Management, Q2 2017, 71% of global enterprise security technology decision makers rate improving the security of customer-facing apps and services as a high or critical priority.
It’s also critical that brands take the proper measures to ensure that customers trust your brand and feel comfortable sharing personal information with your company. Customers will stop engaging with your brand if they don’t feel confident that you are protecting their data. The obvious ramifications of this potential chain of events highlight how customer loyalty and retention is inextricably tied to data security.
Omnichannel Experiences
Customers expect to be treated as individuals and have a consistent brand experience regardless of which channel they are interacting with. Every channel needs to be seamlessly integrated to deliver consistent customer experiences and lead each customer through his or her own engaging customer journey. Brands know that customer expectations have changed, but haven’t quite caught up--87% of customers think brands need to put more effort into providing a seamless experience (ZenDesk).
By creating a single view of the customer, brands can ensure that they are not only following the journey across all platforms and channels, but also reacting to it at the points of highest value. Cross-channel and cross-device marketing can be automated and triggered based on customer behaviors, interactions and more.
For example, a customer abandons a cart on her web experience, leaving a roll of paper towels and garbage bags. Instead of sending an email two days later about her abandoned cart, the brand is able to send the her a push notification as she enters the brick-and-mortar location later that day to remind her about the necessities she left behind, saving her an emergency trip out to a competitor’s store.
It’s white-glove, omnichannel treatment.
Loyalty As A Behavior, Not A Tactic
In the past, loyalty programs were centered on customers demonstrating loyalty to businesses; however, customer expectations are forcing marketers to rethink the value exchange to be more about a business demonstrating loyalty to its customers. How do brands do this? They serve up unique experiences, offers and content based on declared, observed and statistical data about each individual customer. In exchange for this devotion, customers reciprocate with greater frequency, bigger baskets and referrals.
The new loyalty paradigm has retired spray and pray, generic discounts in favor of customized offers based on an individual customer’s past purchase history. Leveraging historical data to determine which products a particular customer is most likely to purchase and then incentivizing a customer to buy those products increases reward redemption, generates incremental activity and decreases margin erosion.
For example, if you have a customer who comes to your coffee shop every morning for coffee, you could give that customer a free coffee as a thank you for his or her patronage. This might seem like a nice thank you and while customers might appreciate it, you are likely discounting to customer who are willing to pay full price. Rather, leverage that offer to increase recency, frequency, or spend (or why not all three!). Instead of the free coffee, try an offer for a free bagel tomorrow morning or an iced coffee in the afternoon. Think about the financial benefit you stand to gain by increasing customers spending $2 a day to $3 or $4, even just a couple days a week.
The best loyalty programs do more than just reward transactions; they have the power to influence crucial business metrics like retention, acquisition, spend and more.
January 29, 2018
What Is a Data Lake vs. a Data Warehouse?
By Julie Graham, Tealium
 Today, organizations are handling multiple types of data from different sources and to make sense of this incoming ‘gold’ (so to speak) they need a data storage and analytics solution. Where an organization houses their data in order to take action on it often leads to the discussion of Data Lakes and Data Warehouses. And while some organizations have heard of each solution, many are unclear on what the differences and benefits are and, ultimately, which solution is best for them.
I recently sat down with Ted Sfikas, Tealium’s Director of Solutions Consulting for North America and LATAM, to ask him about the differences between a Data Lake and a Data Warehouse. Is one better than the other? How can you best maximize the value of each solution? And what are his key recommendations for choosing the best type of solution for a brand?
So Ted, what is a Data Warehouse?
A Data Warehouse is a repository – it’s where structured data goes to rest. It involves engineers building ‘entry criteria’ for the data’s entry – these criteria are based on the data models that the organization wants to analyze. The end result is a well-structured set of data in and forms the schema. Data entering a Data Warehouse has some very specific requirements on it and is very controlled. The tables in the Warehouse used by the organization to produce meaningful analytics are preordained to assist with the most efficient production of this business intelligence.
A Data Warehouse is an important thing – it’s not a place where data is thrown in without a consideration of what will happen next. There are very distinct purposes for loading information into Data Warehouses, there are many technologies connected to it and they depend on conforming to those data principles.
So to summarize, a Data Warehouse is a repository of structured data that has been collected and organized according to a pre-built, rigid model (schema-on-write) based on very specific uses of that data. Because only very well-organized and specific data is collected with this approach, using this data once it’s in the Data Warehouse is fast and easy – but the setup is harder and you may potentially miss data that will be valuable in the future because you’re only collecting what’s in the schema.
And comparatively – what is a Data Lake?
A Data Lake is the response of the IT industry to providing data in an unstructured format. Data Lakes are made to handle the new, “Big Data” aspects of our industry. Think of the analogy with a box of bottled water, ready to drink, as compared to a Lake. The Data Lake is not packaged and ready to use, but it’s got all the same ingredients, it just needs to be put together. Data from external sources (ie: social, video, voice, text) gets poured into this lake through a number of different streams (ie: channels) all related to the customer, but again, it’s not in a structured format. What you end up with is one, massive table of data in the Data Lake where it’s simply not efficient to place analytical tools upon it like you can in a Data Warehouse.
In contrast – the way we work with data in a Data Lake is we use search and tagging capabilities to indicate which pieces of data we want to capture and place into a single object — after that structuring is done, we can analyze. Why have we done it that way? Because for some types of information, it’s a lot easier to get data into a Data Lake and deal with forming a schema later, as opposed to dealing with a Data Warehouse’s schema restrictions up front.
So to summarize, a Data Lake is a repository of unstructured data that’s not rigidly filtered during collection. Rather, the raw data is simply loaded into the lake and modeled and structured later (schema-on-read). Because way more data is collected with this approach, accessing the data takes a little more work and requires certain technology…but it takes very little setup and there’s no risk of missing valuable information.
That’s why a Data Warehouse is referred to as schema on-write because the minute you write the data to disc you have to have a schema in place. As opposed to a Data Lake where raw data enters the repository before it’s structured, and so it’s schema-on-read.
Is one better than the other? As in – who would want to use a Data Lake and who would want to use a Data Warehouse?
In larger companies where there may be a business intelligence department, multiple pieces and types of analytics may be being created in several business units per day. And even the savviest of professionals don’t want to be burdened with dealing with numerous sources of data and having to understand where it all came from – they want it ready to go and to be able to act on it. So in an organization with a larger business intelligence department, a Data Warehouse is much more suitable.
A Data Warehouse is suitable when data is being used in precise ways with the teams to backup managing the data in those ways. But recent uses of Machine Learning and resultant Artificial Intelligence have placed more of an emphasis on getting raw data; you’ll find that the traditional Data Scientist will likely be using a Data Lake because of this, as the information they need has to be dynamic and raw, and far more agile. These roles may not even know how they’re going to use the data when it is first collected, and that’s the point of the Lake.
How does Tealium fit into the Data Lake and Data Warehouse comparison?
What’s great is that Tealium’s solution works with both Data Lakes and Data Warehouses. Just like a Data Lake and a Data Warehouse are very complementary methods to storing, collecting and interpreting data – Tealium is like a third and complementary place for data to go as well.
Tealium is where technology can be pre-programmed to react to data in real-time. The Tealium solution brings in clean data, and then makes that data actionable in real-time. It’s complementary in the sense that it allows for governed data collection and enrichment, orchestrates actions based on business rules, and ultimately allows a business to programmatically define the formation of a customer profile in an automated fashion. Instead of spending days confirming how to work with customers that are changing all the time, those decisions are now automated by Tealium and the business is finally able to react in real-time, something a Data Warehouse and a Data Lake are not designed for. As this is all happening, Tealium can store data in Redshift and S3 repositories that the Data Warehouse and Data Lake can work with, and so Tealium extends the data supply chain to be available in real-time.
What are some of the key things you would recommend a brand look for and consider in choosing a Data Lake or Data Warehouse tool?
Most organizations today have a Data Warehouse, and many of them have heard of the Data Lake or are in the process of building one. It’s important to understand that the cost differences can be enormous.
According to Pricewaterhouse Coopers, a Data Warehouse can cost anywhere in the range of $10M to set up and build because there’s a lot of processes to create, models to build, and a lot of training to do. A Data Lake can be set up for a much lower cost, at around 20% of that of the Warehouse. Why? Because a Data Lake uses open source software like Hadoop, and by definition, will not require any lengthy modeling. There are open-source alternatives to choose from but organizations can also buy commercial-off-the-shelf software for their Data Lake initiative. So cost is a big factor. Organizationally, the company and its personnel must be ready for this change. Having the right people on staff and the right processes in place to leverage the results is mandatory.
Customers have a lot more choice with Data Lakes. And they can be a lot easier to set up.
I’d recommend looking at the organization first to see how it runs best. It may be better to get data in an unstructured, informal manner where you’re helping business units gain value in an informal way. If so, then a Data Lake would be best suited for them.
Thank you Ted!
Want more information on how Tealium’s solution is complementary to both Data Lakes and Data Warehouses? Contact us for a live demo today!
January 24, 2018
What’s the ROI for a Customer Data Platform? It’s In The Way That You Use It.
By Tom Quinn, Refined Path
I’m often asked ‘What’s the ROI from investing in a CDP?’ This is an important question as many organizations have already invested millions of dollars in marketing technologies and related support. Many of these have been very good investments while others have produced mixed results. When explaining my perspective, I channel the Eric Clapton song ‘It’s in The Way That You Use It,’ written coincidently for the movie ‘The Color of Money.’ I say this because while many technologies enable you to do one thing better, a CDP can support a range of strategies – which spreads the ROI burden.
In organizing this thinking, we’ll review three areas’: (1) Enabling the Right Strategies, (2) Thinking Near-Term and Long-Term, (3) Model for Calculating Return-on-Investment.
I. Enabling the Right Strategies
The CDP itself doesn’t generate ROI. You may only use it for one initiative or multiple initiatives, it depends on your business. But when developing both goals and strategies you should be aware of all of your options including how a Customer Data Platform can help your team. A few examples:
Advanced Segmentation: When you enhance your data in a CDP, you can leverage the data to create micro-segments on the fly and target valuable groups that you wouldn’t otherwise.
More Effective Media Buying: Why do firms invest in questionable third-party data? Because they don’t have a better option. A CDP enables you to use richer first-party data without the cost. Also can use CDPs to suppress acquisition offers to be served to your existing customers.
Cross Channel Attribution: Attribution should be calculated with cross-channel data that is integrated at the customer level. CDPs can enable you to transform your attribution modelling and assign true value to marketing activities across platforms.
Personalize Website & Digital Properties: Increase conversion and engagement by personalizing experiences based on rich, real-time data. The better the information, the better your ability to meet your customer’s needs.
Fuel Artificial Intelligence Initiatives: Data is the fuel for AI and Machine Learning. Even if you don’t know what these initiatives will be, having integrated data ready will help you when you are ready to get started.
Create A New Data-Related Product/Service: Publishers are using CDPs to launch new media offerings. You could use a CDP to enhance an existing service or launch a new one. If you have a valuable audience or hard to reach audience you may have the opportunity to sell data for a new revenue stream.
Enable Automation: Many services that are manually provided today will be automated tomorrow. Rich customer data will increase your options.
Orchestrate the Omnichannel Customer Journey: Many firms are engaging their customers more effectively by ‘Orchestrating’ experiences across platforms and touchpoints.
GDPR Strategy: A CDP can help with GDPR compliance, by helping organizations efficiently give individuals access to data when requested. But thinking beyond penalty avoidance, a CDP can improve the customer experience so they don’t opt out.
There are other opportunities as well, but the point stands that it’s important to understand what you can do with a CDP and develop the right strategies for your business. You may decide to invest in a CDP to support a single strategy. However, once you have it you should consider it for other opportunities as well. For example, a digital marketing manager may purchase a CDP for site personalization, but the relationship may expand to the email marketing manager, media buyer, BI analyst, and more.
II. Think Near-Term and Long-Term
Many firms prioritize initiatives that generate results right away. However, much of the value and benefits of a CDP will be realized in the future. So how do we take both into account?
You may want to consider an approach that starts by reviewing business objectives and customer needs on a short-term and long-term basis. For near-term goals, we look for ‘quick wins’ and strategies to drive sales and lower cost per acquisition. The potential for short-term returns vary, but for many firms it is possible to recoup the CDP investment in the first 12 to 18 months.
Longer-term, we recommend envisioning how customers are likely to purchase and engage vendors in your category. For example, are there potential disruption opportunities or threats? How can AI and automation change the game? Recognizing that most disruptors such as Facebook, Amazon, Netflix and Google leverage customer data to deliver personalized engagement, your customer data and personalization strategies will likely be critical. A CDP can be an important part of your plan. (See Exhibit.)
III. Refined Path’s ROI Model
Our base ROI model is the sum of your data asset value, performance lift, operational savings less costs. We customize this model for each client based on their situation.
The Value of Your Data Asset
Start off by recognizing your data as an asset. This may require a require a shift in mindset as most companies do not have formal data valuation processes. Why should you value data? Consider when Microsoft bought LinkedIn for $26 billion. The value isn’t based on the user interface, the most valuable component was the unique database of professionals. How important is the asset value? The Strategy unit of PwC has estimated that, in the financial sector alone, the revenue from commercializing data will grow to $300 billion per year by 2018.
So how do you calculate the value of your data asset?
This is complex exercise and one that you can discuss with your finance department. The clearest business case for data valuation is the acquisition, sale, or divestiture of business units that have significant data assets. Also, many companies have valuable data but no expertise in selling it. They may have to go to outside experts to value their data. Consider the value of what you have and how the value increases when you enhance it.
Aside from the value if you sold your data asset. How critical is it to you in achieving future goals? For example:
Category Disruption: You may have a plan to disrupt your category or fend off disruption. What are the data requirements for your plan? Can you calculate buy versus build?
AI Plan: Data is the fuel for artificial intelligence. Do you have AI plans in place, and if so what data will you need? Even if you don’t have plans in place, have a discussion – what are you likely to need?
New Data-Driven Products: As we mentioned in before, if you have a valuable audience, there may be new ways to monetize your integrated audience.
Even if you don’t have a solid plan in place for dealing with disruption or AI today that doesn’t mean you should hold off integrating your customer data. Building an integrated database takes time and by starting now you will have a better shape to plan and execute when you are ready.
Performance Lift: From Sales & Marketing
Customer Data Platforms enable you to serve your customers and prospects in more targeted and meaningful ways. Your ROI Calculation should include:
Incremental new business: A CDP will enable better targeting and nurturing of prospects. They also can alert you to important buying signals that you would otherwise miss.
Improved retention: Serve your existing customers better. Understand their needs and improve response. Monitor changes in net promoter score and retention.
Upsell / Cross-sell: In addition to nurturing prospects you can enable better nurturing of existing customers. You can improve listening, to understand their evolving needs and serve appropriate offers.
Realize Operational Savings
Automated sales, marketing and service: We are likely to see sales, marketing and service automation get more sophisticated in the in the coming years. Your CDP-generated data will be a core component in one-to-one engagement.
GDPR compliance: How will your company respond to consumer requests? The administrative costs alone could be prohibitive (never mind potential penalties). A CDP can play a significant role in both compliance and administration.
Media savings: With better data you can target more precisely and save dollars on wasted impressions and with better first-party data, you may decrease investment in weaker third-party data.
What Costs Should Be Considered?
Customer Data Platform: The most obvious is the cost for the CDP itself and implementation.
Professional services & training: Implementing your strategy requires expertise and bandwidth. This may include new hires or partnering with a consultant or specialist agency. With greater personalization you also may need to budget for increased content generation.
Maintenance & data enhancements: Over time you will need to continually maintain and enhance your customer data.
Summary
So, there you have it. Select the right strategies, take into account both short-term and long-term impact and then calculate your ROI. And I’ll wrap up by again citing Eric Clapton. In the film ‘The Color of Money’ he sang:
It's in the way that you use it
It comes and it goes
So, don't you ever abuse it
Don't let it go
Data is all around us. Some is picked up and harnessed while much goes unused. Be thoughtful in what you collect and integrate. Also remember to use it to both serve your customers better and achieve your business goals. Protect your customer’s data and ensure they see the value in how you use it. If not, they may let you go. But activating the right data with the proper strategies will lead to happier customers and shareholders.
January 22, 2018
Top 7 New Year’s Resolutions for Tag Management
By Ty Gavin, Tealium
 Say goodbye to bad habits and say hello to the new year ahead. Cheers to looking ahead into 2018 and exceeding goals for your organization.
As the leader and pioneer in tag management, here are Tealium’s Top 7 New Year’s Resolutions for Tag Management:
#1 – Act Now
Tag Management is now in its later stages of awareness, adoption and deployment within the digital marketing space. If you’re among those who are late adopters to this powerful solution it’s officially time to hop on board. Putting customers at the center of everything a brand does is a strategy that is isn’t going away anytime soon. Top brands are deploying tag management systems to not only deliver personalized experiences, but to also create more meaningful ones.
Tealium users around the globe have successfully deployed hundreds of tags on their sites which is enabling them to better collect, correlate and enrich customer data from multiple tools, touchpoints, and channels where their customers are interacting.
Are you working with a digital agency for your customer journey, website, automation, or analytics initiatives? You’re in luck. A good strategic digital agency should be well versed in tag management and is likely certified with the multiple top choice solutions.
Make 2018 the year that you plan for the integration and implementation of a tag management solution tool into your business practices and start delivering even better experiences for your customers.
#2 – Stay updated
If you’ve been using a Tag Management System (TMS) for a few years you likely have tags that are getting old or have even been replaced. The benefit of a TMS is to be able to make multiple updates with only a few clicks as any robust TMS tool will be able to ‘opt in’ to the latest-and-greatest features and functionality to keep your tags and data clean and correlated. For example, Tealium’s “Tag Status Checker” helps identify tag templates in need of an update. Google recently updated their suite of tags to use the new ‘gtag.js’ and Tealium now has these available in the Tealium iQ Tag Marketplace. As with any updates to a website we recommend testing and trying anything new out in a staging environment prior to going live.
#3 – Be a minimalist
Were you evaluating a vendor to add to your digital marketing stack, but as a result the tags were possibly never removed or shut off? Tealium iQ lets you know which tags are running (active) and will either pause or delete them.
Is there a digital marketing vendor on your site who is now redundant? For example you may have more than one analytics vendors, and while there may in fact be a case for running two, three or four is probably one too many. Keeping the General Data Protection Regulation (GDPR) in mind, it becomes imperative to only send data to places where it needs to go. Removing a tag will make implementation simpler and may even result in other “AJAX” code running sooner on a page. Being more concise and simple with tags may result in greater overall results.
#4 – Don’t just trim – build
As 2018 approaches, vendors that you’re working with may want to start running data through Machine Learning (ML) and Artificial Intelligence (AI) systems. Help them help you by sending them additional data points and other insights / analytics as it can potentially fuel predictive analytics and intelligent journey mapping based on the data.
Tealium’s mapping toolbox lets users see what is available to send to a vendor that a brand is working with. Many even have the ability to send custom attributes for no extra charge. Start bringing vendors into a more data driven and intelligent conversation by asking the question, “What other collection points of events and customer data can I send to maximize the value of your software?”
#5 – Be prepared for Privacy Management
Ensure you fully understand what your TMS has to offer and how it can be used to help your visitors manage their privacy settings. Make sure you’re marking applicable data as Personally Identifiable Information (PII) and protecting against data leakage.
#6 – Take some time to reflect
Meditation on the meaning of tags may not result in enlightenment; however, it could result in more strategic decision making that positively impacts your company’s bottom line. Start asking questions like “Am I taking as much action as possible based on my data?” For example, using Tealium’s Universal Data Hub to remove repeat website visitors from marketing segments ad spending can be better allocated towards acquisition. I suggest repeating that last statement out loud at least 5 times today for maximum results
#7 – Get Tealium’s Digital Velocity conference on your 2018 calendar
Our Digital Velocity event series is practical, inspirational, and a generally good time. Tealium now hosts these events that are designed to inspire, transform and activate attendees all over the world — and the customer success stories that are presented at these events are truly invaluable. But don’t take my word for it, check out this highlight reel from the most recent Digital Velocity San Francisco event.
Take a sip and celebrate. You made it through another year, and if you keep those seven resolutions in mind, you will set yourself up for a year of new innovation, strategic decisions that will have great impact and best of all new beginnings that will lead to success. May 2018 bring you the promise of innovation!
Want more information on how to get started with Tealium’s TMS solution? Contact us for a free 30 day trial today and take our TiQ product for a spin!
January 19, 2018
Why a Customer Data Platform is Key for Effective Marketing Measurement
By Sam Carter, Fospha
“One of the biggest pitfalls for performance measurement is to measure the ‘part’ with ignorance of the ‘whole,’” wrote Pearl Zhu, author of the Digital Master book series. Her quote perfectly reflects the growing need for businesses to understand marketing effectiveness across the ‘whole’ customer journey – rather than the individual value of a conversion from a single touchpoint (the ‘part).
But whilst the need for end-to-end marketing measurement is not a secret – with a survey by BrightFunnel (2017) finding that a whopping 91% of marketers agree that measurement of their efforts is a top priority - few find themselves able to achieve this goal, with only 13% of respondents ranking their measurement abilities as ‘excellent’ in this same survey.
Having the right tools in place is undeniably key to helping marketers accurately measure the impact of their work, yet 51% of marketers only use Excel spreadsheets and haven’t yet implemented CRM, marketing automation tools, or a dedicated attribution platform.
Multi-touch attribution modelling accurately assigns value to a marketing touchpoint based on its position in the entire customer journey, rather than based on an arbitrary metric such as first or last click. For this reason, it is the pinnacle of effective marketing measurement.
Underpinning multi-touch attribution modelling is a clean, single source of data. And for this, a Customer Data Platform is key.
Here are Fospha’s top three reasons why a Customer Data Platform is key for effective marketing measurement.
1. Single Customer View
Marketing measurement is contingent upon every step in the customer journey being measured. A Customer Data Platform gathers, integrates and centralises customer data from various sources to provide a single source of truth of what steps a customer has taken on their path to purchase. Data sources are uniquely tagged in order to ensure that customer data is being captured at every possible stage of the customer journey. With this single customer view, marketers can ensure that every interaction, across multiple channels and devices, has been taken into consideration when measuring the effectiveness of their marketing mix. Multi-touch attribution modelling is similarly contingent upon the data source being as rich as possible, and the Customer Data Platform’s single customer view therefore easily acts as the single, accurate source that should be input into the attribution modelling algorithm.
2. Integrate Online and Offline Data Sources
A Customer Data Platform also possesses the capability to integrate both online and offline data sources; an act that is often over looked by rudimentary measurement systems such as first or last click attribution. Without measuring both sources of data, marketers will never be able to fully understand how their marketing activities relate to customer conversions. Much like the single customer view acts as the single source of accurate data across the entire customer journey, a Customer Data Platform will easily integrate and stitch online and offline data points in order to enrich the customer view – whereas other integration tools do not necessarily possess this capability.
3. Connect Multi-Device Data
Shoppers use an average of 5 connected devices in their purchase process (CampaignLive, 2015) – an increase from 2.8 devices from the previous year. Clearly, the customer path the purchase is becoming increasingly complex, and marketers need to be able to take this into account when understanding how their marketing activities relate to conversions. For instance, are customers more likely to convert when they browse on a mobile and buy online, or is mobile advertising sufficient to induce a purchase? A Customer Data Platform easily accounts for this complex customer journey, by bringing together multi-device data so multi-touch attribution modelling can take into account the relationship between marketing activities and customer browsing devices to provide the most accurate measurement of value.
In summary, marketers know that accurate marketing measurement is key to effective marketing. But, in order to do this, they need to ensure they have an accurate source of data from which to carry out measurement techniques – such as multi-touch attribution modelling. The result of using a Customer Data Platform – with its ability to integrate and stitch various data sources – is an accurate display of a customer’s path to purchase across a myriad of marketing channels, devices and sessions.
January 17, 2018
CDP Vendor Classification: One More Draft
David Raab, CDP Institute
Remember the CDP classification project that’s been percolating since last September? Last week I circulated another draft approach to the CDP Institute Board of Advisors for comment. As always, their feedback was immensely helpful. In particular, they’ve convinced me that we can’t entirely separate “What makes a CDP?” from “How do CDPs differ from each other?” Answering that second question remains the primary goal for this project. But the answers to the first question are needed to put the second question in context. My solution is to present both sets of answers but to separate them so people can focus each as needed.
Stepping back a bit: this project began because I heard frequent complaints that about the wide variation among systems classified as Customer Data Platforms. This was causing confusion in the marketplace and leading to questions about whether CDP is a meaningful category.
My first line of defense was to clarify the CDP definition itself, for example in this Library paper and, more visually, this blog post. But that doesn’t help users who need to a rapid way to narrow their choices among 50+ CDP vendors. So the next step was a project to clarify which CDPs did what.
The result of this project must be easily understood by people who just starting to explore buying a CDP. These are likely to be marketers or marketing technologists who know the problems they face but are unfamiliar with the details of CDP systems. This argues for starting with use cases, which potential buyers should understand, and relating these to CDP features, which potential buyers won’t know much about.
But most CDPs support nearly all use cases to some degree, so that discussion immediately gets lost in nuances that will only confuse buyers looking for clear direction. Detailed feature checklists would be even more baffling to people who are just starting out. My solution in the past has been narrative descriptions of system features, which can capture some of complexity that checklists mask. But narrative descriptions also rely heavily on readers knowledge of they imply.
Our recent CDP Industry Update pointed towards a solution. Looking for an easily understood way to present information from that report, I found it made sense to group vendors into three broad categories: systems that only build the unified customer database; systems that offer the database plus analytics (especially list building, a.k.a. segmentation); and systems that include the database, analytics, and “customer engagement”, which really means message selection. Those are fairly easy distinctions that any marketer or marketing technologist should understand regardless of what they know about CDPs. Equally important, most buyers should have some idea of which they want their system to do, so this sort of classification should meet our primary goal of helping them decide which products to examine more closely.
This insight – that the classification scheme should be based on what marketers already know about their needs – lends itself to a further subdivision based on channels supported, since that's another thing marketers know from the start.. As it happens, CDPs vary considerably in which channels they support – and unlike generic use cases, support for a particular channel can easily be tied to specific system features.
The combination of broad functions plus channels supported should allow marketers with no knowledge of CDP technology to quickly identify which CDP vendors might suit their needs. Assigning the vendors to these categories is fairly easy since the decisions can be based on a few key features that can are relatively easy to assess. This won’t capture the subtle differences between systems but that’s something we leave to the marketers themselves. Indeed, it’s something we want marketers to do because only they can determine which of the hundreds of fine differences matter in their situation. And even if we did build that list, it would soon become out of date.
This line of reasoning led to the feature list I sent to the Advisors. Their comments led me to add a few features that distinguish CDPs from other types of systems. By definition, these apply to all CDPs, so they don’t help with choosing one CDP over another. But they do help marketers understand why they’d choose a CDP over a non-CDP. A list of shared features also helps clarify that the other listed items are NOT essential CDP requirements. The Advisors’ comments showed that’s wasn’t necessarily clear.
In short, we’ve ended up with two classes of CDP features:
- Shared CDP features, which are present in all CDP systems but not necessarily in other types of systems. So they constitute both a baseline for understanding what a CDP can do and a benchmark for comparing CDPs with alternatives. All are related to data management.
- Differentiating CDP features, which are present in only some CDP systems. This includes three subclasses of data management, analytics, and customer engagement.
It’s important to stress that even though all CDPs build a database, the features listed under "data management" are not required to do that. Rather, they are advanced features that support specific use cases. Channel-specific features illustrate this clearly: every CDP can load Web data by importing a flat file but not every CDP provides cookie management.
It’s also worth noting that the channel-specific features all relate to data management, not to analytics or engagement. Some CDPs also provide channel-specific engagement features, such as injecting personalized messages into a Web site. That is the sort of detail we’ve decided not to include. What we do capture is which systems have special features to manage data from a given channel and which do some type of engagement management. If a system manages data from a particular channel and does engagement, there’s a good chance it does engagement in that channel. You can be even more confident of the opposite: a system that doesn’t manage data from a particular channel almost certainly won’t do engagement in that channel. That’s all we need to know to quickly eliminate systems that don’t match the user’s needs. Some of the remaining systems will later be eliminated after closer examination, but that's okay: our job is only to reduce the number of systems that must be examined closely.
We’ll get to the actual feature list shortly. But first I want to state again that this is not intended to be a comprehensive list of CDP features. In fact, the goal is the exact opposite: to find the minimum list of features that can distinguish CDP vendors in each category. This simplifies matters for buyers, vendors, and whoever is assembling the data. The lack of detail also forces buyers to look more deeply at the individual products to tell them apart. That’s important because buyers too often rank products based on feature lists without bothering to understand which features are important in their particular situation.
Let me also clarify that one CDP can belong to several categories. A CDP could have analysis or engagement features without doing advanced data management features. Or it could do advanced data management in some channels but not others. The intent is for buyers to decide which of the features they need and find vendors that have them.
Without further ado, here is the list. It’s not yet cast in concrete, so please comment publicly or privately if you see something you think should change..
Shared CDP Features
Every CDP can do all of these. Non-CDPs may or may not.
- Retain original detail. The system stores data with all the detail provided when it was loaded. This means all details associated with purchase transactions, promotion history, Web browsing logs, changes to personal data, etc. Inputs might be physically reformatted when they’re loaded into the CDP but can be reconstructed if needed.
- Persistent data. The system retains the input data as long as the customer chooses. (This is implied by the previous item but is listed separately to simplify comparison with non-CDP systems.)
- Individual detail. The system can access all detailed data associated with each person. (This is also implied by the first item but is a critical difference from systems that only store and access segment tags on customer records.)
- Vendor-neutral access. All stored data can be exposed to any external system, not only components of the vendor’s own suite. Exposing particular items might require some set-up and access is not necessarily a real time query.
- Manage Personally Identifiable Information (PII). The system manages Personally Identifiable Information such as name, address, email, and phone number. PII is subject to privacy and security regulations that vary based on data type, location, permissions, and other factors.
Differentiating CDP Features
A CDP doesn’t have to do any of these, although many do one and some do all. These are divided into three subclasses: data management, analytics, and customer engagement.
Data Management.
These are features that gather, assemble, and expose the CDP data.
Base Features.
These apply to all types of data.
- API/query access. External systems can access CDP data via an API or standard query language such as SQL. It’s just barely acceptable for a CDP to not offer this function and instead provide access through data extracts. But API or query access is much preferred and usually available. API or query access often requires some intermediate configuration, reformatting, or indexing to expose items within the CDP’s primarily data store. Those are important details that buyers must explore separately.
- Persistent ID. The system assigns each person an internal identifier and maintains it over time despite changes or multiple versions of other identifiers, such as email address or phone number. This allows the CDP to maintain individual history over time, even when source systems might discard old identifiers. CDPs that use a persistent ID applied outside of the system do not meet this requirement.
- Deterministic match (a.k.a. “identity stitching”). The system can store multiple identifiers known to belong to the same person and link them to a shared ID (usually the persistent ID). This enables the system to connect identifiers indirectly: for example, if an email linked to an account is opened on a particular device, subsequent activity on that device can also be linked to the account.
- Probabilistic match (a.k.a. “cross device match”). The system can apply statistical methods and rules to identify multiple devices used by the same person, such as computers, tablets, smart phones, and home appliances. While many CDPs rely on third party services for this sort of matching, this item refers only to matching done by the CDP itself.
Schema-Free Data Management.
This refers to loading data without defining its contents in advance. This greatly reduces the effort to add a new data sources or new data types within an existing source. It is most relevant when dealing with unstructured or semi-structured sources such as Web logs, social media comments, voice, video, or mages. It can also load data from structured sources such as transaction systems. Semi-structured and unstructured data are typically managed with “big data” technologies such as Hadoop. Nearly all CDPs use some version of this technology but it’s only essential if clients have unstructured or semi-structured sources and/or very high data volumes. Some CDPs handle very high data volumes in structured databases such as Amazon Redshift.
- JSON load. The system can accept and store data through JSON feeds without the user specifying in advance the specific attributes that will be included. Additional configuration may later be required to access this data. There are some alternatives to JSON that offer similar capabilities.
- Schema-free data store. The system uses a data store that does not require advance specification of the elements to be stored. Examples include Hadoop, Cassanda, MongoDB, and Neo4J.
Web Site
This refers to interactions with the company’s own Web site, whether on a desktop computer or mobile device.
- Javascript tag. The system provides a Javascript tag that can be loaded into the client’s Web site and used to capture data about customer behaviors. Some CDP vendors provide full tag management systems but this is not a requirement for this item. This item does require that data captured by the Javascript tag can be associated with a customer record in the CDP database. This is usually done with a Web tracking cookie but sometimes through other methods.
- Cookie management. The system can deploy and maintain Web browser cookies associated with the client’s own Web site. The cookies can be linked to customer records in the CDP database.
Mobile Apps
This refers to interactions with mobile apps created by the company.
- SDK load. The system offers a Software Development Kit (SDK) that can load data from a mobile app into the CDP database. It must be able to associate the data with individual customers in the CDP database. This is usually done through an app ID. Other SDK features such as message delivery are not a requirement for this item.
Display Ads
This refers to interactions through display advertising networks, including social media networks.
- Audience API. The system has an API that can send customer lists from the CDP to systems that will use them as advertising audiences. The receiving systems might be Data Management Platforms, Demand Side Platforms, advertising exchanges, social media publishers, or others. Ability to receive information back from the advertising systems is not a requirement for this item.
- Cookie synch. The CDP can match its own cookie IDs with third party cookie IDs to allow the marketer to enrich profiles with external data or reach users through advertising networks.
Offline
This refers to interactions managed through offline sources such as direct mail and retail stores, where the customer’s primary identifier is name and postal address.
- Postal Address. The system can clean, standardize, verify, and otherwise work with postal addresses. This processing is reduces inconsistencies and makes matching more effective. Systems meet this requirement so long as the address processing is built into system process flows, even if they rely on third party software. Systems that send records to external systems in a batch process do not meet this requirement.
- Name/Address Match. The system can find matches between different postal name/address records despite variations in spelling, missing data elements, and similar differences. As with postal processing, systems can meet this requirement with third party matching software so long as the software is embedded in their processing flows.
Business to Business
This refers to companies that sell to other businesses rather than to consumers.
- Account-level data. The system can maintain separate customer records for accounts (i.e., businesses) and for individuals within those accounts. This means account information is stored and updated separately from individual information. It also means that selections, campaigns, reports, analyses, and other system activities can combine data from both levels.
- Lead to Account Match. The system can determine which individuals should be associated with which account records, using information such as company name, address, email domain, and telephone number. This excludes processing done by sending batch files to external vendors.
Analytics.
These are applications that use the CDP data but don’t extend to selecting messages, which is the province of customer engagement.
- Segmentation. The system lets non-technical users define customer segments and automatically send segment member information to external systems on a user-defined schedule. Ideally, all data would be available to use in the segment definitions and to include in the extract files. In practice, some configuration may be needed to expose particular elements. Systems meet this requirement regardless of whether segments are defined manually or discovered by automated processes such as cluster analysis.
- Incremental attribution. The system has algorithms to estimate the incremental impact of different marketing activities on specified outcomes such as a purchase or conversion. Attribution is a specialized analytical process that relies on the unified customer data assembled by the CDP. Algorithms vary greatly. To qualify for this item, the algorithm must estimate the contribution of different marketing contacts on the final result. That is, fixed approaches such as “first touch” or “U-shaped distribution” are not included.
- Automated predictive. The system can generate, deploy, and refresh predictive models without involvement of a technical user such as a data scientist or statistician. This usually employs some form of machine learning. There are many different types of automated predictive; systems meet this requirement if they have any of them.
Engagement
This refers to applications that select messages for individual customers. It does not include content delivery, which is typically handled outside of the CDP.
- Content selection. The system can select appropriate marketing or editorial content for individual customers in the current situation, based on the data it stores about them, other information, and user instructions. The instructions may employ fixed rules, predictive models, or a combination. Selections may be made as part of a batch process.
- Multi-step campaigns. The system can select a series of marketing messages for individual customers over time, based on data and user instructions. The message sequence is defined in advance but may change or be terminated depending on customer behaviors as the sequence is executed.
- Real-time interactions. The system can select appropriate marketing or editorial content for individual customers during a real-time interaction. This requires accepting input about the customer from a customer-facing system, finding that customer’s data within the CDP, selecting appropriate content, and sending the results back to the customer-facing system for delivery. The results might include the actual message or instructions that enable the customer-facing system to generate the message.
There you have it: 26 relatively simple items that I think offer a meaningful way to differentiate among CDP systems. What do you think?
January 12, 2018
Signs You Need a Customer Data Platform
By David Raab, CDP Institute
 Someone asked me the other day for a list of reasons someone would buy a CDP and what they'd get. The results seemed worth sharing. Here they are.
Signs You Need a CDP
• Your existing customer-facing systems capture useful data about your customers, but you can’t collect it all in a single place.
• You can collect customer data from multiple systems but can’t enhance it with data from external sources.
• You can collect customer data from multiple systems but can’t connect items that refer to the same individual to create a Single Customer View.
• You can build a Single Customer View but only have access to predefined summary information, such as segment assignments, and not underlying details such as details of transactions or Web page views.
• You can build a Single Customer View with detailed data but can’t analyze it, build predictive models, or run machine learning algorithms against it.
• You can build and analyze a detailed Single Customer View, but the data isn’t available to systems that create outbound marketing campaigns (e.g. email or advertising audiences) or real time interactions (e.g. Web site personalization)
• Your IT department has promised to provide a Single Customer View but they haven’t said when it will be available, what it will cost, or what it will actually do. Or they’ve made promises and failed to deliver.
• Your marketing cloud vendor has promised to provide a Single Customer View but they haven’t said when it will be available, what it will cost, or what it will actually do. Or they’ve made promises and failed to deliver.
• You have a detailed, actionable Single Customer View but it’s hard to add new data sources or change how data is processed.
• You have a detailed, actionable Single Customer View but it’s hard to connect it to new systems so they can use its data.
• You have a detailed, actionable Single Customer View but the data is days or weeks out of date.
• You have a detailed, actionable Single Customer View but it costs a fortune to operate.
• You have customers in the European Union and can’t meet the requirements of General Data Protection Regulation (GDPR) and ePrivacy to capture permissions, track usage, respond to data access and removal requests, etc.
Benefits You Can Get from a CDP
• Uncover data quality problems and inconsistencies by comparing data from different systems about the same customer
• Create a ‘golden record’ containing the most accurate information about each customer across all systems.
• See patterns in customer behavior across large numbers of customers.
• Analyze customer journeys based on detailed data.
• Compare behaviors of customers from different sources, with different attributes, who saw different marketing campaigns, who bought different products, etc.
• Calculate value of customers, in aggregate and by customer segment (based on source, attributes, etc.)
• Identify segments of customers with similar attributes and behaviors.
• Automatically assign customers to standard segments and use these segments in marketing campaigns.
• Build predictive models based on detailed customer data
• Calculate the value and predict behaviors of individual customers based on their personal history
• Find the best treatment for each customer in each situation, based on their personal history and other relevant information (product inventory, local weather conditions, competitive activities, etc.)
• Use customer data in personalized messages (and be sure it’s correct).
• Coordinate customer treatments across channels based on a shared view of their data.
• Synchronize customer data with other systems such as CRM and DMP.
January 2, 2018
CDP Industry Doubled in 2017: Latest CDP Industry Update
by David Raab, CDP Institute
 The CDP industry continued its strong growth in the second half of 2017, with new companies appearing from outside the U.S. and more products offering customer engagement features.
That's the headline from the latest CDP Industry Update, the CDP Institute’s comprehensive analysis of CDP vendor growth and distribution. Compared with the first report, one year earlier, coverage has grown from 24 to 52 vendors. Some are brand new companies but most are existing firms that have repositioned as CDPs.
Companies outside the U.S. accounted for 35% of the vendor count, although just 29% of the employees, 19% of total funding and 12% of new funding, In other words, U.S. firms remain the heart of the industry even though their share is shrinking.
Similarly, CDPs with customer experience features such as personalization and campaign management have grown from 29% to 48% of the report, but firms offering just data assembly and analytics features account for 57% of employment, 72% of total funding, and 91% of new funding. Nearly all firms added to the list since the original report are in the customer experience category. (The report only includes CDPs, so firms in the customer experience category also provide data assembly and analytics.)
Total employment among listed firms more than doubled, from 2,000 to 4,400, as measured by LinkedIn. Employment at firms listed in the original report grew to 2,500, a 27% year-on-year increase. The balance of the growth was from firms added later. The lists of largest and fastest-growing individual firms were dominated by data assembly and analytics vendors, although customer experience firms showed some of highest percentage growth, starting from small bases.
In short, the report presents a vibrant industry that’s expanding both through organic growth at existing firms and by accruing new participants. If you’re looking for dramatic tension, there’s a conflict between the older, larger data assembly and analytics vendors and the newer, smaller customer experience products. The drama may be a bit artificial but it does point to a choice that marketers must make between using the CDP as a shared data source and using it to actively manage customer experience. Chances are both approaches will succeed, since different marketers will make different choices depending on their situations. But those who enjoy a good horse race should feel free to place their bets on which will emerge as the most common selection.
Download the full report here.
December 26, 2017
Survey: Most CDP Institute Members Want General Information
By David Raab, CDP Institute
It took three emails within 24 hours but enough members of the CDP Institute responded to our survey to provide useful results. The main goal was to understand what members wanted from the Institute. The answers were quite clear: most want general information and industry research. Smaller numbers wanted help with picking vendors, such as vendor comparisons and evaluation checklists. Even fewer want post-implementation services such as training, peer discussions, and conferences.
The survey also asked background questions about the respondent’s existing systems and status of CDP in their organization. There was a bit of differentiation: people with CDPs deployed or in process were a little less interested in general information and surveys, and a little more interested in case studies, peer discussions, and conferences. Company size and business type showed more of split: people from smaller companies were much more interested in practical resources like evaluation checklists, lists of consultants, training, and peer discussions. People from larger companies were particularly interested in vendor comparisons. B2B marketers of all sizes were more interested in industry research and surveys than B2C marketers, probably because many of the B2B marketers themselves sell CDPs or related products and services.
Since CDP Institute members are anything but a random sample of the marketing community, their responses don’t tell us much about the industry as a whole. Still, it’s worth noting that big B2C companies were much more likely than others to have many disconnected systems and to have a CDP deployment in process or planned for the next future. This supports the general view that B2C enterprises are the core of the CDP market – although in absolute numbers, there were more CDP deployments under way and planned at B2B and small B2C firms.
You can download the full survey report here. Enjoy, and thanks again to those who participated.
December 21, 2017
Retaining Customes Requires Understanding Customers
By Amy Cross, NGDATA
For any business that provides a product or service to customers, the act of finding, targeting and obtaining new customers is always going to be among its top priorities. But, what many businesses tend to forget is that once a customer makes the first purchase, there is much more to be done in the customer relationship. Smart businesses know that the first transaction is really just the beginning, and that the real business value lies in retaining that customer.
It’s far less expensive – not to mention much easier – to keep existing customers than it is to find and convert new customers. Because of this, customer retention is more important than ever to organizations, especially those that consider customer lifetime value. In order to grow customer retention rates, you need to focus on engaging customers, delighting them with their experience, exceeding their expectations and building their loyalty. According to consulting firm Walker Info, “By 2020, customer experience will overtake product and price as the key brand differentiator.”
Of course, keeping customers happy so they keep coming back is a critical component of your business’ success. Organizations implement customer retention programs to retain as many customers as possible and to make the entire customer journey a valuable one.
Retaining customers requires giving them an excellent customer experience throughout their entire lifecycle by personalizing every interaction. The only way to deliver truly personalized and relevant experiences is by completely understanding your customers.
That’s where your customer data comes into play. Your customers generate data at every touchpoint, but harnessing all of that data is a nearly impossible task without utilizing data-driven technology. You can eliminate the data silos, connect the dots of a customer’s interactions across multiple channels, and build a comprehensive, holistic customer profile to gain a real-time, trending, and complete view. When you truly understand your customers, you can act on potential churn well before your customer considers it.
For further interest:
December 18, 2017
A Single View of the Customer is Essential to Your Success
By Patrick Tripp, RedPoint Global
 The modern customer creates a veritable treasure trove of data through dozens of digital and physical touchpoints, all of which can be actioned to craft more relevant messaging and power more engaging experiences. The problem is that much of this data is split into internal silos based on specific technologies and closely guarded by internal departments.
It’s because of these data silos and fragmented solution portfolio that brands have largely failed to maximize the business value of their customer data. This must change. Brands can ill afford to continue the current state of disconnected data and point solutions. What brands need is a solution that can unify disparate consumer data into the single view of the customer that will power contextually relevant experiences regardless of channel.
The Power of a Single View of the Customer
Consumers live a connected, omnichannel life with seamless transitions between digital and physical touchpoints. The same customer who might browse the racks at a retail store could simultaneously be checking competitors’ websites for a better price or searching for coupons on the retailer’s own site. Adding to this environment’s complexity is that consumers expect to have a consistent brand experience across all channels and touchpoints; they also want to be recognized as loyal, repeat customers and gain all the benefits that entails. Native online retailers have already accomplished this goal, and consumers expect that every brand should be able to do the same.
Legacy brands across all industries have failed to create a common brand experience across online and offline engagements. This failure is tied to the lack of a unified view of the customer. Recent CMO Council research bears this out, with only seven percent of brands able to deliver real-time data-driven engagements across online and offline touchpoints. The single view of the customer, also known as a “golden record,” is vital for success in the modern business landscape.
Most brands store customer data in functional and channel-specific silos tied to solutions that don’t share information. Having a single view of the customer means breaking down internal silos and blending anonymous and known data into one composite customer profile that showcases a consumer’s entire interaction history with the brand. Once this customer data is unified, brands can more readily deliver the right interaction at the right time, just like how becoming one with the Force allowed Luke Skywalker to fire the proton torpedoes that destroyed the first Death Star.
Brands may not be fighting a Galactic Empire, but the lack of ability to understand customer behaviors and preferences does have a dramatic impact on revenue. Boston Consulting Group recently found that personalization in retail, health care, and financial services will push a revenue shift of $800 billion over the next five years to the 15 percent of companies who get it right. Brands can’t personalize effectively, and capture that revenue bump, if they lack a single view of the customer.
Customer Data Platforms and the Single View
The desire to unify customer data into a central location is not a new one. Legacy data management solutions, such as data warehouses and data lakes, already accomplish this goal for a variety of data types and structures. But it needs to be said that collecting data into a central database is not the same thing as making that data available to business users. A single view of the customer is functionally useless if it can’t be easily accessed by the departments who need it most.
This is where customer data platforms (CDPs) come in. The idea behind a CDP is to maintain an always-on, always-processing customer golden record that is available at low latency throughout the enterprise. CDPs ingest any type of data, regardless of structure, at batch and streaming cadences and unify it into a central point of data visibility and control. The true power of a CDP lies in making this unified data accessible by the solutions and departments that need it most.
What this means from a functional perspective is that business users can access the data they need without making endless requests to the IT team. This enables IT to focus on tasks of greater strategic value to the organization, while also empowering business users to act at the speed of the customer. Because of its ability to unify data regardless of source, type, and cadence, CDPs are a foundational component of an effective omnichannel customer engagement strategy. The prowess of a CDP at connecting customer data also opens the door to effective real-time customer engagement, which is increasingly vital for the modern connected consumer.
Unified Data Leads to Engagement Success
McKinsey recently found that 50 percent of customer interactions now happen during a multi-event, multichannel journey. For most companies, this data is spread among multiple departments and multiple technology silos. This siloed data environment no longer functions as well as it once did; now, brands that can unify customer data will succeed where those who retain internal silos will falter.
CDPs are the technological solution to this problem, enabling brands to craft contextually relevant messaging that reaches consumers in the moment of need through the channel they prefer. The automotive service center company Monro recently experienced this when they deployed the RedPoint Customer Engagement Hub™, which is underpinned by our customer data platform. Monro used the RedPoint solution to unify its customer data and deliver more relevant messaging to consumers through multiple channels.
Achieving a single view of the customer is an important goal for the modern brand. Customer data platforms achieve that goal and, more than that, enable companies to operationalize their customer data in such a way that they more effectively engage with the connected consumer. Consumers have already moved on to a life with seamless integration between the digital and physical worlds. Brands must follow if they wish to retain their customer base and enhance revenue in the coming years.
December 14, 2017
The Good, The Bad And The Ugly: Beware of Outlandish GDPR Claims
By Jessica Davies, Tealium
 Uncharted waters are something the digital media industry should be used to by now. But the General Data Protection Regulation is a whole new ballgame.
With six months to go until the law is enforced, there’s an equal amount of sense and nonsense being spouted about how to tackle it.
Here are some of the more startling claims overheard in the wild:
The aggressive sales pitch
Supposed “GDPR experts” are making loud claims. One publisher executive balked at being pinned by a third-party vendor with the pitch: “Without using our auditing services, you leave yourself open to extinction!”
The overconfident
It may seem hard to believe, but despite all the scaremongering around the GDPR, a lot of companies are still in denial about it. “The amount of times I’ve heard ad networks say, ‘GDPR and ePrivacy aren’t going to affect programmatic advertising’ — it’s crazy how many companies are still in denial about it,” said a digital marketing exec.
The artful dodger
Beware any niche third-party tech vendor that claims to already be 100 percent GDPR-compliant (there are plenty of them), as it’s highly unlikely they are. The final direction on how to obtain consent and share data won’t be published until next month, so avoid those claiming to be clairvoyants.
The lazy vendor
More than a few publishers’ jaws have dropped after receiving stony responses from vendors when inquiring about how they’re preparing for GDPR compliance. Some vendors seem to believe publishers alone will be on the hook for fines. As such, they’re sitting pretty, doing nothing and expecting publishers to gain consent on their behalf.
The worryingly complacent
Some are still not taking action because they think the outcome of GDPR enforcement will be akin to that of the European Union’s ePrivacy Directive in 2011: a load of fuss made about nothing. This is not the case, however. “Most worryingly, a company stated to me they wanted to do absolutely zero, as they cited it being the EU Privacy Directive all over again. [It’s] a massive gamble not just in terms of fines, but business reputation, too,” said Lindsay McEwan, vp and managing director of Europe for tag management vendor Tealium.
The blunt approach
There’s talk that some agency holding companies plan to cap programmatic spending to limit exposure to GDPR penalties, according to industry sources. Some are also looking to instigate new insertion orders with GDPR-related terms and conditions. “Agency operating groups have ordered [their agencies] to limit exposure by reducing the amount of programmatic spend to 20 percent of total digital spend up to the end of September, I suspect to boost their own internal supply marketplaces,” said an ad tech exec. “Programmatic is a shitshow. Makes sense to avoid it and be seen to avoid it.”
The bloodsucking lawyer
GDPR lawyers, consultants and information security companies are making an absolute killing, and that will only increase. It’s best to boost your own GDPR knowledge to make your business less at their mercy.
Image courtesy of UKseobiz.com
December 11, 2017
The Omni-Channel Customer Experience: Be Where Your Customers Are
By Amy Cross, NGDATA
 Customer expectations have never been higher. We are living in an omni-channeled, constantly-connected and instantly-gratified world where customers want better value out of the brands they choose to do business with. And, in today’s multi-faceted, digital world, the way customers communicate is all-encompassing – email, voice, text, TV, social media, website. There are countless ways to engage and interact today.
However, customers don’t think in terms of communications channels. They simply want an answer to their question or a solution to their issue. They may reach out via voice, through your website or app, or through your social media platforms – and not always consistently. On top of that, as mobile is now king, the lines between channels can start to blur.
If you’re only able to see your customer conversations in silos, then you’re unable to have a complete view of all interactions. Being able to connect all of the dots is crucial for successful customer experiences. You need to have access to each and every customer’s communications preferences, behaviors and context to take into account every interaction on every channel. You want your customers to be able to connect with your brand via all of your communications channels, and have the ability to pick up where they left off on one channel and continue the conversation on another.
Once you are able to engage with your customers via all channels, you’ll be able to pin-point the channel that’s optimal for each point in the journey for each type of customer – at the optimum time. Truly customer-centric brands expect their customers to jump from one channel to another, be it digital or physical. But jumping channels needs to be 100% on the customer’s terms, not yours. Every time you interact with your customers, through any channel, you have the opportunity to extend the relationships and deliver value.
Are you able to provide your customers an omni-channel experience?
For further interest:
December 7, 2017
How Many Campaigns Are Too Many: Seeking the Magic Number
By Omer Liss, Optimove
 As he’s clicking the ‘send’ button, every marketer wonders– is this the one campaign that will bring back my churned customers, or is this the one that could cause my unsubscribe rates to soar? These 5 steps will help you decide
One of the toughest quandaries every marketer faces is deciphering the number of campaigns they need to send to their customers: What should the frequency look like? What amount will produce the best results?
To start answering these questions, we should first understand where they originate from: sending out a large bundle of campaigns may cause the exact opposite reaction from the desired result – serving as a distraction. The ideal case is that each campaign is impactful, but for marketers, it’s a delicate balance. Campaign bombardment can lead to a reduction in the campaign status and can cause lower open rates.
The customer will become annoyed, and you’ll lead them to the one place you don’t want them to go – the unsubscribe button, that’s where you lose them, oftentimes for good.
To try and mitigate this dilemma, we will need to dive deep into our data in order to better understand our customers behavior and feelings. In these 5 steps, we’ll try to help you reach this magical number – the optimal number of campaigns
Step 1: Definition
Let’s define the variables we want to examine:
Campaign: what is a campaign, and what purpose does it serve? Does SMS fall under this category? What about push notifications? Or, do we only want to test our email program? It’s important to stress that defining the campaign means studying every email sent out to a specific customer. Hence – sending 10K identical messages to 10K customers, is 10K different entities.
Period of Time: this term also derives from the question posed earlier. Is the aim figuring out how many campaigns we need to send during a week? 10 days? A month? Take notice in our examples that will follow, that the time period is not set according to a calendarial term. As such, seven days is a week, no matter which day we began counting. We think of a customer who received three campaigns (on Saturday, Sunday and Monday), as a customer who received three campaigns in a row, although technically, they were delivered on two different calendarial weeks.
Maximum Break Between Two Campaigns: defining this term will allow us to establish what a sequence is. We can decide to leave this term empty – hence, there’s no consideration on the maximum break between campaigns. On the other hand, we can set the number to 1, determining that any campaigns sent in a row one right after the other will be considered consecutive campaigns.
Using this set of characterizations, we can map and define every sent campaign by looking back at the time period we set and the number of campaign sent before. Here are some simple examples:
In example A, the time period is seven days, with no set maximal break between campaigns.
In example B, the time period is six days, but the maximal break is 1. Therefore, although the campaign is the 5th during these 6 days period, it’s the 4th that answers our maximal break scenario.
Response: to define the effectiveness of a campaign, we need to establish what constitutes as successful. Is it opening the email, placing a deposit, or having an active day on the site? This must be a binary answer – yes or no questions and responses. In most cases, the response will be measured on the specific day the campaign was sent out. In the example we’ll show, the response was set as day of activity.
These definitions need to be well established, as they will help us come to more fundamental answers during our research. For this piece, all our testing will be based on example A.
Step 2: Mapping
The mapping process isn’t simple, and it’s quite technical. In this stage we define each campaign according to the definitions we set in step 1. After mapping them, we will look for a response from the customer. At the end of this basic process, we can check the division of the various types of executed campaigns, and conclude the response rates (simply divide the amount of “positive responses” with total amount of sent campaigns). You can also create this analysis from simple division using criteria such as – age, country, VIP level, lifecycle stage etc.
In the table below, we see a benchmark of 12 gaming sites (17 million emails), dated from the beginning of 2017. In this case, there was a second division of customers while they received their campaigns – to new customers and active customers – to better understand the frequency alterations during the different lifecycle stages.
By this mapping, it’s clear that most emails were 2nd campaign types. It’s also obvious that the campaign distribution for the 3rd and 4th campaigns is higher at the ‘New’ lifecycle stage. That’s because the marketing plan is more organized in the new stages of its customers.
Going forward, the next stage is understanding the response rate of different campaigns types. Look at the next table:
Here we can see the response rate for email campaigns – measuring the active days on when the customer received the campaign. Active customers respond better (26% to 17% on a non-weighted average), except for the first campaign sent, which is significantly higher for new customers.
This graph, which again gathers the data from 12 different brands, doesn’t show substantial changes. When conducting your research, try to look for the main highs and lows, in order to establish your threshold.
Step 3: Different Measures
After formulating the right threshold for each group, we can run the analysis again, while changing the criteria. The final results—that determine the right number to send for each group during a specific time--must be formulated after considering differing variables. And so, even if we see a massive downfall in the response rate of campaigns that are 4th in line, we should combine it into more tests in order to reach a conclusion.
The unsubscribe rate is a good example. A number of irregular campaigns or a high frequency of campaigns can rapidly raise the amount of unsubscribes, and cause some major losses.
This next table shows the unsubscribe benchmarks for different campaign types:
We can see how much higher the unsubscribe rate is in the 1st campaign in seven days, than the campaigns that follow. Again, that is probably supported by the fact that a 1st campaign creates a unique stimulation, and many customers will make their decisions according to this communication. We also notice that the unsubscribe rate for active customers begins to rise considerably after the 5th campaign in seven days. It’s something we will have to take into account when setting our final conclusion.
Step 4: Micro-Segmentation
As stated previously, we wouldn’t want to decide based on groups that are too general. To dive deeper into our customers’ behaviors, we need to create more sub-divisions – to reach more specific groups. Examining different lifecycle stages is always a good way to go, but consider whether any sub-groups that may act differently. Do VIP players need to be tested differently? What about other players with different product preferences?
In the next example, we divided our active customers from the previous benchmarks into different groups, defined by their frequency – the average time (in days) that passed between activities.
We can see from the first table that active customers who have a high frequency rate (F<3) responded better to numerous and more recurrent campaigns. We can alsosee that customers who are active less frequently (F>7) show a drop-in response rate as the number of campaigns increases.
From the unsubscribed table, we can learn that customers who are less frequent will experience a higher jump in their unsubscribe rates percentages from the 4th campaign on.
Step 5: Conclusion
After executing numerous tests, we can start framing an answer for each group we chose. In an absolute world, all our measurements will point to (?) the same threshold and we could determine the right number of campaigns and their frequencies. But among the results, we will probably encounter some contradictions. We may see a response rate hike on 2nd type campaigns in seven days as well as a rising unsubscribe rate. To settle these types of results we’ll include additional calculations. Always try to choose the right measurement for your needs that will best serve the objective of your target groups. For example: if we look at the ‘active customers who are at risk to churn’ group, the most important measurement for that group is the response rate, as we want them to be more active. As for the ‘active customers with no risk of churn’ group, the crucial measurement is the unsubscribe rate, because we want to be able to contact them.
Once you’ve decided on the numbers of campaigns, implement the frequency results in your marketing programs by creating a test group. Check whether the customers who are receiving the definite number you came up with are responding better than your other customers.
The method suggested here is just one of many, that deals with the important question – how many campaigns should I send and at what frequency? There are additional research methods to incorporate, depending on your data, but try and implement them wisely in order to reach that optimal, yet elusive number.
December 4, 2017
How to Achieve a Dynamic, Single View of the Customer
By Lindsay Bloom, SessiomM
 Many brands are struggling to meet customer expectations because technology is changing at such a rapid rate. This is changing how consumers are capable of engaging with a brand and their perception of a brand's ability to engage with them. Many of the problems and barriers that prevent a brand from meeting those expectations start with data silos and bottlenecks across the different systems that a brand uses for customer engagement.
When we’re talking about the customer engagement ecosystem, we’re referring to marketing campai | 
