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June 17, 2019
Leveraging Customer Analytics to Reduce Churn Rates and Grow Marketing ROI
By Chuck Leddy, Zylotech
Customer retention, often measured by “churn” rate (the percentage of existing customers who leave in a specified period of time), is the most important success factor/KPI for any business. When customers stay, your business can build long-term profitability through repeat purchases, as well as cross-selling and up-selling opportunities. When you retain customers and optimize their lifetime value, you also create brand ambassadors who give you priceless word-of-mouth marketing and referrals. “Churn,” on the other hand, is a revenue killer.
As a recent Forbes article explains, “it can cost five times more to attract a new customer, than it does to retain an existing one. Increasing customer retention rates by 5% increases profits by 25% to 95%.” Not to belabor the point, but your business simply must retain existing customers in order to grow. This post will explain how customer intelligence via analytics can help you reduce churn.
Customer analytics for the customer journey
Customer analytics can drive retention and engagement throughout the entire customer journey, from generating top-of-funnel leads to the mid-funnel nurturing of leads to bottom-of-the-funnel conversion and closing, turning leads into revenue. Knowing more about your customers, which customer analytics enables, is the best way to engage your customers with relevant messaging that keeps them moving through the funnel to conversion (and greater lifetime value).
For example, you can improve lead generation with bots backed by machine learning who perform quick engagements with your customers, answering basic questions and routing them to relevant products, services, and salespeople. Contrast this real-time, bot engagement with a website form that a prospect might fill out, only to wait weeks for a busy salesperson to answer. Which of those two scenarios drives more engagement, faster?
Customer analytics can help “solve” customer churn
The best way to leverage customer analytics is to map out your entire customer journey and identify places where analytics can help (places where your funnel is “leaky”). In its simplest form, the process would work as follows:
Let’s identify customer “churn” as a big problem-to-be-solved (see step 1), which it certainly is. In step 2, you would need to collect and then leverage relevant data to better understand where and why customers are leaking out of your funnel. Then you’d build a model based upon this data, deploying machine learning, in order to help you predict the moments and the reasons your customers leave. So instead of passively watching customers leak from your funnel, you would know why they leave and be able to proactively engage them to prevent churn.
Machine learning basically uses math, statistics and probability to find connections among variables in your data, helping you optimize important outcomes such as retention. These machine learning models get even smarter at making predictions by constantly integrating new data. The result? You get data-driven insights that lead to marketing actions that retain your customers.
In another example of driving retention and ROI, you might apply customer analytics to better understand your customer’s past purchasing or browsing history, and then build a predictive model that could anticipate the next product a customer (or customer segment) might be interested in buying. This predictive model, using machine learning, would then help you identify the right customers (and the right times) for up-selling or cross-selling opportunities.
To do all this and more, you need the capacity to build models based upon quality data and deploy machine learning so these models get smarter over time, driving relevance and stronger customer engagement. You need a data and analytics platform that allows you to make your data actionable (the old saying remains true, “garbage in, garbage out”). By leveraging an automated customer data platform with machine learning analytical capabilities, you can leverage your data to reduce churn and boost ROI.
June 14, 2019
How Hyper-Personalization Helps Build Loyal Customer Relationships
By Venu Gooty, Element Solutions
Businesses and brands have always strived to segment and target their customers to be able to adapt their communication to the exact client needs. With the advent of the digital era, the customer segmentation is now undergoing an enormous transformation.
Today, it’s the age of hyper-personalization, a paradigm shift in the marketing industry that’s made the biggest businesses sit up and take notice. Hyper-personalization demands pushing the boundaries of personalization to an individual level and adapting functionalities and interactions in real time to offer each and every customer a truly unique experience that delights them and keeps them coming back for more.
For effective hyper-personalization, customer data is a prerequisite and rightly so, since it is one of the industry’s most valuable assets. So, what do customer satisfaction, customer loyalty, and hyper-personalization have in common, you may ask. Well, everything.
How does data help with hyper-personalization?
True one-to-one personalization requires a lot of data about every aspect of customer behavior. Mature brands have been pushing the envelope with hyper-personalization thanks to the help of advanced data management and data-driven marketing. And, this is exactly where a good Customer Data Platform (CDP) steps in to take over the reins. It is the Customer Data Platform that processes large amounts of information flows, analyzes this data and converts it into actionable insights that can be utilized by marketers to tactically anticipate a customer’s needs. Personalizing your business’s communication and campaigns can mean making or breaking a brand. Today’s consumers are tech-savvy and demand experiences that are relevant to their requirements, making hyper-personalization one of the best marketing investments you can make.
Gartner predicts that over 50% of companies will redirect investments towards customer experience innovations in 2018.
What’s more, today’s customers are comfortable, to a certain extent, with their favorite brands knowing their individual information, such as product and personal preferences so they can enjoy a tailored experience crafted specifically for them. Customers also value their time, and pre-set buyer journeys allow them to get to their destination faster. This means organizations can use data to hyper-personalize experiences, albeit with customer consent where needed.
How does hyper-personalization help achieve customer satisfaction and drive revenue?
Research supports hyper-personalization, with studies stating that reaching out to a customer personally drives engagement, brand loyalty and therefore, revenue. For instance, studies by VentureBeat displayed results that a simple change such as using a customer’s name in emails pushed the email open rate up 29.3 percent on average. The same study also stated that websites using personalization drove up their page views as well as conversion rate by a large percentage.
Once you segment your customers using behavioral data, you can understand each segment and tweak its respective buyer journey. This is easily achieved by a CDP that integrates data from multiple channels, online and offline. Marketers can study these segments, and the results can then be utilized to create a number of micro-segments, which can then be used to personalize journeys further, much to the customer’s delight.
By focusing on defining your customers, their preferences, issues and challenges, you can access intricate details about the pain points in their purchasing journeys and find out what exactly it is that affects their buying decisions.
Now that you have the capability to listen as a business, you automatically have the means to respond based on the intentions of a customer. For example, if a customer has abandoned their cart after adding products to it, you can deliver a relevant and timely communication with a form of incentivization, like a discount, which can then act as a trigger to the targeted customer. The best part about all this? It’s all automated, thanks to an effectively plugged in CDP.
What is big data’s role in hyper-personalization?
A large number of businesses manage to personalize their communication and product offerings; however, there are many companies who continue to struggle to scale and seamlessly stitch data across multiple channels. This is where technology has a vital role to play, and big data is what helps marketers tie it all together.
To put it simply, hyper-personalization can only be achieved through advanced analytics as this is precisely what allows businesses to react and reach out to customers individually and in real time. It is big data that holds the key to understanding the customer, their personality, attitudes, geographic locations and other underlying factors that can offer marketers the ability to drive personalization in an omni-channel and digitally connected environment.
Big data means nothing if not used correctly. The benefits need to be translated to the consumer and consequently, to the brand. According to McKinsey, personalization can reduce acquisition costs by as much as 50%, lift revenues by 5-15%, and increase the efficiency of marketing spend by 10-30%.
What are the challenges to hyper-personalization?
Well-thought-out customer personalization requires businesses to redefine their strategies to optimize results. A business serious about taking advantage of personalization needs to understand the state of their industry and build the required technological capabilities that support it.
One of the biggest challenges marketers face when it comes to personalizing customer journeys is gleaning relevant information fast enough. CDP steps in here with the help of Artificial Intelligence and Machine Learning to tackle this challenge head on.
Another challenge often faced by businesses is the security and compliance required to manage large amounts of data entrusted by customers to the company. Today, consumers are open to trading their personal information for free or improved services which leads to a large amount of Personally Identifiable Information (PII) data that your business is directly responsible for securing. Adding to this is the IoT (internet of things) and its hyper-connected nature, which can turn out to be a real security nightmare. With a CDP, businesses can control what data is accessed by whom, leading to better governance of data, leading to increased data security.
So, there you have it; to conclude, hyper-personalization doesn’t have to feel like a daunting and overwhelming undertaking that requires big bucks to implement. Successful businesses often begin small, creating a big impact quickly in the Martech ecosystem. Taking the time to incorporate a CDP into your business process will reap your organization awards in the short and long run.
June 12, 2019
The difference between real-time and instant data is the difference between personalisation success or failure
By Joe Cripps, Celebrus
In the context of technology marketing, the term real-time has been ubiquitous for as long as I can remember. And for those who are responsible for evaluating and making decisions to procure technology solutions, real-time has become a concept to be wary of. So why the scepticism? Real-time is perhaps the most over used and certainly the most abused phrase in the history of technology. Quite simply, the number of technology vendors who have laid claim to being real-time and the vast range of their capabilities, has made the term meaningless.
Perhaps we should draw a parallel with another industry whose marketers are obsessed with speed – the car industry. This highly regulated market obliges manufacturers to publish auditable performance figures relating to the speed and economy of their products, and these figures are a significant/important evaluation consideration to would be customers. Ironically in the tech industry, where purchases involve much higher levels of investment, and in theory more sophisticated and professional evaluation and procurement processes, there is no such objective performance data available in many cases. If the car industry was like this, budget low powered shopping cars would be claiming supercar performance.
In a market where anyone can claim to have real-time, it is very hard to accurately establish whether a software vendor’s solution is actually capable of enabling your intended use case. Of course, many uses of CDP technology does not require real time capability. For example, most analytics projects do not require data instantaneously because the insights from analytics are generally not applied in-the-moment. Similarly, if your customer data is being used to personalize email or social media campaigns, the data simply needs to arrive before the campaign is launched rather than within milliseconds of the data being captured.
However, if you are aiming to activate customer data for use in-the-moment, it’s hard to deny that you will need the data instantly. For example, if you are using your data to personalize the content of a web page or mobile app, the data needs to arrive within milliseconds, in significantly less time than it takes for the page to load. Even if the data arrives in one second, this in-the-moment personalization, because the data needs to arrive within a decisioning solution which generates a next best action and personalized content must then be created and served to the CMS. All of this needs to happen in significantly less than a second, because the page content will load in less than this time.
Now, it seems reasonable to assume that a vendor claiming real-time capabilities would be able to achieve what I have described above, but not everyone’s definition of real-time is quite this exacting. In reality, the marketing departments of many vendors make bold ‘real-time’ claims for solutions that take multiple seconds, minutes or even hours to connect data. That is why we at Celebrus have found a better term to describe the speed of our data connections. Because true real time capabilities should involve zero latency. In-the-moment personalization requires data to be available instantaneously, and Celebrus is one of a very exclusive selection of solutions that can deliver instant data.
Celebrus instant data is connected within less than 500 milliseconds of the customer interaction taking place. Celebrus’ unique tagging free technology captures the interaction data, identifies and profiles the customer, detects key signals of opportunity or threat within the customer behavior and connects either these signals or the entire data stream within the customer profile to the chosen campaign end point or decisioning solution.
We think the instant data term makes our capabilities a little less ambiguous for anyone considering which CDP to implement. But ultimately it’s not the name that the CDP vendor uses to describe their capabilities that’s important. It’s about defining your use case and determining how quickly you will need data to be available in order to achieve your desired outcome. If you want to achieve genuine, real-time personalization across digital channels, you will need a CDP that can capture and connect customer data within significantly less than a second.
Find out more about Celebrus instant data capabilities. Click here to contact our expert team.
June 10, 2019
3 Tips for Achieving Customer Data Quality
By Ariella Brown, Zylotech
You may have heard the expression, quality doesn’t cost -- it pays. A more precise formulation applies to business in the form of the 1-10-100 rule of data quality. The idea is that while it could cost you $1 to corroborate the data upon entry, it costs $10 to clean it later and $100 to leave it uncorrected due to the various losses that will result from it. How to prevent that happening? Adopt a CDP solution.
Losses due to poor data quality cost the US economy $3.1 trillion annually, according to IBM’s 2016 estimate, and concern about data quality has risen since then. According to Dunn & Bradstreet’s 6th Annual B2B Marketing Data Report, it grew from 75 percent in 2016 to 89 percent this year. It also found that only half of those surveyed express confidence in their own data.
As we discussed here, the CDP is not merely a customer database. A CDP enables businesses to automate best practices for data quality, assuring complete, integrated, updated, and cleansed data. Essentially, a CDP puts into action the core best practices for maintaining data quality: cutting through silos, synthesizing data for strategic marketing, and eliminating the headaches that result from outdated or duplicated data.
1. A CDP solves the silo problem. Bhavesh Vaghela was among the experts whose customer data management tips were shared in an article on best practices. He identified his top customer data management concern as “siloed internal structures.” The problem it poses is not just a lack of convenience in one centralized repository of data but a setup that “stifle[s] collaborative learning and prevent[s] organizations from getting a better understanding of the customer journey.”
Vaghela expounded on why breaking through silos is so crucial: “With a myriad of touch points connecting customers to brands, it’s now more important than ever for these different touch points to connect and form a cohesive brand experience. But when data is not shared between departments, this cripples the ability for companies to make the most informed decisions about their customers.”
2. Getting all the data streams to run together is the first step toward what Joe Pino identifies in the same article as his primary concern: “to focus on centralization, particularly with the fast, furious, fragmented nature of cross-channel customer data.” Achieving that gives you a much more complete picture of what your customer is about. Pinto adds, “In turn, this approach allows marketers to become more strategic, gain greater understanding and have greater engagements in order to deliver relevant messaging.” Ultimately, your centralized data enables you to “start generating powerful results from it.”
3. There is such a thing as too much data that can actually cost you in terms of wasted resources and a less informed look with your customers. Accordingly, Mathew Boaman’s top tip for the article is to “avoid duplicate data.” Even if the bit of digital bandwidth is of no concern, it is a source of “frustration for employees using the data and also potential customers who are receiving inquiries as a result of the duplication.”
Boaman explained why the problem is such a common one:
Duplicity happens very frequently as companies change business names, move addresses or transfer phone numbers. Imagine that both Company A and Company B are both in the same outbound marketing campaign and are simultaneously receiving letters in the mail. This is a waste of money, and also makes the company sending the letter look bad.
To combat that problem, the CDP ascertains that the data is clean and resolves any duplicates that will impede optimal results for your marketing team.
The Zylotech platform assures data quality because it collects and merges both anonymous and known user profiles back to a single record through an automated process in real time. AutoML comes into play to probabilistically match two seemingly different records and create truly complete customer profiles.
On that basis, it gathers together unified customer data, drawn from all relevant sources to build highly intelligent and centralized segments that can be delivered downstream to all your marketing channels. As activations happen across all your connected marketing tools, new events are picked up in real-time by Zylotech, creating the most up-to date 360 view of the customer from which to derive insight through the embedded analytics engine.
A CDP solution makes it easy to avert the $100 losses and even the $10 cost of data quality cleanup. Like quality in general, the investment in data pays.
June 7, 2019
Open the Door for Customer Exploration: Uncovering CDPs’ Irrevocable Powers
By Amit Levkovich, Optimove
Turning your ideation to execution process from a long, stressful, multitouch point one into a smoother, self-sufficient, result-oriented procedure is not a fantasy. Learn what Optimove can do to help you uncover data insights.
I often encounter situations at work that bring me to this invaluable conclusion: Even the smartest marketer’s hands are frequently tied by the lack of technology and shortage of execution tools, that prevent her from achieving her goals and showcasing her skills as an experienced professional.
I want to share an interesting story: One of my closest friends currently works for a B2C enterprise as a marketing manager. We recently met up during the long Easter weekend. While catching up, she shared about the 10-step journey she had to go through to send her Easter promo campaign.
First, she needed to ask the Business Intelligence team to query the database and export the list of customers who fit her desired target audience. It took just two hours to receive the list. Unfortunately, the audience size was smaller than expected, so she needed to rethink and redefine her audience.
“If only I was able to query the DB myself,” she told me. “I could have been way more productive without having to depend on the other team.”
That’s just one aspect of being dependent on others - the data analysis team will then need to analyze the campaign, share the result in an Excel file, and who knows if it will take a day, two days, or arrive by sometime during the following week. From my experience - it’s usually the latter.
You can probably identify with her frustration. How could she turn the ideation to execution process from a long, stressful, multitouch point process that requires having to push most of her work to the last minute into a smoother, self-sufficient, result-oriented procedure, she could have full control over.
Untying the marketer’s hands
In recent months, Customer Data Platforms (CDPs) nabbed the spotlight they deserve in the martech conversation. These solutions allow marketers to collect, analyze and act upon all customer data within one interface among the “must have” tools in the current marketer’s toolbox. And one application inside the CDP I’ve benefitted greatly from is the customer insight discovery.
Marketers face immense pressure to increase their company’s revenue. Many times, we fall into the trap of focusing on acquiring new customers in order to meet these goals. Other times, marketers put an emphasis on maximizing the value of existing customers.
No matter which of these groups you fit into, much of your success will be tied to your ability to uncover insights about your targeted customers. Below, I offer two of my favorite examples (with short videos made with the help of my colleague) on how to leverage your CDP to uncover valuable insights.
1. Which promotion should you offer valuable customers at risk of churn?
As marketers, there’s a fine line we need to tiptoe around when creating promotions to stave off churn. On one hand, we want to provide customers with a juicy offer they can’t resist. On the other hand, we don’t want to cannibalize our revenue with an aggressive offer. By storing all customer data in one place, you can easily access your segment’s history and confidently answer this question:
In the example above, the average order value for this “High-Value High Risk” customer segment was $331. The average price paid per item was $116.3, and the average items per order was 3.4. The marketer who can easily find and act upon this information will probably offer either a 10% discount for orders above $380 (for a total order value just above the segment’s average), or a quantity discount for orders that contain more than four items. As dealing with high-value customers usually involves outliers, exploring the groups key characteristic and reviewing their range could also determine whether this group should be split to more granular groups. In this example, the average and median are relatively similar, meaning this group is pretty homogenous.
2. Are you acquiring valuable customers?
Sometimes, marketers aren’t interested in discovering insights to create promotions, but rather to understand if different actions or strategy changes had a positive impact. One example of positive impact that could result from a strategy shift could be whether customers acquired after the change had higher future values than before. With Facebook ad spend projected to continue growing in 2019, we imagine marketers are trying to understand if new strategies are improving their results. Leveraging a combination of historic and predictive values from your CDP could answer this question in minutes:
As seen in the example above, marketers can quickly compare the future value of customers acquired before and after a Facebook acquisition strategy change. In this case, the average went from $48.3 to $54.2, a massive uplift.
Focusing on insight discovery
Insight discovery is not a new obsession for marketers, but only CDPs allow marketers to do this on their own terms. Your discovery could focus on identifying the ideal segment to engage with, the precise manner in which you should do so, or even what you should offer. And once you’ve run your campaigns, it should focus on seeing whether your efforts impacted company revenues in the short and long term.
This can’t be mastered after quickly glancing at a few how-to articles, which is why our webinar offers in-depth, easy to grasp examples that prove the benefits of customer exploration capabilities.
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.”
June 3, 2019
How AI and Machine Learning Are Impacting B2B: 3 Great Use Cases for CDPs
By Chuck Leddy, Zylotech
An earlier Zyloblog post described the multiple benefits CDPs offer technology companies, benefits that go way beyond “just” the marketing function. This post will explore why so many B2B companies are now choosing CDPs in the noisy marketing technology/martech landscape (now with over 7000 vendors), what CDPs offer them, and how they’re implementing CDPs for three important, marketing-related purposes: Account Based Marketing (ABM), ID resolution, and GDPR compliance.
Unlocking the value of data: 3 key questions
Collecting raw data, by itself, provides almost zero value in B2B or anywhere else. Data needs a strategy and a structure to unlock its massive potential. How should you begin?
Start by defining exactly where your B2B company wants to go (i.e., map your goals), and define how you’ll navigate to get there (i.e., defining your key performance indicators/KPIs aligned with those goals). You must then answer 3 key strategic questions: (1) what data is most relevant to the business outcomes (goals and KPIs) you seek to drive (hint: it’s usually connected to ROI/return on investment) and (2) how you can leverage data to drive organizational decision-making, including around what products you make and how you engage customers?
Answering that second question will have you converting prospects and building customer engagement/lifetime value, while creating great, full-funnel customer experiences. Only by answering these two “data-needs-a-strategy” questions above can you begin asking the third, “data-needs-a-structure” question: (3) what particular technologies, tools and processes can help our B2B company reach our strategic goals? That’s where a CDP comes in.
3 great B2B use cases for CDPs
CDPs with machine learning give you complete, actionable visibility into your customers’ behavior: you can engage them across multiple channels in real-time, plugging leaks in your funnel (i.e., driving customer retention), segmenting customers to drive ROI, and leveraging predictive customer analytics to identify (and take advantage of) cross-selling and up-selling opportunities. An automated CDP helps you become truly customer-centric in how you run and market your B2B business. What follows are 3 of many great B2B use cases for CDPs:
1. Account Based Marketing. ABM is a B2B strategy that concentrates sales and marketing resources on a defined set of target accounts within a market and leverages personalized campaigns designed to resonate with each account. It’s a top trend in marketing today, and your CDP complements and enables it, helping you deeply understand your key accounts and key personas to drive more ABM revenue.
Your CDP provides actionable, timely account intelligence, customer profiling, persona matching, and ongoing data enrichment of target accounts that will drive ABM success. You can leverage a comprehensive view of each account, while your contact data updates throughout the process. Learn who is connected to who, and how, with key account data that tracks your account’s different business units and organizational hierarchies. Know how they make purchasing decisions that impact your revenues, then influence those decisions through ABM.
2. ID resolution. The first rule of sales and marketing is “know thy customer,” but that can be challenging when it comes to digital channels. A CDP can help by organizing collected data points, making them actionable throughout the funnel. A CDP’s data enrichment function will simply fill in missing data fields such as customer names, email addresses, mailing addresses, phone numbers, and more. You can better know your customers through their unique identifiers, which helps you engage them.
3. GDPR compliance. When B2B marketers gain a deeper understanding of who customers are and what they need, as CDPs enable, those customers tend to give you “permission” to market to them. This “permission” approach to marketing, pioneered by Seth Godin, is necessary in a world where data privacy has never been more important (especially to customers).
The General Data Protection Regulation/GDPR means you’ll need customers to actively opt-in, giving you permission to market to them. Marketing to customers you don’t know with offers they don’t find relevant will ultimately lead to customers refusing to stay “opted-in.” Know your customers and their needs, or risk violating a growing regulatory framework around data privacy, of which GDPR and California’s Data Privacy law are two recent examples. A CDP lets you manage and maintain customer permissions, so you retain them.
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.
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:
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.
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:
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.
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.
Secondly, the proliferation of touchpoints, channels, and offerings makes a customer journey non-linear, unclear, and unpredictable.
Thirdly, in this day and age of big data, data still is unable to capture the emotions and feelings of customers.
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:
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).
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.”
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.”
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.
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.
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.”
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.”
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.
“I had,” he said, “come to an entirely erroneous conclusion, my dear Watson. How dangerous it always is to reason from insufficient data.”
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:
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:
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.
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?
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 email@example.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:
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:
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:
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.
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:
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.
● 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 – firstname.lastname@example.org.
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:
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:
Or, maybe you’ll opt for the next preference center type:
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:
Or if you’re a fashion retailer, you could group items into:
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:
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:
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:
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:
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.
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:
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.
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:
What makes an Effective Identity Management Solution: 5 Mantras
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.
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.
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.
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:
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:
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
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:
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.
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:
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:
When Universal Action Is Possible Through Universal Data
3 Key Components Of A Powerful MarTech Stack
To get more key takeaways and knowledge around all things personalization and experimentation, watch the on-demand webinar and learn:
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:
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:
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:
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.
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:
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 email@example.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
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:
What Is Data-As-A-Service?
To get more key insights and takeaways on Data-As-A-Service, watch the on-demand webinar and learn:
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:
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:
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.
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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.
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.
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.
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.
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?
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.
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.
Service Worker Events:
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
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:
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.
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.
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:
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:
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…
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:
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:
Once you implement a customer data platform, there are five steps you can take to fully leverage its power:
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:
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.
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.”
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:
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.
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?
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:
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:
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.
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.
Web Page Usage
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.)
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:
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.
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:
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