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January 21, 2021
How to Convert One-Time Buyers into Two-Time Buyers (and Beyond)
By Elizabeth Burnam, Lexer
Two-time buyers are nine times more likely to make repeat purchases than one-time buyers.
That means that the second conversion—not the first—is the most critical milestone for deepening customer loyalty and building customer lifetime value (CLV).
But converting first-time buyers into two-time buyers (and beyond) is a time-sensitive endeavor. It requires personalized, targeted, and timely marketing messages to capture your buyers’ attention and encourage them to make the second purchase.
To maximize conversions to the second order, you need to:
1. Target the right people.
Not every customer is worth retargeting in paid campaigns—so you have to target the right people to maximize your return.
With the help of a CDP, you can use advanced audience segmentation to identify your highest-value customers. Typically, your customers with the highest likelihood of a second conversion include those who’ve purchased recently, those who’ve opted into email, and when focusing on marketing campaigns, those whose gender aligns with the product featured in the creative.
Along with identifying and targeting your highest-value customers, you should also be sure to suppress churned, low-value, or irrelevant audiences to avoid wasting ad budget. Common suppressions include suppressing frequent buyers from branded search and suppressing recent buyers from ads for the products they’ve purchased.
2. Communicate with buyers at the right time.
Timing is the most critical factor for turning one-time buyers into two-time buyers.
If you target customers too soon after their first purchase, you risk creating ad fatigue and turning them off from your brand. If you target them too late, your customers’ brand awareness and satisfaction may have faded too much for them to consider a second order.
Using a customer analytics solution like a CDP, you can understand which product categories have the highest repurchase rates and approximately how long customers take to make the second sale.
For example, you might find that fifty percent of customers whose first purchase was in the denim category make a second purchase within 30 days. Knowing this, you can target them with timed offers, such as free shipping on purchases made within 30 days, to increase conversions and reduce the time to second purchase.
3. Send the right marketing messages.
It’s no secret that personalization is key to successfully engaging customers.
Use your customer data to discover which products your buyers are most likely to purchase next. For example, if denim is the product category with the highest repurchasing rates, then what do customers usually buy after making their first denim purchase?
Often, we find that the second purchase is made in the same product category as the first, which is usually a great sign that customers love your product and are coming back for more. Post-purchase surveys, customer reviews, and other zero-party data can tell you more about why customers purchase particular products and inform your messaging for retargeting campaigns. For example, if customers say that they repurchase denim because of its amazing fabric, then you may want to emphasize this feature in your ads.
By understanding which products customers are most likely to purchase next and why, you can develop highly personalized marketing messages that maximize conversion to the second order.
4. Send those messages via the right marketing channels.
The omnichannel movement has been building steadily over the past decade, and for good reason—different customers have different preferences regarding where and how they shop. By targeting customers in the channels where they’re actually paying attention, you can increase the likelihood of engagement.
We recommend using both paid and owned channels to boost your reach and, ultimately, ROI.
By enabling self-serve insights, quick segment-building, real-time updates, and automated syncing to all of your marketing channels, a CDP provides marketers with a level of agility, efficiency, and effectiveness that they wouldn’t have access to if they approached this process manually. With the ability to set up hyper-personalized campaigns across channels, you can drive the second sale for stronger, longer-lasting, and more lucrative relationships with your customers.
This is the bite-sized version of “Loyalty Is Lucrative: A Practical Guide to Driving the Second Sale,” originally published on Lexer.io. If you liked this, you’ll probably find more value in the original, in-depth version too. Click here to read the rest.
January 14, 2021
How to Measure the Impact of a Customer Data Platform (CDP)
By Elizabeth Burnam, Lexer
According to a 2020 Harvard Business Review survey, 88 percent of customer-centric businesses say delivering a frictionless, omnichannel customer experience is important to their company’s overall performance.
The same study cites predictive analytics, a single view of the customer, and personalization capabilities as the top three technology must-haves for delivering these exceptional customer experiences.
To meet these goals, many businesses have been turning to Customer Data Platforms (CDPs) like Lexer. CDPs combine, cleanse, standardize, and enrich your data into a holistic single customer view, providing insight into who your customers are, what they need and expect from your brand, and how to communicate with them to increase their loyalty and lifetime value.
But with a plethora of vendors specializing in different industries, a wide variety of valuable use-cases, and a diverse set of strengths and capabilities offered by different vendors, choosing the right CDP for your business can be difficult.
As you carefully consider each vendor, pay close attention to these top four CDP impact areas:
Here’s what you need to know about evaluating the impact of CDPs on each of these business areas.
CDPs are more than just functional tools for insights and activation.
For many businesses, CDPs act as the catalyst for innovation and cultural evolution. A single customer view gives every team access to unprecedented customer insights, empowering even non-data scientists to test, interrogate, manipulate, and draw meaningful conclusions from data.
When every team is informed by the same set of customer data, your business can accelerate decision-making and agility, easily collaborate across the business, maintain sophisticated performance reports, and establish customer-centric product and marketing strategies that drive incredible results.
KPIs and metrics for measuring the impact of CDP-powered transformation include:
If customer-centric transformation is one of your business goals, be sure to take stock of these metrics before, during, and after your CDP implementation to understand its true impact.
Revenue growth is a critical goal for any business—but revenue-generating strategies that don’t put the customer experience first are misled and unsustainable.
CDPs enable businesses to implement revenue growth strategies that are informed by real customer behavior and directly correlated with long-term customer satisfaction. From prospecting to retention and service, every stage in the customer journey can be improved with a unified, actionable set of customer data, increasing returns and strengthening customer loyalty. Marketing, ecommerce, service, and executive teams can all use CDP-powered measurement and reporting tools to understand the direct impact of their activities on overall revenue growth.
KPIs and metrics for measuring the impact of CDP-driven revenue growth include:
As you’re evaluating different vendors, look for measurement and reporting capabilities to help you easily track these metrics as they relate to customer behavior. By giving every team member access to customizable dashboards, advanced segmentation reporting, and straightforward visualization tools, you can renew your business’s focus on revenue-based KPIs and drive better outcomes.
The ecommerce landscape has become increasingly tumultuous and uncertain over the past year, and saving costs wherever possible should be a high priority for every business.
However, these cost-saving initiatives shouldn’t have a negative impact on the customer experience. That’s where CDPs come in. By increasing operational efficiencies, eliminating the need for outsourced agencies or freelancers, reducing wasteful ad spend, and automating important but menial tasks such as updating audiences or triggering customer journeys, CDPs help businesses save a substantial amount of costs while still enhancing the overall customer experience. This combination of saved costs and precise, insight-driven targeting leads to high-impact campaigns that lead to rapid business growth.
KPIs and metrics for measuring the impact of CDP-enabled cost savings include:
By helping you track and improve these cost-saving metrics as they relate to the customer, CDPs facilitate personalized and efficiently-executed customer experiences at every touchpoint.
In today’s competitive landscape, the customer experience is paramount.
Most ecommerce businesses attract multiple customer personas with varying needs within and across each persona. In order to scale your business, you need tangible data to understand who these customers are, what they buy, why they buy, and how to speak to them. CDPs not only consolidate and standardize all of this data into enriched customer profiles, they also empower you to build high-value customer segments and activate personalized campaigns to efficiently target those segments across every channel. These proactive, relevant, and streamlined experiences delight the customer and differentiate your brand.
KPIs and metrics for measuring the impact of a CDP on the customer experience include:
Of all of the metrics on this list, direct customer feedback from NPS scores and surveys is the most valuable—but traditional NPS surveys don’t tell you the reasons behind scores. By augmenting NPS surveys with additional preferential and behavioral questions and unifying each survey response to individual customer profiles, you can gain deeper customer insight for improved personalization in the future.
Ultimately, the true value of a CDP depends upon how you use it. Maximize your return by approaching your CDP with a customer-first mindset and an eagerness to test, learn, and continuously optimize campaigns.
The full version of this article was originally published on Lexer.io. If you liked this, you’ll probably find more value in the original, in-depth version too. Click here to read the full version of “How to Measure the Impact of a Customer Data Platform (CDP).”
December 17, 2020
Customer Experience – The unlikely cure to the slowdown
By Quinn Pham, Meiro
Starting from around the middle of the last year, organizations around the world had begun bracing themselves for a recession. Their fear was fuelled by several valid reasons – China’s economic deceleration, the US-China Trade War and slower global industrial production, amongst other reasons.
A year hence and the fear has been realised, with the pandemic being the major culprit - rising and ebbing across the region, and concurrently affecting economies as well – travel, hospitality, F&B, production, and trade have significantly slowed down or come to a complete standstill. The Singapore PM in his continual statements has stressed the impact of the virus on the economy, and the steps to work through it to recovery and a sense of normalcy. Much the same sentiments have been echoed around the world today.
The recession is here, and if you haven’t already, it’s time to take action. Not just any action in a state of panic, but instead to use a smarter, more practical strategy. To improve your Customer Experience (CX) - improving performance, keeping all valuable and profitable customers and stretching every marketing dollar to thrust ahead.
The study by Watermark Consultancy showcases the performance of several companies in the US during the last downturn of 2007-2009. The results showed that CX Leaders performed better than the laggards, and even came out on top as compared to the broader market.
Why your customer controls the lifeboat
Maintaining stronger customer experience is key in a slowdown, as the push-marketing of old times has been replaced by a pull-marketing model that depends on the consumer’s fancies.
In an economic slowdown, it is natural that the general populace starts to spend less and becomes more selective about what they spend on. Which then makes acquiring new customers an extremely expensive strategy to pursue. The essential focus then becomes customer retention - investing in your customers, and continually earning their loyalty.
It’s your current customers that control your lifeboat and who will save your organisation. No need to look elsewhere.
In the long-term, take this loyalty you’ve earned and add to it the amplifying effect of Social Media. Take care of your current customers now, and they’ll work for you later to bring in new customers.
Begin investing in your customer base simply by knowing more about them. Better CX begins with data – a better insight into your customers can allow you to personalize their journey, respond faster and uniquely improve each touchpoint.
Today, there are hundreds of ways for a customer to interact with your brand – stores, websites, apps, portals, social media – which can lead to a million different points of data about a single customer. The challenge with having so much data is then selecting good quality data, and knowing what to do with it.
How? By consolidating all the valuable insights we can glean from the martech we’ve invested in, and using those insights to follow a few crucial steps:
CDP to the rescue
As you may have gathered by now, enlisting a Customer Data Platform (CDP) in your marketing efforts can greatly help in improving your CX. But it can also be incredibly useful, to stretch your marketing dollar for maximum returns.
You might ask why you should invest yet in another technology when marketing has already begun to feel like a software/IT department. Well, read on.
A CDP consolidates data from online and offline touchpoints, recognizing and stitching customer profiles to avoid repetitions and identify unique customers across multiple channels. To create one neat and tidy customer identity that helps you understand your customers behavior and preferences at a deeper level and create more relevant and meaningful interactions with them.
The real value though lies in how a CDP can make sure your marketing dollars are only spent on relevant audiences. With the ability to create highly precise customer segments based on a combination of their demographic and behavioural traits, you are able to direct your most relevant communication to them only when they are ready for it. For example, it would not make much sense to remarket a product to someone who has already purchased it. However, it would to someone who has looked at it online a few times, or even left it in their shopping basket.
From Strategy to Reality
Our client, one of the top national retail banks in Indonesia saw this benefit when connecting their consumer touchpoints across their website, 3 mobile apps and their customer-care system. Wanting to know more about this use case? Read the rest on our blog and check other posts to find out more about Meiro CDP.
November 23, 2020
AI, Big Data and kids: The potential and threat posed by biometrics
By Susan Raab, CDP Institute
Artificial intelligence is increasingly part of sales, marketing, customer service and production. It is also part of product development, including in the children’s book and toy industries and, according to a 2020 report from the Emotional AI Lab, is an area expected to grow throughout the decade. Recent news about integrating story and play using AI, includes Mattel’s announcement that it is collaborating with Bookful, the augmented reality (AR) book app, to produce AR-activated books for characters beginning with Barbie® and Thomas the Tank Engine®.
Educational publishers have led the way in digitized learning and now earn substantial revenue from sales of digital texts and from data they collect. Marketers know this data also can provide insights into what a family may purchase, but it is important to take care when pursuing this area for a number of reasons, not the least of which is because of complex regulation with regards to children’s data.
Collecting data on young users and developing technology designed to interact with a child on an emotional level is also a very tricky business and it’s important to consider implications of what various aspects of this may mean. MIT Technology Review said, children “are often at the forefront when it comes to using and being used by AI, and that can leave them in a position to get hurt.” UNICEF with similar concern has launched the Artificial Intelligence for Children policy to explore how to protect children’s rights in this area. The authors of the Emotional AI Lab report looked at how AI could have the potential to influence kids’ behavior and project that this could add “a commercial dimension” into the child-parent dynamic.
There are a good number of products on the market now that can respond to what we say and do, but now with more biometric data technology, far more can be captured and analyzed. This can include cameras to capture our expressions; embedded microphones to record the pitch and timbre of our voice; sensors to detect finger movement on a keyboard or screen, and monitors to track our pulse, blinks, and breathing. This biometric data can then be analyzed to ascertain how a person feels while interacting with a given product or experience, and the data can be algorithmically analyzed for predictive modeling to try to anticipate how that person might behave in the future. This is what the Emotional AI Lab terms, “Emotional AI,” and which they define as, “technologies that use affective computing and artificial intelligence techniques to sense, learn about and interact with human emotional life.”
This raises obvious ethical issues when applied to children because, as the Emotional AI Lab report research says, children are vulnerable, “their privacy rights are not well protected,” and “existing legal instruments” like the General Data Protection Regulation (GDPR) and the Children’s Online Privacy Protection Act (COPPA), “do not do enough to ensure their data will be kept safe.” Further the authors say, research shows, “children are, depending on their age, unable to understand persuasive intent or separate ads from content,” so, “marketing to children and parents based on a child’s negative emotions [for example, if their parent chooses not to get a new product that’s been presented to them as desirable] is deeply unethical and harmful.”
The authors believe this could easily lead to adding a new “commercial dimension into parenting,” in three ways: 1) marketing to parents when they themselves are vulnerable and may be more receptive to something that seems to satisfy their child, 2) parents may be largely unaware of the type of data being collected on their children, so would not take efforts to stop what they might otherwise find unacceptable, and 3) the possibility that some companies might make a two-pronged marketing effort in which on one hand they do outreach to a child to try to heighten the child’s emotions about wanting a product; while at the same time targeting the parent with the notion that the company is offering a product that can make their child happier.
For marketers and corporations, it is important to understand where this aspect of business could be headed to ensure ethical practice and safety for children, who cannot easily advocate for their own welfare, even if they were made to understand the importance of doing so. Further, this is an extremely sensitive area that could have significant social and legal consequences for companies especially as laws evolve, which they should do in this area.
More information is available at:
-UNICEF’s Policy Guidance on AI for Children
-The UK’s Information Commissioner’s Office issued a Code of Practice this September, which sets out 15 standards for with children online.
-The U.S. website on the Children’s Online Privacy Protection (COPPA) Rule
November 9, 2020
How to use Social Sentiment to boost your Marketing and Product development [use case]
By Dionysios Zelios, Data Talks
When was the last time that you received customer feedback?
And more importantly, do you know how to automatically collect, analyze and turn it into actionable intelligence?
Understanding how users feel about your brand helps you keep your marketing and product development efforts on track. It also allows you to respond right away to positive or negative input.
Thus, it is important to continuously track your brand’s social media channels and product reviews websites (i.e. g2 crowd, capterra, google reviews) and look out for any red flags. That’s where social listening comes in.
What is Social Listening
Social listening is the process of tracking mentions of certain words, phrases, or even complex queries across the web, followed by an analysis of the data.
Hence it helps you to understand why and where these conversations are happening, as well as what people think about your company; not just when they are tagging or mentioning your brand. This feedback can be beneficial for your future marketing campaigns since it can give you input on how to improve your content strategy and messaging, construct an effective loyalty program or even build more impactful brand partnerships.
Despite its name, social listening isn’t just about social media: many listening tools also monitor news websites, blogs, forums, and the rest of the web. Thus, you might ‘listen’ to what product features and functionality your users (or your competitors’ users) long for and pass this feedback to your product development team.
There are several tools available online to help you analyze the results of your social listening.
But is this enough?
Wouldn’t it be great to understand how people feel about your brand? This is where the social sentiment kicks in.
What is Social Sentiment
Social sentiment, also known as brand sentiment, is a way of measuring the emotions behind any online mention. Social sentiment can be:
Why is Social Sentiment important?
Social sentiment doesn’t just look at how many people are talking about your business. It adds context by measuring the tone of those conversations, comments, and mentions. It helps you understand what someone behind an online post is feeling. Knowing the emotion behind a post can provide important context for how you proceed and respond.
Without sentiment, data can be misleading. If your company receives a large number of mentions on social media while launching a new feature, you might assume the launch has been successful. However, if the majority of posts are negative, then the opposite is true.
The presence of negative sentiment doesn’t, however, mean that a product or brand is a failure. The ratio of positive to negative is also important. Not every consumer will love your business. Even those who are loyal customers will sometimes discover products they don’t like or have a negative experience. Thus, as long as the majority of customers have neutral to positive feelings toward your company, your overall social sentiment will be positive.
How to measure Social Sentiment?
Measuring social sentiment can be challenging, depending on the size of your company and the number of online mentions of your brand. However, just because something is difficult to measure doesn’t mean you shouldn’t try to measure and track it. So where do you start?
That’s easy! One approach would be to read through each and every post while assigning a numerical score based on the tone of each post. But this is a tedious and time-consuming process.
A better approach is to perform a Social Sentiment analysis using Natural Language Processing (NLP), given that you have consolidated all of your online mentions into one place (does the CDP word ring a bell? :) ).
Intrigued? Let’s dive into it.
All data that you gathered via your social listening or social sentiment activities is most likely unstructured data; it could be anything from customer emails, support tickets and online product reviews to social media posts. So, how do you analyze this?
Natural language processing is a field in machine learning that can help computers accurately identify specific items of interest inside vast texts and can learn the sentiment hidden inside language (identifying negative reviews, or positive customer interactions with customer service agents), at almost limitless scale. The best part? You don’t have to build, train or deploy the model by yourself since there are services who can do the heavy lifting for you.
5 Real-World Sentiment Analysis Use Cases
#1 Determine which customer segments have strong opinions
Sentiment analysis can help you identify your brand ambassadors (you don’t want to neglect those!) but also those that feel negatively about your brand for whatever reason, providing you an opportunity to start a dialogue with them. Maybe they don’t like your return process or they think that your customer service is difficult to reach. By addressing their concerns, you can convert them to loyal customers.
#2 Input for Product Improvements
Sentiment analysis makes it possible to understand what people loved or didn’t love about your latest product release. It basically ‘massages’ your unstructured data and converts it into insights that you can use as input/feedback for your Product team. In addition you can track if there are any complaints over time about “ease of use” that you should be worried about or just attribute it to a new product learning curve.
#3 Prioritize Customer Service Issues
Customer support tickets tend to be answered in order of submission, but is that the best approach? Companies can use sentiment analysis to put top priority customer service tickets at the head of the queue. Done strategically, this can help companies quickly address negative feedback and avoid escalations.
#4 Correlate customer feelings with their online touchpoints
By pairing demographic and other quantitative data, it is possible to segment the customer base and look at their sentiment in isolation. For example, do customers who spend less feel more negatively (and therefore it is a barrier to them spending more) or do unhappy customers return orders more frequently and what is the cost associated with it?
#5 SWOT analysis of your competition
There is one thing that you and your competitors have in common – a target audience. You can track and research how society evaluates competitors just as you analyze their attitude towards your business.
What do customers value most about other industry players? Is there anything competitors lack or do wrong? Which channels do clients use to engage with other companies?
You can then use this knowledge to improve your communication and marketing strategies (or overall offering) and provide services & products your customers would appreciate.
Use case: SWOT analysis of your competition
Have you ever thought about doing a SWOT analysis on your competition?
We sure did!
By thoroughly evaluating your competitors, you can learn to deal with their strengths, capitalize on their weaknesses, take advantage of any opportunities they present, and handle any threats they pose.
We were so convinced that this would give us great insights, that we actually spent some time on creating a proof of concept (PoC) for Data Talks. The whole project duration was less than a working day which is amazing if you consider that you build the process for analyzing this feedback for future cases as well.
Now we want to share that process with you (you’re welcome!)
Below are the 3 steps that we took and some screenshots included as well for further clarification.
#1. Get online customer reviews of your biggest competitors
We’ve used a Python script to crawl customer reviews of our competitors via G2 crowd, Capterra and TrustRadius. We couldn’t find customer feedback on Google reviews or any other similar software but these data points were enough for our proof of concept.
Once the data was fetched, we used the Data Talks Integrator to insert the data in a Data Lake and then the cleaning/transformation process started. Keep in mind that people don’t submit feedback the same way in all peer-to-peer review websites.
#2. Use Amazon Comprehend to find insights and relationships in text
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. No machine learning experience required.
Comprehend consists of 5 components
Here is how it works:
#3 Data Visualization in a business intelligence software
We wanted to analyze the results thus we created an interactive dashboard, where we applied various filters and played around. It was important to check the date of the comments so that we get relevant feedback but also understand the overall sentiment.
The end result looked like the graph below.
As a next step, if this wasn’t just a PoC, we could connect our project management software via an API and pass the feedback (specific comments to the product team) while sending relevant feedback to customer support as well.
Bringing it all together, sentiment analysis could be a game changer for your Marketing and Product development, or Customer Success teams if you manage to harness its potential. I would urge you to start simple, from any of the use cases described above and if you need help, feel free to reach out to me :)
November 5, 2020
What happens when Email Marketing meets the power of First-Party Data
By Dominika Vamberska, Meiro
Of the many twists of recent events, surely one is the ‘return’ of the classic marketing standard - Email Marketing. Often thought to reach its demise over the last few years, Email has stubbornly stayed alive and relevant – and in 2020 has become a level playing field for new online migrants and established brands alike.
At Meiro, we can’t say we saw these events having these effects... but we’ve always known the worth of email marketing. When deftly wielded in conjunction with segmentation, Email marketing can be a powerful tool and a necessity in every marketer’s arsenal. In fact, more than 59% of marketers according to Hubspot have mentioned email as the biggest source of their ROI.
2020 and the effects on Email
As small businesses have quickly become online migrants, email marketing with its ease of use and accessibility, has been their first-stop strategy of sorts. While on the other hand, even the bigger brands have relied on email marketing to retain their customer bases by messages of support and offers to drive the audience to their online stores. As Netimperatives interprets, recent data shows that 44% more emails are being sent now, as compared to before worldwide lockdowns began.
Conversely, the surge has also led to a rise in readership as audiences cooped up at home possibly turn to their inboxes for a dose of brand therapy. Open rates reached a high of 27% in the month of April, higher than before the crisis began.
Okay, so email has never been stronger. But does this mean that marketers can adopt a ‘spray and pray’ approach - spamming every kind of promotional email they can create to everyone on their list? Hardly. Let’s take a look at how brands can optimise their email strategies.
Care via Email
Brands make email a journey of communication, using it to establish and nurture relationships with their customers. In fact, according to the Wumderman study 79% of customers said that brands must demonstrate a measure of understanding and care before they will even consider making a purchase. Which explains all those “we’re all in this together” emails that have been flooding your inbox.
Care must also enter the equation of the customer experience here, as an email is only as important as where it leads to. Tracking the customer journey can help you create a better CX through more specific landing pages that don’t make the customer feel lost or led astray.
Personalization through Segmentation
Blast marketing methods never work. Speaking to a crowd can never let you truly understand your customer. It’s important for marketers to segment their customer bases according to criteria such as location, age, gender, occupation or interests – or even micro-segments, or according to their online interactions with your website or app, and then create more specific messaging for each.
Inboxes face more spam than ever from sellers and providers of every sort, and a personalised email can help cut through the clutter to create a direct connection. Indeed, a study from Campaign Monitor has shown emails with personalised subject lines are 26% more likely to be opened, and marketers have found a 760% increase in email revenue from segmented campaigns.
Why include Exclusion Driven Emailing
Contrary to what it sounds like, Exclusion-Driven Emailing isn’t about reducing your contact base – but using first-party data to know how, when and which customers to exclude, and to find a better, more effective way of reaching them. If for example, a customer has left a bad review on your website – first-party data can inform you to leave them out from sales messaging for a week and instead target them with Facebook ads. Letting your email campaign tie in, and be a valuable component of your omnichannel strategy.
Another way to use exclusion driven practices to boost your email marketing? Stop sending emails! You’d be wastefully paying for people who don’t respond or react to your emails. Maybe direct those resources to newer segments instead?
Boost your Email Campaign
Read the rest of this email story on our blog to find out what to do now to increase the impact of your email campaigns and deliver the right message to your customers and clients.
November 2, 2020
How to choose the best Customer Data Platform - the ultimate guide
By Dionysios Zelios, Data Talks
Whether you sell B2B or B2B, the behavior of the buyer has changed dramatically.
Over 90% of all purchases start with a search on the web or a social media reference. Before purchasing, the customer has probably touched several digital channels such as web, email, mobile and social media. Often combined with traditional channels such as visits to shops or print media. This creates a need for frictionless customer journeys in all channels. In addition, all these platforms generate large amounts of data.
Use the data and create a good customer experience!
Customer experience has become increasingly important. Today, customer expectations are greater than ever and you are not only competing with price and product, but also with customer experience that has thus been given higher priority. And with a really good customer experience you will be able to increase customer satisfaction, loyalty and profitability.
Sounds challenging, right?
It does not have to be, thanks to the Customer Data Platform.
What is a Customer Data Platform?
There is a lot of talk about the importance of working data-driven. And to be able to work data-driven and get the full potential of your customer data, you need a Customer Data Platform (CDP). But what is a CDP and why do you as a marketer need to work with one?
A CDP is a system that helps you as a marketer to combine data into a single customer view. Gartner defines a CDP as:
“A marketing system that combines the company’s customer data from marketing and other channels to enable customer modeling and to optimize the timing and targeting of messages and offers.”
At the core of a CDP is precisely the ability to integrate customer data from any external and internal source or platform, including your own CRM, sales data (POS), mobile, transaction, website, email and data from a marketing automation tool. Because when you have all the data collected you can easily analyze it, create insights and act. But more on that later.
Ok, now we have briefly explained what a CDP is. But why should you as a marketer work with a CDP?
Why use a Customer Data Platform?
Your customers want a good customer experience. One way to provide a good customer experience is to provide customized, personalized and relevant offers. You can only do that when you listen to your customers, and you do this most easily by collecting the data that your customers give you. And to manage all the data you need a CDP.
When you implement a CDP, it becomes possible for you as a marketer to create a truly holistic view of each customer. You get a 360° view of your customers’ behavior across multiple channels throughout the customer journey.
When is the right time to invest in a Customer Data Platform?
If you’re still not sure, here are a few signs that your company would benefit from investing in a Customer Data Platform:
What to have in place before you choose your CDP
Below is a series of crucial steps that most of the companies omit (or avoid intentionally) since it might be time-consuming, if not done right. However, having a plan in place and well-defined requirements is the key for the success of any project, including the adoption of a Customer Data Platform.
Ready? Let’s get started!
#1 Start by measuring the effect of doing nothing.
In other words, what would happen if you didn’t advertise at all at a specific channel or if you didn’t set a data-driven prospect prioritization process? Thus, measuring the effect of doing nothing allows you to create a baseline to measure against & predict the return-on-investment of any use case that you will think of. Although this might sound trivial, most of the companies omit this step since they think that it is not necessary or it takes too much of their time.
#2 Define control groups
Once you have a full overview of where you are standing, defining control groups is the next step. A control group is a vital part of your testing process since it helps validate testing results and proving the return-on-investment of a specific use case. Put simply, a control group is a test cell of customers or prospects who receive no special treatment. In that way, you will be able to understand if you really moved the needle in the right direction or it was just a coincidence, that would have happened anyway.
#3 Define use cases
It is now time to define your use cases. A use case lets you show people what to expect and the outcomes that you will achieve. A use case is a very powerful and reusable piece of content, since people tend to digest the information and see your point much easier than reading paragraphs of text.
#4 Choose your KPIs
Last but not least, choosing your KPIs (reflecting strategic goals) and metrics (reflecting tactical goals) will ensure that you get your company’s credibility and show how your specific use case has contributed to the overall growth. Without measuring the right KPIs/metrics against specific goals, you cannot build a rapport with the decision makers in your organization since different parts of the organization care about different issues.
Following the recommendations above will help you feel more confident about your decision on adopting a CDP since you can articulate (internally & externally) why you do this, what’s the impact, the expected return-on-investment and how are you going to monitor the success.
How to evaluate CDPs using 4 core capabilities
So, how can you find if a Customer Data Platform fits your needs?
Start with CDP Institute’s definition of a CDP and ensure any solution you consider has the following four core functionalities, in addition to a marketer-friendly interface
Data Ingestion and Integration
The CDP should be able to digest any data such as event-level behavioral data (e.g. websites, apps, mobile browsers), demographic & firmographic data, transactional data, offline & modeled data (e.g. RFM models, propensity scores, next best action).
Customer Profile Management
It should be able to connect many different identifiers from multiple platforms and devices in real-time to enable people-based targeting, personalization and measurement.
Through deterministic and probabilistic matching, it should be able to create universal and persistent consumer profiles by solving the identity of customers and visitors across different states (known & unknown).
The CDP should make it easy for you to define and manage rule-based segments on the fly.
Expose customer data to other systems
As a Marketer, you want to be flexible when it comes to which external channels that can consume your valuable customer data. Hence the CDP should be able to integrate out-of-the-box with any software system through connectors and ready-made APIs, allowing access to data for deeper analytics while boosting customer engagements.
Last but not least, you need a marketer-friendly UI and UX. You should be able to create, deploy and evaluate campaigns without the help of the IT department.
The speed and lack of friction in that process is a critical component and a critical goal of the CDP.
What is NOT a customer data platform?
There seems to be a lot of confusion over what qualifies as a CDP or not, since more and more vendors try to pass off related technology as CDPs.
Why the confusion?
These software solutions that are marketed as CDPs, don’t contain radically new features. Data integration, segmentation, and personalization features are familiar to marketers and exist in other products. Many vendors have simply repackaged their existing technology to appeal to the growing CDP demand.
Hence, it is important to keep in mind that a CDP is not:
✗ a customer relationship management system (CRM)
✗ a data management platform (DMP)
✗ a digital personalization engine (DPE)
✗ a digital experience platform (DXP)
However, each of these systems has specific strengths and can be really beneficial when connected to a CDP.
Questions to ask when talking with vendors
You now know what kind of functionalities a CDP should support and how to avoid cooperating with a non 100% CDP partner. In addition, you have the use cases and the KPIs in hand. You can’t wait to choose the best technology partner.
But how do you do that? How can you evaluate which one meets your business needs?
The answer starts with asking good questions. The questions below are some of the core considerations we recommend. CDPs differ in levels of advanced features and technical complexity, so it’s a good idea to have a list of questions to ask vendors before making a purchase. Here’s a few to get you started:
Bringing it all together, a CDP is the key tool to provide an outstanding customer experience for your customers. Hopefully you now are well informed and equipped to start your journey on finding the best customer data platform for your business.
In case you want to discuss the above further, feel free to reach out to me :)
October 29, 2020
CDP & CRM: Turning Customer Data into Customer Service
By Sam Malissa, Amperity
Customer service is a key piece of a brand’s relationship with consumers— it can entice unsure buyers to make their first purchase or encourage one-time buyers to become repeat customers. And like every interaction between a customer and a brand, customer service is more effective when it’s tailored to each customer.
In the context of customer service, personalization means that the customer service rep has access to information about the customer’s history with the brand and adjusts their service accordingly. That means knowing what they’ve purchased, what problems they might have had, and what sorts of products or offers might appeal to them.
Years ago people might have been put off if a customer service rep knew details about them, but now it’s expected. A customer might reasonably think, Well, if I’m letting them have that information about me then it’s their responsibility to use it to make my experience smoother. The good news for the brands is that when they make customer experiences better, customers want to come back.
CDP + CRM = Happy Customers
Customer Data Platforms (CDP) and Customer Relationship Management software (CRM) share the goal of improving customer experience. Combining a CDP with a CRM gives customer service reps access to all the relevant information about a given customer. Attributes like value tier, lifecycle status, and number of purchases made give the customer service rep the insight they need to treat each customer like the unique individual that they are.
On the other hand, without a CDP feeding the CRM, customer service reps have to toggle between multiple systems to get information about the customer they’re helping. Not only does this make things take longer, but pulling from separate systems that aren’t necessarily talking to one another puts the customer service rep at the risk of working from outdated or contradictory information.
Regularly feeding a CRM from a single reliable source of customer data makes it so that when reps are serving customers they have fresh information that’s accurate and ready to go.
“Everything we do revolves around improving our runners’ experience, and that means leveraging customer data in every interaction. At a technical level, that means having our CDP and CRM systems work in lockstep. When a runner contacts us with a question or an issue, the additional customer data now available enables the Brooks team to serve them even faster.”
Using Customer Data for Customer Service
For best results, a CDP will populate a CRM with both historical data and predictive attributes. Historical data gives the customer service rep a complete and up-to-date picture of the relationship between the customer and the brand, including last order, info on shipping and tracking, loyalty status, satisfaction survey results, and purchasing preferences. Predictive attributes include lifetime value, churn likelihood, product recommendations, and next best actions.
With this information at their fingertips, customer service reps can adjust their approach based on who they are helping and what the situation calls for:
Levels of treatment and types of resolution can vary for high value customers. This might include offering special perks like free shipping for a next purchase, not requesting verification in the case of a quality issue, or replacing an order with no questions asked.
One Time Buyers
Reps can increase purchase frequency through tailored service like coupon codes and flexible return policies.
Customers at risk of lapsing need extra measures to keep them around. This could be in the form of a discount if an order was delayed, free shipping, or just providing an overall more personalized experience.
Understanding a customer’s predicted product preferences allows reps to make custom recommendations for cross-selling opportunities or future purchases.
Setting up the customer service operation with a foundation of organized, accurate, and accessible customer data makes it possible to personalize every service interaction. That way, reps can provide the right kind of service that keeps customers happy and leads to great outcomes for the business. Top-notch customer service that speaks to the specific situation of each customer can make all the difference in turning customers into superfans of your brand.
October 26, 2020
How entertainment businesses can find lookalikes on their best customers
By Daniel Cedergren, Data Talks
You’re in the entertainment industry, right?
And you want to find more loyal customers to your amusement park, festival, trade show, water park, fair or other entertainment business? (Online and/or offline tickets, with respect to the current corona situation).
Great, then you’re reading the right blog post!
Lookalike modeling is about using existing customer data and profiles to find similar potential customers to market to. Rather than targeting potential customers based on demographics or geographic location (in the spirit of “spray and pray”) you identify people who are likely to have the same interests and behavior as your current ideal customers.
This approach is not only known to help you save time and lower cost of acquisition, it helps you to identify high-qualified customers and expand your potential audience segments. According to one source, lookalike audiences average a higher CTR (click-through rate) than other audiences 90% of the time (source).
Find lookalikes on your best customers
Overall, companies within the entertainment industry are good at buying media and they often have extensive media budgets. The problem is that most aren’t very good at using their data to acquire new customers. Instead, they purchase media with generic data, which results in high costs of acquisition.
Also, the campaign data often tends to stay with third party media agencies. Or it gets stuck in social or ad networks without generating any value to the actual company.
In short, this is a waste of the media budget.
A better, more efficient and cost-saving marketing strategy is to find lookalikes on your best customers. This concept assumes that people who have the same sort of interests and behavior will act similarly when it comes to marketing campaigns.
Lookalike modeling is an approach used by marketers to define customers who are most likely to engage with your marketing messages. This is not a new approach, it has been used for a decade within direct marketing. What is making the big change nowadays though is the leverage in digital marketing. (Hello Facebook, Instagram, Snapchat and other social networks!) Thanks to these platforms you can be more precise in who you target.
Makes sense, so far?
The lookalike modeling: how it works
This lookalike model analyzes and considers common behaviors or traits among customers, and seeks customers who share similar characteristics. The tactics are always the same, i.e. to drive conversion by serving relevant marketing messages to a much more targeted set of audiences.
The benefits of using lookalike modeling as an approach is that you can find new target audiences that look like your best customers. It also helps you to get a ROI (return-on-investment) on your media spend, which is double or triple compared to using any standard targeting method built in ad networks.
What role can a Customer Data Platform play in Lookalike modeling?
If you use a Customer Data Platform (CDP) you have the key data about your customers right underneath your fingertips for quick action. When you want to acquire new customers, then you simply look at your best and most loyal customers.
What and when are they buying? How often do they make purchases? How much do they typically spend? How long have they been a customer? How many products or services have they purchased? What are their preferences/interests? How old are they? Where do they live?
By using the full capability of a CDP to look at the collected and unified data of your customer base, you can easily create segments and profiles based on customer variables in your data house. You can then select your best customers to tag and push into your ad network for planning and media buying purchases to be able to target the right audience.
What is a CDP?
The CDP Institute defines a Customer Data Platform as “a packaged software that creates a persistent, unified customer database that is accessible to other systems.”
It’s a system that centralizes customer data from all different sources – such as the web, email, customer support, apps, social media – and creates a 360° customer view. This data is then made available to other systems, such as a marketing automation system.
This in turn means that the data becomes actionable and can be used for marketing campaigns, customer service or to enhance the customer experience.
A CDP should be able to manage personalization, campaigns across different channels and at the same time follow the GDPR guidelines. It enables marketers to group data into profiles, thus creating a better and more personalized customer experience.
Contact us to hear about how you can use lookalike modeling and a CDP to:
October 22, 2020
How Apple did (not) kill a 330B$ industry
By Pavel Bulowski, Meiro
After the big January blowout full of clickbait-ey headlines about Google killing cookies, (yes, for most marketers that’s still an open emotional wound) which we debunked for you here, we are in for another treat, my friends! Killing of IDFA this year.
...caused the upheaval was the announcement made at Apple’s July WWDC 2020 keynote. Apple revealed that come full rollout of iOS 14 this fall, they will be making changes to IDFA tracking and availability. Up until today this ID, which is used for marketing and advertising purposes such as targeting app users on ad networks or deeper user behaviour analytics inside of the app, was available to every app owner.
How are the user behaviour insights, metadata, and identifiers like the IDFA (Apple) or AID (Google, Android) being used of course grossly varies? On one side, you have brands who collect in-app user data and use it for better segmentation and understanding of customers and on the other side, you have thousands of free apps that sell your data to anyone who asks and trust me, a lot of companies in the ad tech ecosystem do ask.
...would Apple do this? There are two aspects to this question and the latter will be highly speculative. But if you have a few seasons in the ad tech world under your belt, you would surely find it plausible.
First, the noble cause of user privacy. Apple has long been trying to position itself as a consumer privacy steward. The other option could be Apple’s fierce competition with their major frenemies, the tech giants, namely Google and Facebook who maintain a de facto duopoly on the advertising market at roughly 60% of the whole pie (US figures). They cooperate in some areas, compete in others and the digital advertising industry which is worth some 330 B$ annually is surely a strategic stake.
...will be hit by this move? The truth is that the impact will be felt by the entire industry and all its key players. Everyone will have to change their strategy in the short term and their role in the ecosystem in the long term. But if we really have to take names, here goes:
...does this affect the world of Customer Data Platforms?
A lot, and yet – only a little. While the absence of IDFAs may be a loss of data, there are still other first-party data, typically PII based identifiers that CDPs use – in identity resolution i.e. persistent cookies, advertising app IDs, emails, and phone numbers, that aren’t really going anywhere. More identifiers per customer mean more options and opportunities for data activation. So what does the solution look like? By using these 1st party identifiers to build a comprehensive client profile using intelligent profile stitching, potentially enriched by third-party identifiers.
We are not cheesy enough to say that this situation will help CDP as a category overall since we are a CDP vendor, but if you read the market like that, who are we to argue?
...next for brands?
Despite the doomsday predictions, brands can still look forward to bright days ahead for digital marketing, albeit via different, more responsible, and transparent means. Are you curious about our suggestions on how to get ready for another digital marketing era?
You can finish this article on our blog, where you can find more info about the world of data, CDP, digital marketing and Meiro itself of course.
October 19, 2020
Do I need a CDP? + 3 use cases to get started
By Dionysios Zelios, Data Talks
87% of consumers want a more consistent customer experience.
They expect one-to-one communication, requiring you to know what they did in the past, what they are currently doing and what they will most likely do in the future (no pressure!)
At the same time, companies need to account for new channels, technologies and data sources. For you as a Marketer, it is challenging to keep pace with the rapidly changing ecosystem and potential knowledge gaps. How can you cope with all this? For many companies, the answer is spelled CDP.
What is CDP?
Let’s start by defining the concept Customer Data in Customer Data Platform.
Customer data is the information your customers provide while interacting with your business via your website, mobile applications, product, surveys, social media, marketing campaigns or any other online or offline channel.
All this data acts as a backbone to a successful business strategy. Data-driven companies realize the importance of this and take action to ensure that they collect the necessary customer data points that would enable them to improve the customer experience and fine-tune their business strategy over time.
More often than not, this data is stored in silos, which means that it’s spread over different systems, teams and channels. This leads to a difficulty in interpreting the data and getting a single source of truth. Thus it becomes cumbersome to act on the data insights in real-time and provide a unified customer experience across all channels.
So what exactly is a Customer Data Platform?
The CDP Institute defines a Customer Data Platform as “a packaged software that creates a persistent, unified customer database that is accessible to other systems.”
It’s a system that centralizes customer data from all different sources – such as the web, email, customer support, apps, social media – and creates a 360° customer view. This data is then made available to other systems, such as a marketing automation system.
This in turn means that the data becomes actionable and can be used for marketing campaigns, customer service or to enhance the customer experience.
A CDP should be able to manage personalization, campaigns across different channels and at the same time follow the GDPR guidelines. It enables marketers to group data into profiles, thus creating a better and more personalized customer experience.
Do I need a Customer Data Platform?
If you’re a medium or large size company, the most likely answer is this: yes! To help you figure out if this is the case for your business, I have gathered the following list of 10 questions that you can ask yourself to find out if a CDP is a right fit for you:
If the answer is negative to at least one of the questions above, then you probably want to consider a CDP solution, which helps to solve all of the above.
Before you start typing an email to me requesting a demo, I want to give you some inspiration on what you can accomplish with a CDP. Why are use cases important? Well, a software can do as much as you have planned for; nobody wants to purchase an SUV car just to drive to the grocery store and back. Hence here is some inspiration on what you can achieve with a CDP.
3 use cases to get started with your CDP journey
#1 Optimize marketing spend
Stop advertising to existing users and target only new potential customers (cut marketing spend).
You can target look-alike audiences and avoid spending budget on acquiring users that will not convert or that have a customer lifetime value that is not big enough.
#2 Unify online and offline data
Merge data from social media, your ecommerce platform, CRM, ERP, POS into one place. Unify customer data from online and offline to deliver a holistic customer experience with personalized offers.
Through deterministic and probabilistic matching, we can create universal and persistent consumer profiles by solving the identity of customers and visitors across different states (known & unknown).
What does this mean? That we can create a unique customer profile, if we connect many different identifiers from multiple platforms and devices in real-time to enable people-based targeting, personalization and measurement.
#3 Run win-back campaigns (and avoid churn)
Choose criteria relevant for your business. For example, a customer:
Have an alert be created from your CDP solution (sms, email) to win-back this customer, either by calling him/her or via email/sms.
Remember: “It can cost five times more to attract a new customer, than it does to retain an existing one.”
That was it! Hopefully by now you can answer the question ‘do I need a CDP’ with confidence and you are equipped with a few use cases to start your CDP journey.
If you are still unsure about buying a CDP or you want to discuss your use cases further, feel free to reach out to me :)
October 15, 2020
3 steps of marketing measurement: Design, collect and measure
By Ashutosh Kumar, SAS
To measure average speed of a car halfway through a given distance it is critical to design activities that account for this measurement objective. We want to take actions and measure their impact – scientifically.
More often we are interested in measuring the impact of our actions – and doing this scientifically requires some planning. To measure average speed (of a car) halfway through a given distance, we should mark the midpoint and should have a clock. To measure conversion through a print catalogue and to compare that against the same from a digital catalogue, the design of the catalogues should allow you to identify the source of conversion and should be able to differentiate between print and digital. While these measurement activities can be done in numerous ways, what is critical is to design activities that account for those measurement objectives.
Here is a three-prong construct to help designing marketing activities in order to effectively be measured.
Design to collect
Let’s take an example of measuring impact of a catalogue (print / digital) on customer conversion (buying). In some cases, a customer may see the print catalogue first and then jump to digital channels (e.g., uses an app to buy online). Ideally, these activities are to make the conversion (buying) easier for a customer. But that would also create ambiguity in terms of which activities to be attributed for conversion (in this example, print or digital catalogue). This introduces the concept of activities sequenced to drive a customer behavior in certain direction, also known to us as “customer journey.”
The role of analytics in designing these activities is critical. The design should include what is intended to be measured. This is where the third prong of the construct (measure) drives the first (design).
We have seen vanity URLs and unique 800 numbers used to handle this challenge. But those involve other issues – for example, cost of registering different 800 numbers, possible confusion to customers with different 800 numbers/URLs, losing catchiness and simplicity, etc.
With modern digital innovations, collecting parameters at digital channels for direct marketing is no longer a challenge. Many technological supports (software/platforms) do collect measurement parameters as out-of-the-box capabilities. There are other considerations here, but let’s hold those thoughts until we reach the third prong.
Collect to measure
An effective first prong of this construct would make this step (collect) simple and purely operational. This would then be nothing but populating a table with data for which structure was created in the first step. Alas, life is never so straightforward. Often we fail to design accurately, mostly due to ever-changing priorities and market dynamics, which leads to some retrofitting-type marketing activities. Since those activities could be tactical and opportunistic, we won’t further discuss them here, but we should consider that granularity of collected information and data is critical in bringing adoptability against the ex post changes in the measurement requirements.
However, a broader question would be, can we “time to market” the process of designing and deploying (collecting)? This can be substantially achieved by squeezing the time needed to design and closing the gap between design and deploy. Ideally, we should be able to design quickly based on the business objectives and deploy immediately once designed. (https://www.sas.com/sv_se/customers/ica-banken.html)
Measure to improve
Here is the piece of the puzzle that has gone through tremendous research and has significantly evolved in recent years. AI and machine learning based algorithms are also being used for attribution analysis. Storage capacity and processing speed are no longer constraints. Traditional methods like first touch or last touch attribution are not lucrative enough to modern marketers; they are now looking for algorithmic approaches. And why shouldn’t they – customers’ expectations are changing faster than ever, and businesses are competing for their attention. Any additional insight to recalibrate the customer engagement process would add significant value. My colleague Suneel Grover has blogged about some recent advancements in attribution measurement.
Quest of sophistication in this part creates newer challenges in designing the (sophisticatedly) measurable activities. Data scientists have developed attribution algorithms that go beyond the traditional rule-based and typical out-of-the-box methods. Someone willing to use more sophisticated algorithms to measure attribution needs to incorporate elements to collect in the design of the activities.
The whole cycle of our three-prong construct also requires us to follow the principle of agility and should support time to market. I would tend to call this something like “EMAP” – effective marketing attribution principles, which, in conjunction with the three-prong construct mentioned above, would be described as below:
Test and learn is another aspect that requires significant swiftness in the design and execution of marketing measurement but may not truly be categorized as attribution analysis. It was traditionally assumed that only basic measurement methodologies could be adopted in these cases, because added sophistication would delay the results. While this was true until few years ago, advancement in technology has allowed marketers to be detailed, analytically sophisticated and quick, all at the same time. Ball is now in marketer’s court, rather than technology department’s. Those technologies must be in place to enable automated measurement (and attribution) using analytical methods. Furthermore, based on the results, actions should also be automated, which may be picking the most effective variant of content or message.
Attribution analysis in the context of multichannel customer journey
This topic can be considered as an extension of this blog. While the above is not necessarily limited to single-channel marketing activity only, customer journeys across multiple channels and activities bring a lot of complexity into measuring effective attribution.
October 13, 2020
Twilio has acquired Segment. Why? And was it a good idea?
By Josh Neckes, Simon Data
Last Friday, Twilio (TWLO) announced that it had acquired Segment in an all-stock deal worth north of $3.2B. The move has significant ramifications for the entire marketing and data technology ecosystem. Major players like Salesforce (CRM), Adobe (ADBE), and Oracle (ORCL) have the beginnings of a new, developer-focused marketing cloud competitor on their hands. Other martech vendors have new existential questions around the partnership and competitive landscape. Investors want to know if it’s a good deal. And brands still just want to message customers more effectively.
Is new-look Twilio the answer? Is it a threat? Is it even a good idea? All of the above?
Let’s look at the deal and see what’s going on.
Who is Segment? And how do they fit Twilio’s market thesis?
Founded in 2012, Segment began life as a segmentation tool (hence the name). Quickly, however, it pivoted into a new sort of tag manager that leveraged its late-mover advantage to differentiate based on its orientation to streaming data. Segment has since become the gold standard for web and mobile tracking, as seen by players like MetaRouter and RudderStack leveraging their code in an open source capacity. Over the last eight years, it has built an impressive roster of reportedly over 20,000 customers and $150m+ ARR.
Easily the largest non-marketing cloud player in the CDP space, Segment, like many other “CDPs,” has had an uneasy relationship with the category. Last year, it branded itself “Customer Data Infrastructure,” emphasizing its developer-first orientation and seeking to separate from the muddled mass of “CDPs” with varying functionality and maturity - and with very different buyers and end users. In the press release announcing the acquisition, however, Twilio’s CEO Jeff Lawson embraces the term, writing:
As the leading CDP, Segment enables developers to unify customer data from every customer touchpoint, and empowers marketing, sales and customer service leaders with the insights they need to design and build relevant, data-driven customer engagement.
The quote provides good context on both Twilio’s orientation to Segment and to the market at-large. Twilio is a developer-first shop. Their fundamental market hypothesis is that developers will drive innovation in martech and customer communications through access to superior infrastructure. They believe that by owning the “pipes” that drive all customer communications, they at once control the emerging martech ecosystem (who build platforms on their pipes) and emerging brand ecosystem (whose developers build internal tools to send through them).
Twilio’s 2018 acquisition of SendGrid affirmed this strategic orientation. Many, if not most, next-gen marketing clouds (e.g. Braze, Iterable) used SendGrid to send email - often exclusively and at tremendous volume. Many, if not most, emerging brands (e.g. Airbnb, Uber) used SendGrid to send both product and marketing emails - often exclusively and at tremendous volume. Twilio’s acquisition meant that developers from both ecosystems could now work with one progressive, high-scale delivery provider.
Culturally and structurally, Segment feels almost identical. Segment sells mostly to developers. Segment views itself very similarly to SendGrid - Peter Reinhardt, Segment’s CEO, has spoken publicly about being the data infrastructure for the next generation of marketing tech. Developers at progressive, high growth brands have used them to manage their data flows, and - in both cases - once deployed, they’re deeply entrenched.
What does the acquisition mean for Twilio?
With the acquisition, Twilio can step past simply being a one-stop shop for marketing infrastructure. Twilio’s press release conspicuously stresses concepts like “customer experience” and “empowering marketers.” Coupled with a reference to Twilio Flex, this provides important clues into their forthcoming strategic orientation.
The full formula for managing customer experience is simple: you need data solutions to help to understand customers, orchestration solutions to decide what messages they receive, pipes to send those messages. Until now, Twilio has concerned itself exclusively with pipes while driving itself to a >$45b market cap. Turns out, controlling the pipes is pretty profitable.
By acquiring Segment, Twilio is betting that significant additional opportunity lies in controlling the data flowing through those pipes. Snowflake’s IPO, along with the early spike in Twilio’s share price on Monday, certainly confirmed that public markets share this view. But Twilio’s press release doesn’t stop at controlling data and pipes. Specifically, there’s a telling reference to Twilio Flex, intended to be a developer-first orchestration solution:
Over time, the addition of Segment will allow Twilio to integrate data intelligence into Twilio Flex and every one of our offerings to provide highly personalized customer touchpoints.
The writing is on the wall here: Twilio likely intends to build a developer-first experience cloud. Flex will be their orchestration offering, and they’ll be taking on Adobe, Salesforce, and Oracle through a developer-first product suite that is designed for a more modern organization.
Will Twilio’s strategy be successful?
Twilio has shown a tremendous ability to evolve and change. Their longer-term success ultimately hinges on five important factors:
The final point here is particularly nontrivial. Selling and implementing solutions like Segment in the enterprise is incredibly challenging, and may have had something to do with their desire for a deal. Twilio doesn’t have deep experience, and the headwinds are going to be significant. A CTO of a publicly-traded company shared this thought with me over the weekend along those lines,
“So many of today’s digital enterprise platforms have legacy systems and/or technical debt that makes deployment of systems like Segment incredibly difficult. The migrations and upgrades I led at [Company] made these sorts of deployments easier, but it was a long road that many shops won’t want to go down”
“Beyond simply the technology challenges, accounting for measurement anomalies is a huge burden in the enterprise. Traffic numbers will change following installation and we had to deal with their auditors to account for the differences. When we first launched, traffic was off by .5M per month and we did an insane dive to figure out all the differences and account for it. We required a disclosure when releasing their EOQ numbers which was exquisitely painful.”
Why did Segment sell rather than IPO?
Twilio’s strategic trajectory and long-term ambitions are only one side of the story. Segment chose to do this deal, and to do it against the backdrop of Snowflake’s valuation more than doubling following a last-minute repricing and significant private market valuation increases in the period leading up to IPO. Why? Public markets want data infrastructure companies. Data is the future. Outside looking in, doesn’t Segment stack up well against these criteria?
Ostensibly yes. But now their strong alignment is Twilio’s to benefit. Why did they do the deal? Three theories:
Ultimately, it may well be a combination of the above causes - and the truth of the matter tends to leak out after the dust settles on deals like these.
At the end of the day, the success of this acquisition will be dictated almost entirely by two factors - Twilio’s share price and Segment’s ability to catalyze their long-term strategy. Jeff Lawson has strong ambition - and a matching checkbook - to take on large marketing cloud incumbents. Their developer-led, land-and-expand framework pairs perfectly with their current product ecosystem, and Segment fits well into this landscape. If Twilio can develop a more powerful marketer-led enterprise GTM motion/brand, continue to make and integrate smart acquisitions, and navigate delicate ecosystem dynamics, they should be set up well.
For the rest of the marketing technology landscape, there is both incremental uncertainty and opportunity. A CDP, your preferred definition notwithstanding, has been acquired for multiple billions. There will almost certainly be a scarcity-driven increase in CDP acquisition by relevant folks. From the perspective of other marketing tech providers, Twilio and Segment no longer seem like innocuous supporting pipes, but are now a daunting entity with a potentially nefarious agenda. The partnership landscape will undoubtedly shift. Some, like Sparkpost, will benefit, while others (e.g. pick your Twilio-powered ESP) may find it difficult to adjust.
October 12, 2020
Build vs. Buy: How to Know When You Should Buy a CDP Over Building an In-House Solution
By Dionysios Zelios, Data Talks
To build or not to build.
This is probably the big question that comes to mind when IT and Marketing join forces to evaluate new technology that would like to add in their software arsenal. Choosing how to proceed with a Customer Data Platform is not an exception.
You want to deliver a world-class customer experience, based on your data. After thorough research, you decided that a Customer Data Platform (CDP) will enable you to reach your goal (congrats!) How will you go about it?
You’ve got two choices: either you build your own solution in-house, with all the management and upkeep that this entails, or you buy a solution from a vendor. It can be a difficult decision, with a number of benefits and drawbacks to either approach.
You might think that I am biased (transparency: of course I am!): I’d rather you buy our CDP Data Talks PRO and keep you as a loyal customer until eternity. But I will take a different approach being fully objective (I promise!) so that you are much better equipped to make the right decision for your company’s growth future.
Ready? Let’s dive right in!
Imagine you enter the meeting room where Marketing & IT stakeholders discuss the way forward. You can cluster their opinions into two main groups:
Group A: We have a large development team with a wide range of IT competencies (data engineers, backend and frontend developers, security developers, data scientists, analysts and so on) with substantial budget. We can afford to wait 1-2 years until we roll out the solution. In that way, we can fully customize our solution to our needs instead of compromising with a generic software.
Group B: We have a strong development team but our core business should not be interrupted from building and maintaining a Customer Data Platform. Instead, we would like to run pilots (POCs) quickly before we go all-in, test that a CDP is the right solution for us and leverage the knowledge that a vendor has in the field to fuel our growth. We might even find a vendor who is specialized in our line of business.
Which one is right?
Well, as always, it depends.
Group A refers to enterprises that operate within software business and have already a rather big dedicated team that they can allocate in various IT projects. What they might miss is the people investment required to deliver and maintain a custom CDP which is considerably higher than the buy option. In addition, scope creeps, failure to accurately define specifications (& use cases), cost overruns can transform the project to a large-scale IT project lost in translation. However, done right, they can build a solution which fits their needs ~ 100% and reap the benefits once it is launched.
Group B refers to medium/large companies that either operate within software business but don’t want to lose sight from their core business or companies with a not-software-related core business (i.e sports, entertainment, utilities, ecommerce, banking). They are up for buying a pre-packaged CDP (SaaS) which results in having a much lower upfront cost and quick onboarding that takes away risks, while providing a go-to market solution today. What they might miss is cooperating with a vendor who does not provide a solution crafted for their specific industry.
So instead of focusing who is right, you should investigate what suits your business at this specific time. In every build, there’s something that you’re buying – be it a cloud platform, a data warehouse, a workflow engine or an SMS provider. And whenever you are buying, there’s also a build component, like industry specific integrations or some logic within your application platform.
Common pitfalls & how to think around them
Below are some of the common pitfalls that I have noticed that Marketing leaders fall for when discussing a CDP solution. Let’s look into them:
#1 Our business is special
“We have unique requirements that makes it impossible for a third-party vendor to efficiently meet”.
All businesses were not created (& shouldn’t be) equal; this does not mean that you cannot learn from how others have tackled similar challenges. You might be impressed with how a moving company thinks and apply the same logic to a sports club.
#2 We already have a CDP
“We own the data, we have integrated all systems with each other (more or less) and we also have some analytics platform. So maybe we already have a CDP in place”.
There is a modern way of connecting the data for optimal performance and there is an old school way of doing that. If you don’t leverage your data today to its full potential, it is time to re-think the process.
#3 Built it as an IT led effort
Focus heavily on solving for the technology layer, even before solving for core CDP capabilities.
We will discuss below what the core CDP capabilities are but you should keep in mind that a CDP is a tool for Marketers and they should be involved in the process as much as the IT department. This will ensure that, not only the infrastructure is robust, but also that the Marketer gets the insights that (s)he is looking for.
Requirements that your CDP solution should fulfil
Whatever your business needs are, and no matter if you choose to build or buy a Customer Data Platform, there are a set of general requirements that have to be met for it to be successful. These are the 4 core CDP capabilities that we mentioned above.
Data Ingestion and Integration
The CDP should be able to digest any data such as event-level behavioral data (e.g. websites, apps, mobile browsers), demographic & firmographic data, transactional data, offline & modeled data (e.g. RFM models, propensity scores, next best action).
Customer Profile Management
It should be able to connect many different identifiers from multiple platforms and devices in real-time to enable people-based targeting, personalization and measurement.
Through deterministic and probabilistic matching, it should be able to create universal and persistent consumer profiles by solving the identity of customers and visitors across different states (known & unknown).
The CDP should make it easy for you to define and manage rule-based segments on the fly.
Expose customer data to other systems
As a Marketer, you want to be flexible when it comes to which external channels that can consume your valuable customer data. Hence the CDP should be able to integrate out-of-the-box with any software system through connectors and ready-made APIs, allowing access to data for deeper analytics while boosting customer engagements.
Last but not least, you need a marketer-friendly UI and UX. You should be able to create, deploy and evaluate campaigns without the help of the IT department.
The speed and lack of friction in that process is a critical component and a critical goal of the CDP.
Framework to choose the right option
I attach below a helpful framework that will enable you to make the right decision - build or buy. Of course, I advise you to treat it as a rule of thumb but I am pretty sure that it will yield the right result. Let me know otherwise :)
Bringing it all together, CDPs have proven to be one of the most effective technology platforms to empower data-driven marketers in an era of complex, multi-channel, personalization-led experiences. No matter what your final decision is, build or buy a CDP, I hope that you have all the information needed to make the right decision for your business.
In case you want to discuss the above further, feel free to reach out to me :)
October 5, 2020
9 CDP Misconceptions Clarified
By Dionysios Zelios, Data Talks
Customer Data Platforms (CDPs) are here to stay; the CDP Institute estimates that the industry revenue will reach $1.3 billion in 2020, a 30% increase over 2019. Although the term was coined in 2013 to describe several types of marketing systems that shared the ability to build a unified customer database, there are still some misconceptions of what a Customer Data Platform is.
But worry not, I will clarify these below. Brace yourselves, it’s a long post but very enlightening.
Ready? Let’s dive in!
# 1 A Customer Data Platform is the same as a CRM
A Customer Data Platform (CDP) and a Customer Relationship Management (CRM) software share some similarities. However, their primary purpose and function have many differences.
A CRM stores data of customers who had some interaction with your business. It could be data about your business prospects and customers, their product needs and purchasing history. Hence a CRM is critical for Sales and customer-facing roles to manage customer data.
On the other hand, a CDP is a database which consolidates useful customer data including personal identifiers, website visits, purchase orders, email responses, social media comments, audio recordings, customer service interaction, mobile app touch-points and any other data related to the customer. The CDP pulls this data from different sources and then cleans and combines it to create a single and unified customer view. Thus CDPs are essential if you plan to execute scalable, personalized and omnichannel campaigns.
It’s not about choosing between a CDP and a CRM. Rather, Marketers should know the difference between CRM and CDP in order to take the necessary action to the respective software for each use case.
#2 Customer Data Platforms are the same as Data Warehouses (or Data Lakes)
To store data you need a data storage system. Many companies today use a Data Warehouse to store data, while more and more are starting to use a Data Lake. Many companies need both. (If you don’t know what a Data Warehouse or a Data Lake is, no worries, just keep on reading to find out).
A data warehouse and a data lake both serve the purpose of storing data, but in very different ways.
Data warehouse: A Data Warehouse is a system that pulls together data from different sources for reporting and analysis purposes. The reports are often used to make business decisions. A Data Warehouse stores processed and refined data according to the business logic. This means that you need to prepare the data by cleaning, transforming and aggregating it before using it for analytical purposes.
Data lake: A Data Lake is a system that stores data in its raw format. Basically, you can store your data as-is, without having to first structure it. Data stored in the Data Lake can be in structured, semi-structured or unstructured format. You can import this data to your Data Warehouse for adding more business value to it, or you can use that data in dashboards and visualizations directly from the Data Lake. Beware that the direct visualization of data from the Data Lake can be risky, as data is not cleaned and it might contain corrupt or duplicate records for example, that might affect the final figures quite a lot. By using a Data Lake you are building a strong data foundation for better decisions and a single source of truth.
A Data Warehouse focuses mainly on reporting, and the data modelling and format is very strict, which limits the data you can store. A Data Lake on the other hand, is more flexible and can handle more sources of data with any kind of format. It will also give you patterns about your customers, instead of pure facts, which will help you to create an engaging and relevant customer experience.
What kind of data can you store with a data lake?
If you’re looking to act on your data – a Data Lake is what you need. It’s the foundation for any data-driven company. And as you might have guessed, it is included and an essential part of a CDP!
Thus a CDP includes a Data Lake (and/or a Data Warehouse) but it does not stop there. The CDP is responsible for the orchestration of the omnichannel and personalized campaigns that you will run, as well as the analysis, predictions and reporting of your data.
A CDP can be connected to your existing Data Lake or Data Warehouse, fueling your existing business data with insights from the Marketing activities.
#3 A CDP is the same as a DMP
Let’s define the various data categories first before we clarify the difference between a Customer Data Platform (CDP) and a Data Management Platform (DMP).
What kind of data can you collect?
Zero-party data: Any data that a customer intentionally and proactively shares with a brand is called ‘Zero-party data’. It can include preference centre data, purchase intentions, personal context, and how the individual wants the brand to recognize them. This differs from first-party data since while brands own first-party data, they do not own zero-party data. Instead, consumers grant a brand the right to use their zero-party data for the purpose of a particular intent or value exchange.
First-party data: This is the best type of data because first party data is the information you yourself have collected about your audience.
Second-party data: This is the next best thing. Second-party data is someone else’s data (usually a trusted partner who’s willing and has the consent to share their customer data with you).
Third-party data: This helps to complement the current data. Third-party data is usually provided by companies, also known as data aggregators, that sell user data. You should be very careful when using this type of data. Make sure that you can trust the source before you commit to a long-term contract.
A DMP is used when you want to build marketing campaigns for audiences that are unfamiliar with you. DMPs are best for this because of their use of third-party data. They can give you access to audiences that you don’t know. You can then use that new data to build a targeted marketing campaign.
CDPs are built for processing zero, first and second party data. If you plan to create highly personalized marketing campaigns based on your own data, use a CDP. Your CDP can gather website data and send it to any number of different tools, depending on your needs.
There is a high chance that you need both platforms in your marketing arsenal. Your CDP can handle the segmentation and creation of look-alike audiences, while you might use a DMP to target these audiences in your preferred advertising platform.
#4 A CDP is a Marketer-managed and Marketer-only system
A CDP is defined as a Marketer-managed system designed to collect customer data from all sources, normalize it and build unique, unified profiles of each individual customer. That should not be interpreted as that only Marketers should manage and work with it.
Sure, Marketers should drive the implementation since they are the ones who have defined the use cases and will be responsible for proving the ROI. However, the IT should be involved as well, at least during the partner selection and the initial implementation of the solution. That will ensure that major technical hiccups will be avoided or spotted early in the process. In addition, keep in mind that the CDP will be ideally connected to the existing Data Warehouse (or Data Lake) hence IT involvement is needed for a smooth project.
Once it is implemented, your Data Scientists, Database Developers or Analysts might want access to either fetch some data or implement a machine learning algorithm, supporting your Marketing activities. Last but not least, the Finance department might want to double check how much you have spent for a specific campaign in a specific channel since the invoice from the advertising vendor seems too high this month.
#5 A CDP’s main capability is Identity Resolution
It is correct that your CDP should be able to connect many different identifiers from multiple platforms and devices in real-time to enable people-based targeting, personalization and measurement.
Through deterministic and probabilistic matching, it should be able to create universal and persistent consumer profiles by solving the identity of customers and visitors across different states (known & unknown).
However, keep in mind that the Customer Profile Management described above is just one of the 4 core CDP capabilities, explained in more detail here.
#6 CDPs are known for handling only personally identifiable first-party data
A CDP can handle zero-, first-, second- and third-party data! Sky is the limit on how you can leverage your CDP. However, most of the companies use a CDP to handle zero, first and second party data while third party data is being processed by their DMP.
#7 Implementing a CDP involves replacement of our Marketing Automation or visualization software
That is a tricky one and depends on the vendor that you will choose to partner with. It might be the case that the vendor has already a full suite of products, hence yes, you should stop using your preferred Marketing Automation (MA) software or visualization software. However, there are vendors who are MA or visualization software agnostic.
Why is being that flexible a huge benefit?
Let’s say that you implement a CDP solution today and after 6 months, for whatever reason, you decide that the Marketing automation software (or your visualization software) does not meet your business expectations. Instead of throwing all of your CDP-implementation out of the window, you can easily switch to your selected MA solution and the CDP partner will do the rest for you. Pretty cool, right?
#8 The implementation of a CDP takes years
This is a reasonable concern. You read all this information online about the CDPs, you speak with a few vendors and you understand the complexity. On average, for an enterprise, it takes 6 weeks to build one integration. Given that you have 5-6 data sources, one Marketing Automation software and a visualization tool to connect, that is about… well, no need to do the math. It seems like a never-ending project.
And that would be the reality in case you choose to build instead of buying a CDP solution.
So it is not hard to assume that the technical implementation will take years which you cannot afford; you need to show the ROI internally in a few months after signing the contract.
Well, I have some good news for you!
Basically the integrations and the various connections are pre-built normally from the vendor which means that you will save a lot of time and money. Not to mention that you will not have to maintain these integrations once it is live.
Given that you select the right partner and that your stakeholders are committed to the project, the technical implementation does not last more than one month. Thus in a couple of months you can have your first use cases up and running, demonstrating the return-on-investment.
#9 CDPs are all the same
As you might have guessed, this is not true either. The CDP Institute groups CDP vendors into four categories based on the functions provided by their systems. Each category includes functions provided by the previous categories. There are great variations among vendors within each category.
Bringing it all together, clarifying these 9 misconceptions above is one step further on uncovering a CDP’s core value and the impact it can have on the bottom line. Therefore, it is important that you understand the nuances of the Marketing technology stack and especially the CDP features and capabilities. The more accurate the understanding, the better it will serve in selecting the right Customer Data Platform for your business.
Have I missed anything? So interested to hear from you if you have any questions or input!
October 1, 2020
CDP Benefits and Tips to Optimize your CDP
By Jason Skelton, Acxiom
Getting their martech stack right is the key that unlocks the door to untold opportunity and transformational customer experiences for data-driven marketers. In an ideal martech environment, where all platforms are tied together by a unified data framework, the entire business can benefit and take action based on real-time, informed insight.
Yet for many marketers, piecing together the martech puzzle remains a challenge. There are many questions to consider:
And those are just the high-level questions.
Including a customer data platform (CDP) in their martech stack is (one of many) attractive options for marketers looking to limit data fragmentation and achieve a unified, omnichannel approach. So, what is a CDP? What benefits can CDPs unlock – and how can you tell if a CDP, or another data solution, is the best for your organization’s tech stack?
Should Marketers Look to Invest in CDPs?
Although CDPs work to centralize data across multiple sources for a unified database, because there’s no one single formula all CDPs follow (and as there are multiple diverse, ever-evolving and maturing data solutions on the market today), it can be complex to understand what each one does, and which is the best fit.
Indeed, confusion about CDPs is a common challenge Acxiom helps customers address; the CDP Institute itself recognizes more than 100 CDP solutions, and Acxiom is also tracking the landscape to make informed solution recommendations to clients based on their requirements. Whether marketers are considering a CDP investment, or looking to evolve and optimize their martech stack, they face common questions: “Is a CDP the right solution to unify my data, and do I need one in my martech stack?”
The answer here typically depends on the organization’s unique position, existing frameworks and technology. And with vendors evolving their positioning and technology, the scope of CDP capability, standard features and support can vary greatly.
What are the Benefits of a CDP?
Despite the variety of CDP solutions, support and features on the market, most CDP vendors say their solutions address a number of common challenges – to support and solve:
Considering this, investing in a CDP to achieve a centralized, omnichannel solution may seem an obvious choice – and for some organizations they are! Yet it’s important to keep in mind that as capability can vary between vendors, a CDP may still require support from other technologies in the tech stack, and may need advice and consultation from a data and technology expert to be truly optimized for real value.
For this reason, and because different marketers and organizations have differing requirements, data ecosystems and existing martech, it often helps to consult a data partner to identify:
How Can You Optimize to Ensure Value from Your CDP?
If you do have a CDP solution, or are looking for one, what can you do to optimize it to ensure it delivers best value, performance and ROI?
As a substantial enterprise solution, it’s important to make sure your CDP is the right martech solution for your business goals – so key considerations may be:
As implementing such a substantial solution across an enterprise can be a complex challenge, with many considerations to ensure optimal ROI, many organizations choose an expert data partner to advise on the best path to drive success.
With many standpoints, most organizations likely are either:
Whatever your standpoint on a CDP, Acxiom can help advise, support and enhance your current martech stack and platforms. With a services framework to help you navigate through the CDP/martech landscape, we support clients at all stages, from initial platform decisions (mapped to business requirements etc), to ongoing support services.
Originally published on here.
September 28, 2020
5 benefits of a Customer Data Platform (CDP) that all marketers should know about
By Maria Stenvinkel, Data Talks
Do you know what a Customer Data Platform (CDP) is?
Don’t worry, not many marketers actually know what it is.
Right now, there is a shift happening in many companies. Marketing is moving closer to IT and technology. Maybe a little too close for some. This opens up for new challenges and unexplored territory for many marketers.
The shift includes new technologies and a more digitalized approach to communication and marketing. It enables companies to communicate about the right product, at the right time, to the right person in the right channel – the equation for increased customer loyalty and growth.
The fact that many companies have a feeling of “lagging behind” when it comes to digital maturity may not be so strange. Especially when most of us may have a tendency to compare our own company’s digital transformations to the giants at the forefront such as Spotify, Netflix and Apple.
As much as we have to stop comparing ourselves privately in social media, we must also stop comparing our companies’ digital development to those that are digitally mature. Because the fact remains: consumers and buyers will not lower their expectations of a more personal and relevant communication and a really good customer experience.
Either we pursue a digital development, no matter where we are today, or we fall behind. It’s as simple as that.
The pursuit of customer loyalty drives the growth of CDP
There are many concepts and technologies today for marketers to understand, such as CSM, CRM, DSP, DMP - and one of the latest is CDP. A Customer Data Platform, that is. In 2013, the term was established when David Raab founded the CDP Institute - even though CDPs existed before then.
The CDP industry is growing rapidly today. Among other things, industry funding reached $2.4 billion in July 2019 - a growth of 72% compared to the previous year.
What drives the big growth then?
The quest to build customer loyalty.
It’s no longer about offering the best product at the best price. Customers today are generally not loyal and most people have no problem switching brands. Therefore, the deciding factor is not the product or the price, but the customer experience. Thus, communicating the right product, to the right person, at the right time, in the right channel has never been more important.
What is a Customer Data Platform (CDP)?
Before we go into why you as a marketer should use a CDP, we must define exactly what it is. We start with the concept of Customer Data in the Customer Data Platform.
Companies today have more data than ever before. There is everything from demographic data, transaction data, product usage data to behavioral data, profile data and attitude data.
Most often the data is stored in silos, which means that it is spread over different systems, teams and channels. This makes it difficult to make sense out of the data. What becomes even more difficult is to act on the data and create a uniform customer experience across all channels.
Data is a huge opportunity for companies, but many companies today do not know how to gather their data or extract business benefits from it. And it is in the customer data that the keys to loyalty, commitment and growth lie.
So, what is a Customer Data Platform?
The CDP Institute defines a Customer Data Platform as “a packaged software that creates a persistent, consistent customer database that is accessible to other systems.”
It is thus a system that centralizes customer data from all different sources - such as email, the web, customer center, apps, social media - and creates a 360° customer view. This data is then made available to other systems. This in turn means that the data can be used for marketing campaigns, customer service or to enhance the customer experience.
>A CDP should be able to manage personalization, campaigns across different channels and at the same time follow the GDPR guidelines. It enables marketers to group data into profiles, thus creating a better and more personalized customer experience.
Does that make sense?
So it is all about collecting, analyzing and acting on the data.
5 benefits of a CDP for marketers
A CDP is primarily built for marketers, but it helps provide insights and information for the entire company as it reflects your customers and their behavior. For you as a marketer, you have five specific benefits below with a CDP:
September 24, 2020
Shifting Customer Journeys with Customer Data Enrichment: A Marketer’s Guide
By Nick Kobayashi and Henry Li, Treasure Data
Marketing leaders are experiencing a glut of customer data—with estimates from the IDC that the global datasphere will grow from 33 zettabytes in 2018 to 175 zettabytes in 2025. (One zettabyte is a trillion gigabytes or a billion terabytes). At the same time, customer journeys have been shifting rapidly as customers’ needs, technology, and new generations have radically altered the buying experience.
To put that data to use in building and nurturing an ongoing relationship with your customer, your data needs to be highly accurate, detailed, and up-to-date. In other words, getting just your customer’s name and email address won’t cut it anymore. Today’s marketers want to know, for example, if their best customers have recently moved or had a baby. They want to know what brand of tires they buy and what time of day target buyers typically do their grocery shopping. And they need to pull together a host of online activities and behaviors, and resolve them into one accurate profile per customer—especially since so much of each customer’s experience happens online, not in physical stores.
De-duping, Resolving, and Unifying Customer Data
Data enrichment is a requirement for today’s marketing organizations, especially if you rely on customers themselves as your primary source of data. In that case, it’s easy to end up with multiple phone numbers or fake names and email addresses. Or—if you’re running a big advertising campaign—you could end up with too small an audience segment.
By gathering and unifying second-party data and third-party data with your own data, you can remove that duplicate information, fill in missing or inaccurate attributes, and update the record with the most current information. That’s the power of data enrichment. And for best results, you’ll want to go through this process on a continuous basis.
Here are some common types of data to include in your data enrichment process:
Using data enrichment, you can create a detailed composite of your customer by gathering additional data and combining it with your own proprietary data, like email addresses, phone numbers, and mailing addresses. Once all the data is stored in one place—like Arm Treasure Data Customer Data Platform (CDP)—you can analyze it to gain insights, inform your business strategy, and adapt as customers change the way they shop over time.
How do you gather the right second-party and third-party data? Many data providers specialize in certain types of information. Acxiom and Nielsen provide household demographic data. Experian and TransUnion provide credit score information. Bombora and Dun & Bradstreet provide business-related data. These data providers often collect and analyze data using a range of public and proprietary sources including census data, property records, warranty information, customer surveys, and store purchase panel data.
The real value of data enrichment, ultimately, lies in what you do with your customer data. Data enrichment helps with key business operations, such as prospect profiling, ad targeting, identifying look-alike prospects, and message personalization. Using powerful machine learning, you can build new customer segments and create advanced predictive models to analyze those segments. You can pinpoint key target groups of prospects and improve efficiencies across your campaigns.
For example, data enrichment helped Subaru identify the most valuable segments of prospective customers and tailor its marketing campaigns accordingly, boosting the “highly likely to buy” estimate from 26 percent to 73 percent.
How Data Enrichment Works
CDPs combine data sets—such as customer demographics and thousands of behavioral data points—into a single environment. Once that data is ingested and unified into a complete customer profile, it can be easily visualized or exported for modeling purposes. Marketers can also build segments with shared attributes and immediately activate them into campaigns designed to increase sales, boost engagement, and reduce churn.
For more information on how data enrichment can benefit your organization, download our new white paper, the Data-Driven Marketer’s Guide to Data Enrichment.
Lead generation is complicated enough without having to fight with your CRM and martech stack to correct your data. Data enrichment combined with a CDP can make all the difference.
September 17, 2020
New Study Finds Data Key to Unlocking Superior Customer Experience
By Tom Treanor, Treasure Data
I often see the words “customer experience” or “customer-centric” used in marketing messages as companies look to meet increasingly high consumer expectations. That’s not a surprise as today’s technologies and data-driven approaches have enabled brands to provide more personalized experiences across the customer journey—and consumers have taken notice. According to a new Arm Treasure Data and Forbes Insights study, “Proving the Value of CX,” nearly three in four consumers (74 percent) are either somewhat or very likely to buy from a company based solely on their experience, regardless of product or price. This shows the incredible importance placed on the customer experience (CX), but what does CX really mean and how can companies better deliver it to their consumers?
Defining the Customer Experience
While every company wants to be more customer-centric, the term CX can mean many different things to many people. The study found that 45 percent of CX leaders define CX as “the customer’s aggregate perception of your company based on all their interactions with your brand, product or service.” Essentially, that speaks to the customer journey from initial interest to purchase to customer service and everything in between—a critical process to building loyal and repeat buyers. In fact, 65 percent of consumers say a consistently positive experience through their entire interaction would make them a long-term customer of the brand.
Placing CX at the Forefront
However, it’s not easy building an effective CX strategy into an existing business. According to the report, one out of four CX executives say not having the right people involved is a constraint that prevents their team from implementing a streamlined CX approach. To be successful, businesses should look at CX as the whole business, not just part of it. Reorganizing around CX requires reimagining a company’s processes and taking a data-driven approach that breaks down silos across the organization. In fact, the companies that are excelling in personalization and individual customer preferences are those that can understand and draw insights from their individual-level customer data.
Harnessing Data for Better CX
The CX and customer journey are heavily reliant on data from start to finish. It starts with obtaining a holistic view of all the customers’ interactions with the brand across both digital and in-store. While this can often be challenging as touchpoints are spread out across multiple systems—such as POS, CRM, social, customer service, and more—leveraging a customer data platform (CDP) can help securely unify all of the data to provide a single source of truth. Data can then help to define which features will encourage said customers to stay loyal. Finally, data is essential to assessing the potential lifetime value of a customer. Predictive modeling that compares usage patterns of new customers with longer tenured ones can help project which are the most valuable.
Creating a cycle of data-driven improvement is what differentiates the most successful companies from their peers. To create that cycle, having the right tech stack is a minimum investment that directly enables the talent, skill sets, and customer-centric mindset. This is well worth the investment as 83 percent of executives surveyed said they faced moderate to severe revenue and market share risks due to unimproved CX. Not to mention that 56 percent of companies now look to data that captures the interactions of the most engaged customers to evaluate which customer segments to nurture. Ultimately, the urgency to implement such plans to drive better CX cannot be ignored and taking control of your data should be at the forefront of your approach.
To download a copy of the complete report, visit here.
The Forbes Insights and Arm Treasure Data report is the result of two surveys.
The CX executives survey includes the views of more than 200 global CX executives. Executives held marketing, sales, CX, product, and IT titles, and represented a variety of industries. All executives came from organizations with over $150 million USD in annual revenue, with almost half from large enterprises (revenue over $1 billion USD).
The consumer survey includes more than 1,000 consumers globally, spread across all age groups (18+ years old) and income brackets. In addition, they represent a variety of purchasing categories including automotive, appliances, consumer technology, media and entertainment, retail and e-commerce goods, and financial services.
September 14, 2020
CDPs come to the rescue of marketers to deal with CCPA
By Vishal Sukheja, FirstHive
#Ownyourdata campaign created ripples across the world. It led the movement towards providing the right to data privacy for consumers. Every nation responded to this ask from the consumers. National legislations reacted to it with national data privacy laws that protects citizen consumers within the country and in an international scenario.
Since the United States of America is led by a Federal system. California state was one among the few that passed a legislation called California Consumers Protection Act, 2018 (CCPA).
The California Consumer Privacy Act is a state statute intended to enhance privacy rights and consumer protection for residents of California, United States.
Understanding CCPA in brief helps determine the onus that is laid on the businesses by the law. Customer Data Platforms like FirstHive are designed and updated to help companies remain compliant with CCPA.
CCPA is not applicable for all businesses and does not replace any existing laws. It is relevant and applicable to businesses that buy, receive, or sell the personal information of 50,000 or more consumers, households, or devices. Businesses that derive 50 percent or more of their annual revenue from selling consumers’ personal information; or those that have gross annual revenues greater than $25 million. For-profit companies do not necessarily have to be based in California to be subject to the statute.
CCPA provisions for subject access requests. Organizations must be prepared to intake and effectuate consumer access and delete requests as they come in.
Businesses that fail to comply with these requirements or tend to release personal information to the harm of the consumer would face litigation, as well as other regulatory enforcement actions.
Data Mapping of personal information is a recommended best practice on data protection which aids subject data requests. According to CCPA, organizations should know the types of personal information that have been collected in at least the past twelve (12) months, the purposes for which it was collected, and who (including the types) of entities such data was shared with, all tracked on an ongoing basis.
To comply with this clause, CDP ensures the same and keeps the marketing team informed about how personal information is mapped across the organization.
Yes, the law also requires you to announce your association with any third-party vendors that are assigned with the duties of managing your customer data. It needs to be a transparent affair when it comes to who, when and where is the customer data made available and for what purpose.
How does FirstHive as a CDP facilitate CCPA compliance for your business?
CCPA promotes consumer data protection which empowers consumers with more rights than apparent by the law itself. This is where a CDP ensures that businesses adhere to what is expected of them in the realm of data protection.
Data updates and preferences
CCPA allows consumers to request organizations to update and change their data preferences any time. Even though the data provider is not involved with the business in a monetary transaction, the right still applies to the consumer. CDPs automate this process and update customer and lead records in real time.
First-party data for Identity Data resolution
Businesses still depend on third-party data for some basic analytics and tracking a customer. However, that is not considered authentic by CCPA. It requires a business to use only first-party data to build customer profiles that resolve identity issues that arise due to staggered data coming in from fragmented sources of interaction and information.
Right to be Forgotten
Consumers can demand any time for the right to be forgotten. CDP allows seamless implementation of opt-out and deletion requests from customers. This marketing technology is programmed to provide a single source of truth and hence can update the entire database to adhere to such requests.
Consumers interact with your business across different channels. Each consumer comes with a unique set of information and action preferences from each touchpoint. These can be asynchronously updated in the database and fields managed by a CDP.
If a customer demands disclosure, which is allowed by CCPA, a CDP provides a transparent interface of how the customer’s data is being collected, stored, used, and updated from time to time.
Rest of the world is responding to the need of consumer data protection. Each nation is releasing new laws specific to address the data needs of consumers. There are constant updates to the existing legislation. CDP brings the ability to execute marketing campaigns in adherence to the evolving data environment across the world.
For further queries, please drop in a note to email@example.com
September 10, 2020
B2B Marketers, Here’s How Customer Data Platforms Make Personalization and Selling Easier
By Amit Erande, Treasure Data
Do you market B2B products or services? Ever feel like your B2C colleagues have it easy?
As a B2B company, we know that feeling. Your customers are entire companies, not individual consumers. So instead of millions of prospects, you probably have hundreds or thousands. You also often have to deal with lengthy, intricate buying processes, involving multiple people and departments. What’s more, all of these individuals likely have different preferences, interact with different channels, prefer different kinds of information, and respond to different marketing messages.
Somehow, you have to find and reach these decision makers with content and messaging tailored to them, while mounting a coherent campaign across the entire organization. The task gets even harder when your data on these customers is incomplete, inaccurate, or scattered across multiple internal systems.
An enterprise Customer Data Platform (CDP) solves these problems for B2B marketers.
A CDP provides what marketers need to deliver personalized interactions across all marketing channels: a single, unified view of who each buyer is and how they’ve interacted with your business. And it enables you to reach prospects and customers with a consistent, targeted message every time they interact with your company, online or offline.
What a CDP offers, above all, is the ability to cut through the complexity of the corporate buying process through account-based marketing. A true enterprise CDP can associate multiple individuals with an account and help you orchestrate hyper-personalized, omnichannel campaigns. The end result: more targeted, efficient, and effective B2B marketing.
How a CDP Empowers Marketers with Data
No doubt your marketing team already has systems for collecting, storing, and working with customer or user data. These may include customer relationship management (CRM) software, web and mobile analytics solutions, email automation software, call center databases, a data management platform (DMP) for your online ads, and IoT databases for connected devices.
An enterprise CDP, such as Arm Treasure Data, doesn’t replace these other systems. Instead, a CDP pulls in data from all of them to generate a single, comprehensive view of every customer. In addition, it provides actionable insights and tools to help you manage all your interactions with the customer, at every step on a buyer’s path to purchase.
In short, an enterprise CDP isn’t just one more specialized tool for managing a chunk of your internal customer data, or one small slice of your marketing activities. Rather, it’s a marketer-controlled system that unifies all of your company’s data about customers and helps you direct every aspect of targeted, omnichannel campaigns.
Why a CDP is the Ultimate Tool for Account-Based B2B Marketing
Account-based marketing treats each company as its very own market, with content and messaging tailored to match. Through such targeting, an account-based approach can increase conversion rates and reduce the costs of landing corporate customers.
Nevertheless, an account-based approach is hardly simple to pull off. Most companies make purchasing decisions by committee, with various individuals and divisions contributing to the outcome. That inevitably makes for a longer, more complicated, more uncertain buying process. And it means you have to sway multiple decision makers within your target account, each with different interests, intents, and patterns of data consumption.
Here’s where an enterprise CDP really shines. A CDP enables you to target the individual buyers within an organization with personalized, omnichannel campaigns, while also managing your campaign at an account level.
Depending on the CDP, you may have access to powerful predictive tools that provide intelligent guidance for decision making. Arm Treasure Data’s predictive customer scoring engine, for example, applies machine learning to determine how intensely buyers are engaged with your company and how likely existing customers are to churn.
Meanwhile, an enterprise CDP’s unlimited storage of persistent data helps B2B marketers tackle a corporate buying process that can take months or even years. No matter how long a company’s purchasing cycle, your campaigns are based on a complete record of your interactions with both individuals and accounts, going back to your first contact.
How Account-Based Marketing with a CDP Looks in Action
The first step in account-based marketing is to identify the right corporate account to target. Then you can identify likely decision makers within the account, and launch campaigns to reach each of those people with targeted campaigns. Let’s see how that process works with Arm Treasure Data CDP.
Step 1: Identify the account
Let’s say an anonymous user is browsing your company’s website. Using a third-party vendor, the CDP identifies the IP address through account mapping and extracts the account information.
Step 2: Identify decision makers within the account
Working with another partner, Treasure Data then uses account mapping to determine who is part of the buying journey for your product or service and helps create demand units. Eventually, you’ve identified people from five different departments involved in buying decisions. Each of these committee members will have different viewpoints based on their personal data consumption.
Step 3: Build out profiles of each individual
Beginning with just a few pieces of information, the CDP builds out a profile of each person from your first-party data, while enriching it with data from outside sources.
The system can then extract useful information about each buyer’s intent from their behavioral and other data—such as their web browsing activity, mobile app usage, email correspondence, interactions with your digital ads, or communications with call centers.
Step 4: Create segmentation and targeted campaigns
Using the information and tools provided by the CDP, your company then targets each buyer within the account with a campaign tailored to that specific individual, not just a hypothetical persona. This may entail delivering personalized content, automating workflows and setting up event-based activation. When a user interacts with a particular piece of content, for instance, that behavior could trigger a targeted Google ad campaign.
Step 5: Use incoming data to optimize campaigns
Throughout this process, the different channels you are using send new signals back to your CDP, which it uses to generate new insights and update the customer’s profile. Marketers can then use that information to decide what steps to take next.
For example, you could evaluate whether a buyer took the action you wanted after viewing a piece of content—and if not, you could then mount a retargeting campaign aimed at that individual. The CDP can enrich each profile with third-party data, giving you a fuller picture of your buyers and helping you make better decisions over time.
So What Do B2B Marketers Need in a CDP?
For account-based marketing, it’s essential for a CDP to provide segmentation on multiple levels—by account, by individual, and by industry. That’s not a given with every solution on the market. In addition, here are some important factors to consider when shopping for an enterprise CDP.
Scalability: B2B companies have to work with relatively low volumes of data, since they have fewer customers than B2C companies. Nonetheless, scaling is vital for any type of sophisticated data analysis or processing.
Flexibility: A flexible CDP can easily connect with a wide range of outside systems and handle any type of data, from any source. Some vendors may offer CDPs that work well with their suite of products, but have difficulty integrating data from other systems without an add-on or workaround.
Extensibility: This means you can deploy useful new functionality quickly, rather than having to build components from scratch. For example, Arm Treasure Data has a library of prebuilt extensions for B2B analytics, churn prediction, next-best-action recommendations, sentiment analysis, and many other use cases.
Security: Any CDP should ensure the security and privacy of your customers’ data. For example, you may want to verify whether it complies with SOC 2 and ISO 2701 standards, encrypts all data and complies with the European Union’s General Data Protection Regulation (GDPR).
Financial stability: Once you’ve invested in a system, the last thing you need is to find yourself scrambling for a new solution because the vendor has suddenly closed up shop. That’s a risk with some CDP startups that have only limited funding.
To sum up, a CDP could give you the competitive edge you need to land that next big corporate customer. But enterprise CDPs are far from interchangeable, and some of them may be better suited to B2C than to B2B marketing—especially if you’re pursuing an account-based marketing strategy. So it pays to take your time, consider all your options, and see which one is truly right for your business.
August 31, 2020
Rebuilding Consumer Trust through Responsible Data Use
By Amit Chauhan and Meera Mehta, TruFactor
Two years after GDPR and with CCPA now in full affect – how is it still possible that there are still so many unanswered questions about how consumer data is used? Nearly 80% of Americans are still concerned about their personal data and only 6% know what is being done with the data collected. Does the future hold a proliferation of CCPA-like regulations in every state? Or is there an opportunity for business leaders to rebuild consumer trust?
This is where the notion of responsible data use comes into play. A simple example? Consider the needs of traffic planners. Historically, planners were limited to costly and time-intensive surveys and physical observation. Today, we can gather much richer and more accurate traffic patterns based mobile phone data. Analyzing home locations to understand popular routes and destinations or even population demographics enables new levels of sophistication in transportation optimization. Most importantly, these use cases only require aggregate information about the people traveling (i.e., time of day preferred by morning commuters). They do not require knowing which specific individuals are on the roadway.
Responsible use means that the there is a narrow and well-crafted goal for the using the data (i.e., road construction), the application of that data achieves that goal (routes taken by demographic segments), and only that goal, and the application does not rely on identifying any specific individual. The standards of responsible use strike the balance of delivering innovation – bringing the power of data science to improving consumer experience and discovering new market opportunities – while keeping trust and transparency paramount.
The Current Approach to Personal Information
Before we get into how we make responsible data use the norm, we need to understand what is currently considered personal information and deserving protection.
Historically, data privacy centered on personally identifiable information (PII) — data that could identify a person or be used to commit fraud (i.e., name, Social Security number, driver’s license number). In recent years, the focus has broadened to Personal Information which includes persistent identifiers such as Advertising ID, IP Address, and cookies as this pseudonymous data reflects an individual’s behavior and may be used to indirectly identify a consumer if combined with more information.
With the rise of 5G and IoT, data interactions will continue to explode and, as a result, we will see continued evolution in consumer preferences for what is considered private (and more “grey areas” for regulatory policies). It thus falls on the companies creating and using this data to enforce standards for the “responsible use of data” and address the question “should we do this?” rather than “can we do this?”
4 Questions That Define Responsible Data Use
Responsible data use is based on the answers to 4 simple questions. It starts with starts ensuring that the problem statement being addressed only requires aggregated and de-identified data.
The Virtuous Cycle of Innovation and Trust
There will still be questions on the nature of de-identification and aggregation (and extreme examples of “extrapolating” individual identities). However, without user-level identifiers, it is nearly impossible. This ongoing consternation reflects the extent that trust has eroded in technology.
Businesses do not need policymakers to define evolving privacy needs. This is the opportunity for companies to rebuild consumer faith. The standards for responsible use of data provide a framework for utilizing the power of data, without compromising fundamental rights of privacy. By pro-actively adopting responsible use standards, we can start to differentiate applications that respect consumer privacy and create a virtuous cycle where consumers gain confidence in the benefits of these applications.
August 17, 2020
Getting Your House in Order: Privacy Tips for You at Home and at Work
By Susan Raab, CDP Institute
Pull up a sofa. Now that home is where the majority of us are working, individuals and companies are becoming acutely aware that ensuring data privacy is a shared responsibility. This goes well beyond what was necessary prior to COVID-19 when people took laptops home from the office where presumably appropriate protocols were in place to guard against data misuse and outside intruders gaining access to company assets. For marketers who work with customer data, it’s crucial to handle that data with care to avoid it getting viewed, shared or accessed inappropriately.
The first step is to look at basic home information security. These are the things we all know we should do, like making sure our WIFI network is secure; not connecting with unsecured networks; using a VPN; not using personal computers if we have business-assigned computers we are supposed to use; not letting others access our business data or computers; using strong passwords and changing passwords periodically; and being very selective about emails you open and links you click on.
These articles from Data Privacy Manager and Forbes cover this in more detail, but a good rule of thumb is that if you don’t recognize something, check it first – whether it’s an article source, the sender of an email, or the validity of a link.
©2020 Data Privacy Manager - Source: UK’s National Cyber Security Centre (NCSC), Most hacked passwords revealed as UK cyber survey exposes gaps in online security
In terms of list management do and don’ts: do remove personal identifiers whenever possible, delete customer data as soon as you’re done using it, and encrypt personal data that you do store. Don’t download client lists or import or merge outside data. This is also a good time to take an audit of your data to see what you’re storing and why.
Discuss how you can recognize or check for a data breach and put procedures in place with company experts and your own team to be prepared to immediately address a breach, should one occur. Educate and involve your employees at the outset, so they understand what needs to be done to protect data, why it is critical to do so, whether there is any individual liability for that data, and how everyone can work together to comply.
These precautions and protocols are good proactive measures that are easy to implement and far better to do ahead than to deal with the consequences of a problem afterwards. It’s also important to recognize that however long it takes for the current pandemic to be tamed enough to allow a return to the workplace, this has caused a clear rethinking of the work-home paradigm that will likely extend well into the future.
August 13, 2020
What’s in store for retail with the customer data platform
By Shruti Shukla, Microsoft
According to industry analysts, 2020 will bring exciting innovations that promise to reshape the retail industry. Let’s take a look at the opportunities and explore the data challenges resulting from these trends.
Augmented reality (AR) takes the stage
Technology is catching up with expectations, with second generation augmented reality tools that make the technology more immersive than ever before for retail customers. Customers can try on outfits or makeup or even see how furniture will look in their own living room. By enabling try-before-you-buy experiences, augmented reality not only boosts sales, but also reduces returns from online shoppers. With tangible benefits to both the top and bottom line, more retailers will eye opportunities presented by augmented reality.
Digital and physical are converging
It’s no longer either or when it comes to online and offline. Savvy consumers want the best of both worlds, so in the near future, e-commerce gets physical with augmented reality while stores go digital. The death of the store has been greatly exaggerated. Instead, there will be a transformation of retail space. Customers still like the experience of in-person shopping, which combines entertainment and utility. A new breed of stores without walls will go beyond the confines of the physical space to create a blended experience where customers at the store can research products they see in person, order items that are out of stock in the store, or get location-targeted texts about items they have browsed online that are available in store.
Shopping goes social
Thanks to the expanded e-commerce capabilities of social platforms, social shopping is gaining wide-spread traction. Social shopping is the merging of social media and commerce, and it’s yet another channel for retailers, big or small, to build brand awareness, generate leads, and engage customers, as well as transact. From buy buttons to shoppable posts and stories, it’s time for retailers to get in on the action. Social networks give brands access to highly engaged audiences with high purchasing intent.
The rise of chatbots
As technology improves with natural language processing and AI, customers are becoming more comfortable interacting with chatbots for customer service and purchases. Chatbots can assist customers and give them the sense that they’re interacting with a knowledgeable retail associate. Chatbots deliver a high level of personalization with automation, allowing brands to deliver consistent, high-quality customer service at a lower cost. Answering customer questions immediately keeps customers on the site and lowers the chance that they will leave for a competitor or defer the purchase while a smooth hand-off to a live agent will strike the right balance between digital and human touch.
Customer data platforms (CDP) fuel exceptional retail experiences
These four innovations present retailers with the opportunity to take the lead in customer experiences. But they also create new challenges with even more customer touch points and data silos. For retailers who are already grappling with data overload, it’s hard to move forward. That’s where a customer data platform (CDP) comes in. It gives retailers the freedom to innovate without the complexity and cost of managing customer data and extracting value from the data. A customer data platform gives the line of business the tools to deepen its understanding of the customer and enhance the customer journey. The platform unifies an ever-increasing volume of customer data from all sources, including not only traditional sources like campaigns, transactions, and calls, but also emerging sources like social shopping, augmented reality, and chatbots to create a single view of the customer which is essential for powering personalized experiences at scale.
Take for example augmented reality and social shopping. In the morning, your customer browses your website, trying on products with the help of augmented reality. In the evening, they scroll through social media. In an ideal world, they see highly relevant shoppable social posts featuring the products that they’ve not only tried on virtually but also are most likely to buy based on previous purchases. This seamless experience is powered by a customer data platform that is able to bring together disparate data across web, augmented reality, social, and past transactions to create a single view that is at the core of personalized engagement.
Microsoft Dynamics 365 Customer Insights
Microsoft Dynamics 365 Customer Insights is a preassembled and flexible customer data platform with built-in artificial intelligence (AI) to unify customer data across all sources and generate actionable insights that power personalized experiences. Using prebuilt connectors, the solution brings together customer data from any source. The solution automatically resolves customer identities using AI and creates a persistent, unified customer profile. Customer Insights proactively identifies segments and generates predictive insights such as churn rates, lifetime value, and recommended products. Real-time integration with business applications and business processes ensure that marketing, sales, and service efforts are tailored for each customer. Brands see results faster with Customer Insights, an intuitive and ready-to-go customer data platform that requires minimal training and IT assistance.
Microsoft’s customer data platform powers personalized experiences for retail customers while maintaining the strictest compliance and security standards so that all customer data is securely managed and adheres to the General Data Protection Regulations (GDPR). Built on a hyper-scale Microsoft Azure platform, Customer Insights allows organizations to tap into powerful analytical and full customization capabilities using Microsoft AI, Azure Machine Learning, and Power Platform.
Learn more about Customer Insights and see how global brands are transforming the retail experience with a single view of the customer.
August 6, 2020
Our vision for the Microsoft customer data platform
By James Phillips, Microsoft
Empowering organizations everywhere to gain insight from all their data sources and deliver personalized customer engagement
There is a fundamental change occurring across industries and organizations: Data now comes from everywhere and everything. As customers have access to more content, purchasing channels, and brand options than before, the touchpoints become exponential — every website visit, use of a product, and interaction with a customer service representative creates an observation or generates data. But this data is often siloed across multiple systems and organizational departments, making it difficult to gain a single source of truth.
With such an overload of information and choices available, organizations must demonstrate they both understand and value their customers. To this end, we’ve been working to bring customer experiences to the forefront of the business conversation with a new set of intelligent applications with our modern, unified, intelligent, and adaptable business applications. Today, I’d like to dig into our vision and strategy for Microsoft’s customer data platform — a critically important investment from Microsoft. Specifically, how it is helping organizations overcome data silos and leverage artificial intelligence to guide decisions and empower organizations to take meaningful actions for their business.
Dynamics 365 Customer Insights: Microsoft’s customer data platform
Historically, the customer interaction with a brand ended the moment they completed the purchase and walked out the door — limiting an organization’s understanding of why or how its customers are using its products and services. Dynamics 365 Customer Insights enables organizations to gain the most comprehensive view of their customers by unifying data across diverse sources — be it transactional, behavioral, or observational data — as well as uniquely enriching profiles with market insights and real-time product usage.
From data analysts to marketing, sales, and service professionals, every employee in an organization can leverage AI-driven insights. These include churn risk, customer LTV, and recommended next best action, to power business processes across the customer journey that help boost personalization and build richer relationships.
The Microsoft CDP enables breakthrough experiences for customers while maintaining the strictest compliance and security standards so that all customer data is securely managed and adheres to GDPR regulations. Built on a hyper-scale Microsoft Azure platform, the application allows organizations to run powerful analytical capabilities using Microsoft AI and Azure-based machine learning models. Customer Insights can easily extend and customize with the Microsoft Power Platform for even richer data processing and customizations. Customers can benefit from the Microsoft partner ecosystem for development of custom applications and solutions to fit specific industry or business needs.
Let’s look at a few organizations using our CDP to deliver business outcomes and rich customer experiences:
Empowering organizations worldwide
The United Nations Children’s Fund (UNICEF) works tirelessly in over 190 countries and territories to save the lives of millions of children. As private donors and volunteers are increasingly hard to find and retain, UNICEF Netherlands found that personally engaging supporters increased their overall commitment to the organization. Key details on donors, such as their contact information, philanthropic interests, and donation history, were housed in disparate data silos, making it difficult to gain a unified view of the donor base and personalize interactions at scale. To solve this problem, the team adopted Dynamics 365 Customer Insights to quickly and easily combine data from multiple sources, analyze the data to derive insights, and activate the insights via marketing and communication channels. Customer Insights’ out-of-the-box interoperability with Dynamics 365 Marketing helps the team create and optimize marketing campaigns on-the-fly, enabling UNICEF to better develop a customer lifetime value model, which will help it identify and optimize engagement with high-impact donors.
American Electric Power Energy (AEP Energy) — a competitive retail energy solutions company serving more than 400,000 residential, small-business, and commercial customers nationwide — is using Dynamics 365 Customer Insights to deliver efficient and sustainable energy solutions to customers. Previously, AEP Energy faced cumbersome manual processes that required lots of analysis and input to piece together various customer information for a holistic view of its customer, which was both timely and costly. With a cloud-first approach, using Dynamics 365 Customer Insights, AEP Energy can now easily migrate large data sets across various systems of record into a single, unified customer profile. As a result, AEP Energy can better understand its customer needs, identify gaps in product offerings, and ensure both front and back-end operations are focused and efficient to deliver quality, tailored experiences to its customers.
Thus far, we’ve seen great momentum and impact resulting from the customer data platform and how businesses are evolving customer engagement and experiences. Looking ahead, we will continue to innovate on the platform and plan to deliver more opportunities and features in the coming months.
August 3, 2020
The Fifth P: Privacy Lands Squarely in the Marketing Mix
By Susan Raab, CDP Institute
Data privacy, once seen primarily as a legal and IT concern, is now integral to marketing, and marketers are increasingly central to ensuring compliance with privacy regulations. To do this, marketers need to understand how privacy compliance will change their marketing and how those changes relate to the company as a whole. This is a complex and rapidly evolving area that’s crucial to moving forward with a successful data strategy.
Marketers widely recognize that getting ahead of privacy regulations is good for business. In one recent report, 94% said they saw advantages in implementing stricter privacy standards before they became mandatory, citing benefits including better brand perception, higher market valuation, industry leadership, and long-term cost savings. And they’re acting on that belief: 71% expected (pre-COVID) their company increase privacy investments in 2020. Yet more than half cited complexity, cost, and time as barriers to implementation.
Clearly, marketers are looking for help. They recognize that people and process are part of the solution but also that technology is essential for moving ahead. Privacy-related systems perform four main tasks:
Each of these tasks requires different software functions. Some vendors have built products to support just one task; some have combined several tasks in the same product; and some have embedded one or more tasks within broader software such as CRM or CDP systems. The best approach for your company will depend on the size and location of your business, systems in place, and budget. But, however you assemble the components, it’s important to understand what to look for in each category. Let’s dive into those details with special attention to the vocabulary you’re likely to encounter.
Data Management. This encompasses data collection, reading, comparison, mapping and sharing. Key functions include:
Regulation Tracking. This is determining what laws apply to you, both geographically and in your business category and operations. It requires working with legal experts, either on staff or outside the company, to understand current laws and develop policies that embody them. Key functions include:
Policy Implementation. This is the cluster of functions that ensure your company publishes accurate and current privacy policies. Functions include:
Customer Interface. These functions control the direct interactions between the company and its customers. They may be the most critical for marketers because they create the trusted relationship needed for people to willingly share their data. Usability is a key issue, since users need to be offered a large and complicated set of choices in ways that are understandable and easy to execute. Components include:
July 30, 2020
Customer data platform: A key to personalized experiences
By Shruti Shukla, Microsoft
In today’s digital economy, customers are continuing to set the bar higher and higher in terms of what they expect from the brands they interact with. Power that was once held by the providers of goods and services has now shifted to the customer, whose demand for a seamless and highly relevant experience at every interaction is driving a shift in the way businesses must operate.
Customers today have access to more content, buying channels, and brand options than ever before. With an overload of information and choices available, businesses can no longer survive by simply providing the bare minimum necessary to keep customers from leaving; instead they must deliver exceptional customer experiences and outcomes. Personalized interactions allow businesses to cut through the white noise of irrelevant engagement and offers to not only drive sales, but establish deeper, lasting customer relationships. According to a recent report by Accenture, 91 percent of consumers report they are more likely to shop with brands that recognize them and provide relevant offers or recommendations – making it vital for businesses to implement a personalization strategy in order to remain competitive.
It’s all about the data
Personalized engagement is clearly no longer an option, it’s a necessity. Yet the reality is that many companies are still falling short of providing the individualized experiences that customers expect. According to the 2018 State of Global Customer Service report, more than 61 percent of customers report they’ve stopped doing business with a brand due to poor customer service. In a market where customers are free agents and where adoption and abandonment occur at the blink of an eye, companies must demonstrate that they both understand and value their customers.
This is where data comes in. Customers generate enormous volumes of data as they engage across various channels, and organizations that can successfully leverage this data for personalization will set themselves apart, while those that do not will lose business to their data-savvy competitors. On average, companies that leverage advanced analytics gain rapid insights derived from their customer data, and achieve revenue gains of 5 to 10 percent according to a customer experience report by McKinsey.
Historically, it has been a struggle to successfully capture, integrate, interpret, and act on all this data effectively, and for many companies, still is. Adding to the challenge are disparate systems across organizational silos, making it difficult or impossible to unify data in order to generate the holistic customer profiles and insights needed to positively impact the customer experience.
The customer data platform
The ability to effectively personalize at scale requires a complete, unified view of customers from both transactional and behavioral data, that when brought together enables intelligent, actionable insights – and that’s where the customer data platform (CDP) is helping to evolve the personalization landscape. With the ability to consume and unify customer data from multiple sources, CDPs provide holistic customer profiles and management, support real-time customer segmentation, and integrate customer data with other systems.
Many vendors have offerings that are primarily analytics or engagement based, but lack the rich data unification and synthesis that defines a true CDP. With increasing pressure on businesses to provide deep personalization, the need for richer data platforms is mounting and vendors are scrambling to get on the bandwagon whether they have the necessary capabilities or not.
The biggest standout capability of a true CDP is the ability to combine customer data from all sources into a single customer profile, derive valuable insights, and share those insights out to other systems and resources to enable action. The sheer speed and volume at which CDPs can ingest, process, and output the data enables businesses to react at the fast pace necessary to be effective. Without this, insights derived from customer data are often incomplete, or have already become irrelevant by the time they’re surfaced.
Benefits that only a customer data platform can deliver
CDPs offer organizations the opportunity to deliver more meaningful, individualized engagements to customers, as well as streamlining the capabilities of marketing teams to make their efforts more effective. It currently takes marketers far too long to analyze and draw conclusions about the impact of a campaign or tweaks made to the customer experience. According to a report by Forbes, 47 percent say it takes them more than a week to analyze the results, which is an eternity in today’s fast-paced buying market. Of organizations already utilizing a CDP, 53 percent seek to be able to react more quickly to market changes and customer preferences, or improve and streamline internal support operations. CDPs give marketing teams unified, on-demand access to customer data pulled together from a range of initiatives and interactions like online engagement, advertising campaigns, or purchasing histories, providing greater visibility into their customers’ needs. With this enhanced, 360-degree view of customer behaviors and preferences across all touchpoints, organizations can provide not only the highly personalized experiences that customers demand, but can cultivate deeper relationships and in-turn drive increased customer loyalty and retention.
Until now, the data leveraged for customer experience efforts was primarily drawn from straightforward customer actions like website clicks or purchase histories. Yet with enormous volumes of data now being generated from customers engaging across numerous devices and channels, as well as the addition of AI-driven interactions, organizations need more power than ever in order to manage, interpret, and act on all this data at scale – and the customer data platform provides the solution.
Dynamics 365 Customer Insights
Among the authentic customer data platforms, Dynamics 365 Customer Insights stands out with an even greater depth of capabilities. It not only enables complete data synthesis for holistic customer profiles, but also integrates directly with the rest of the Dynamics 365 engagement, AI, and analytics tools, as well as Azure-based machine learning and power apps, providing both rich customer insights and the means to act on them.
With an enhanced customer data platform like Dynamics 365 Customer Insights, companies can unify their customer data across all of their sources to gain a truly 360-degree view of their customers, empowering every employee to provide personalized, authentic engagement at every touchpoint.
Customer Insights is a self-service CDP solution, enabling faster time to initial value with zero to minimal consulting engagement. A wide range of pre-built connectors make it easy to bring data in from any source, automating standardization and merging of data records with built-in AI to create comprehensive, unified customer profiles. Microsoft’s proprietary audience intelligence also adds to data enrichment, delivering more complete customer insights. With Dynamics 365 Customer Insights, organizations are able to maintain complete control of their data as it resides in their own tenant, and can innovate freely to create custom solutions with full extensibility and compliance.
Learn more about Dynamics 365 Customer Insights.
July 23, 2020
Customer centricity: A key priority across lines of business
By Shruti Shukla, Microsoft
In today’s content and product-saturated marketplace, personalization is the key to standing apart from the competition. According to a recent study by Frost & Sullivan, by 2020 customer experience is expected to overtake price and product as the key brand differentiator, and companies globally lose over $300 billion each year due to poor customer experience. Given those numbers, failure to personalize customer engagement will negatively impact an organization’s conversion and retention rates, making it difficult or impossible to remain competitive.
Personalization across all lines of business
With the addition of a customer data platform (CDP) like Dynamics 365 Customer Insights, organizations can unify data from every channel and source, deriving insights that extend directly to other business applications to enable intelligent action across the entire organization from marketing, to sales, and customer service. This not only powers omnichannel, 1:1 content, and engagement at every touchpoint, but allows organizations to know, segment, and target customers with unprecedented accuracy, leveraging every customer interaction with the business.
Having a 360-degree view of customers helps organizations determine the best action possible for each individual customer in any context or stage of the journey, whether it be acquisition, conversion, or retention, to provide the right engagement, to the right individual, at precisely the right time. Moreover, it enables all departments to share the same view and history of a customer to improve service and customer satisfaction at any point of engagement.
The goal of marketing today is no longer to simply convert a customer in a single transaction – the bigger picture is to focus on higher-value prospects that will not only be more likely to make that initial purchase, but generate ongoing business in the future. Forward-thinking marketers can leverage CDPs to unify and enhance customer data, develop rich segments, and target customers more accurately and personally. By increasing the relevancy of content and engagements and streamlining cross-channel campaigns, they can raise the likelihood of conversion, increase the ROI of marketing efforts, and gain competitive advantage with measurable results.
Marketing use cases powered by Customer Insights
With an abundance of available options for every product and service, sellers need to understand their customers on a deeper level in order to be successful in selling to today’s highly selective consumer. It’s about providing the right offer to the right audience at the right time, and to do that requires not only complete customer data compiled from all sources of previous interactions, but the means to create detailed segments that enable highly targeted sales engagement. Organizations like Marston’s, a large pub chain based in the UK, are leveraging Customer Insights to collect and interpret customer data in order to provide personal engagement to every patron, from customized email offers and reservation preferences, to personalized drink recommendations from staff members, driving greater sales and repeat business.
Watch this video to learn how Marston’s is raising the bar for customer experiences with Customer Insights.
Sales use cases powered by Customer Insights
The future of service lies in delivering frictionless, convenient, and personalized service through any channel that a customer chooses. By democratizing data and empowering every call center or customer service representative with 360-degree customer profiles, organizations can provide proactive, omnichannel support that leaves customers feeling valued and understood, ultimately strengthening loyalty, trust, and retention. Tivoli Gardens, one of the world’s oldest and largest amusement parks, is leveraging Customer Insights to track customer behaviors and preferences to provide personal service that strengthens retention, from sending a loyalty offer for an upcoming event based on a customer’s interests, to greeting customers by name with personal activity recommendations when they visit the park.
Service use cases powered by Customer Insights
To learn more, download the eBook below or visit the Dynamics 365 Customer Insights website.
July 20, 2020
What Can the Global CX Community Can Learn from Covid?
By Paul Bergamini, Reuters Events
During the height of the crisis the UK Office of National Statistics announced that just under 50% of employed adults in the UK were working from home – This shift is something that the CX community has been championing for call centre staff for years but has now swiftly become a reality.
The time has come for customer service and experience professionals to move from talking about change to actioning it. The Covid-19 crisis has enshrined in most CEO’s minds that customer support isn’t just a cost-centre, it’s the face of the business and a crucial driver for retention and engagement. From AI and data-driven innovations through to maximising your agent’s effectiveness, the customers’ expectations are increasing rapidly, and you need to ensure you’re keeping up.
Restrictions on movement and businesses have highlighted the importance of digital channels for many areas such as retail which traditionally may have relied on a mix of physical and virtual. Whilst there has been huge turbulence on CS operations with interactions moving completely to online channels or remote agents, the transition has been remarkable. With entire industries such as retail, travel, learning and many more transitioning to online models and performing well, many CS leaders will be asking whether the bulk of their operations can remain remote.
In a recent podcast interview, Sarah Metcalfe the Head of CX for Sure PetCare commented “Look at what we have achieved, look at the we could never do that’s which have become possible. One question I would have for organisations is, your employees are working from home, if that is working for everybody do, they have to actually be in the office?”. Whilst this change has been thrust upon us, it’ll be fascinating to see whether the shift to remote working is one which implements a long lasting change.
According to a recent Zendesk report, Whatsapp has seen an incredible surge in usage with customer queries through the channel up 148% since late February. What this suggests is that customers trust in digital channels is increasing and for non-urgent queries they are more willing to submit a request and receive an answer direct to their messages. With many interactions able to be automated, companies can now focus their agents on the more complex cases where a higher quality, empathetic response is needed.
As part of their CX and Marketing series of B2B virtual conferences and content pieces, Reuters Events have announced they will be bringing together global CX and CS leaders next month. Following on from the Covid crisis, the trends we were seeing in the space beforehand have rapidly accelerated with automation and remote working quickly becoming the mainstream. With over 30 sessions over the course of both days, conversation will centre around the tectonic shifts in call centre’s way of working as well as the huge leaps made in automated technology for customer interactions.
Registration for the Reuters Events Customer Service and Experience can be accessed here: https://bit.ly/3gTpwCR
Glossary of CDP-Related Terms
Please comment to suggest changes or additions!
June 29, 2020
CDP Periodic Table
By Brent Dreyer, DataEm
Q. What are the principal elements of a CDP and how do you leverage these elements to Grow Your Business?
When people are introduced to a new technology, they typically ask, “What is this?” So, the answer they get is a definition. For example, a CDP (Customer Data Platform) is a marketing technology that can ingest customer data from any source, create unified customer profiles (Golden Records), model behavior, and share that data with any source that needs it.
But what they really mean to ask is, “What can this technology do for me?” The CDP Periodic Table answers that question by listing categorized examples (elements) of use. The elements are the titles of Use Cases that are often cited as requirements for a CDP.
The elements are categorized by general CDP objectives, and they are the foundation for asking the most important questions:
The answers are in the elements, categorized by color:
Using the CDP Periodic Table when Interviewing Vendors
Step 1. Review the various elements, look up the definitions and benefits.
Step 2. Print the table and circle the desired features in your marketing system.
Step 3. Use your selected elements as examples of requirements when talking to CDP vendors.
While it is a good start to know your basic requirements, there are other considerations that should be documented before approaching vendors. These include a summary of your marketing channels, marketing priorities (e.g. awareness, acquisition, customer value, retention, expense reductions, etc.), as well as your budget and industry focus. You will also need to complete a review of your existing marketing technology (e.g. Gap analysis) so that you are not overlapping existing technology.
All these details will widen or narrow the list of candidates when interviewing your prospective vendors. Ultimately, the requirements will make their way into an RFP (Request for Proposal).
If this sounds like a lot of work, it can be. Aside from the requirements documentation, it can be an enormous task to research prospective vendors and weed through their bloated marketing collateral. Subsequent hours of phone calls, meetings, demonstrations will cull the group of vendors down to those that have, and can, address your specific needs.
The CDP Institute has created a tool that will provide you a list of top matching vendors, as well as a draft of your requirements in an RFP. Literally, taking a 15-minute survey can save you weeks of work. [See our article, “CDP Journey- Finding a Vendor”.]
This service is currently offered for FREE by the CDP Institute and is accessible on Google Forms page at https://bit.ly/CDPInstitute. During the month of July 2020, the CDP Institute is offering 30 minutes of FREE, vendor-neutral advice from one of the Institute’s expert consultants.
The Easier Way
Call the CDP advisors at DataEM to discuss your specific goals. 954.906.2590
DataEM is a Customer Data Platform (CDP) consultancy. A CDP can ingest customer data from any source, create unified customer profiles (Golden Records), model behavior, and share that data with any source that needs it. Brent Dreyer is the Managing Partner at DataEM, and one of the expert CDP consultants assisting the CDP Institute with their vendor-neutral advisory services.
June 25, 2020
COVID-19: The Unexpected Catalyst Driving Digital Transformation in the Retail Sector
By Medha Kumar, Netcore
According to Michael Leboeuf, “A satisfied customer is the best business strategy of all”. In other words, it simply means put the customer first by understanding their needs and behavioral patterns and then map it backwards to your products.
Due to the increased adoption of smartphones, faster networks, and increasing consumer propensity to spend, the global ecommerce sales was expected to reach $6.5 trillion by 2023, with a penetration rate of 15%.
This was based on customer and market research in a pre-COVID world. But, the current COVID-19 pandemic has altered the market landscape and consumer behavior beyond recognition. The penetration rates are expected to increase to 25% by 2025.
According to the recent National Retail Federation (NRF) survey, these are some of the key consumer behavioral changes that are affecting retailers, like you:
In a pre-COVID world, traditional enterprise retailers largely focused on driving growth, acquiring higher market share, increasing footfalls to their physical stores - instead of prioritizing the building of an online presence.
But, the current COVID-19 crisis has altered consumer behavior in 2 major ways:
This is highlighted in an emarketer report that shows that:
What does this mean for offline retailers like you?
You need to be agile in adapting to these changes – accepting and embracing the new normal is crucial. A missed inflection point has cost a lot for brands in the past like Kodak and Nokia.
You don’t want to and can’t afford to miss the bus right now!
In such a scenario, you can reshape your business strategy on 2 pillars built on the foundation of customer data:
1. Digital Transformation
What does this mean in the retail industry?
It simply means two things:
Let’s consider the Future Group-owned BigBazaar in India as an example.
With the increase in demand for staples and household supplies amidst this pandemic, the Future Group launched BigBazaar.com and stepped up their efforts to service customers online, building omnichannel models within ten days that have since then scaled to about 10K orders per day.
Strengthening your online presence and continuing to deliver customer value is well within your grasp!
Your digital transformation journey can be broken down into 2 steps:
(i) Data Collection:
It is very important to inculcate a data-first mind set. It is not about one single metric that you need to pay attention to, at the same time it’s not hundreds either. And, the basis of these metrics stems from a Unified Customer View.
What is a Unified Customer view?
It is capturing diverse data-points about every single customer both online and offline, across all channels and devices.
As a retailer, why do you need it?
The ongoing digitalization has resulted in a new generation of customers who use different channels and different devices during their buying journey. Today’s consumers are ultra-connected, increasingly empowered to get in touch with brands however and whenever they want, and hyper-aware that these businesses are collecting information about their behavior and preferences every step of the way. They expect that the brands will use this information to provide seamless, personalized experiences across each interaction – regardless of whether the touch point is digital or live.
How do you tackle this?
Companies with a strong Omnichannel customer engagement are able to retain 89% of their customers, compared to 33% for companies with a weak omnichannel customer engagement.
A simple CRM or a Data Management Platform will not help you retain 89% of your customers; you need to have a Customer Data Platform (CDP) in place.
Why do you need a Customer Data Platform (CDP)?
First party data is the data that comes directly from your audience. It gives you an insight into how your customers and website visitors behave and what their preferences are. With first-party data you don’t have to guess what your customers like - you have the information directly from the source. You can capture demographic, geolocation, device-related, and behavioral data-points such as:
Using this first party data you can develop a rich individual customer profile. These unique profiles get enriched in real-time by capturing data with every customer interaction across your website, mobile app, marketing campaigns and in-store purchases.
When as a retailer you are looking into strengthening your online presence in order to deliver a seamless omnichannel experience, it is very important to import data from in-store purchases. By combining your offline sales data with your e-commerce first party data, you can get a holistic understanding of the purchase behaviors of your customers. This can give you insights into which products are most popular and what types of customers prefer which products.
All this as a retailer helps you to create a Unified Customer View.
Siloed data prevents you from creating a consistent and hyper - personalized omnichannel customer experience. With CDP this problem is solved, by gathering first-party data and by unifying customer data across all channels and devices, CDPs enable the most effective marketing decisions powered by accurate data.
This will empower retailers like you to deliver a seamless hyper - personalized experience across the customer’s journey with your brand thus ensuring that they will keep coming back to you.
(ii) Converting Data into Useful Insights:
Once you have collected relevant first party data and you have your CDP in place, making sense of data and using it to drive business strategy is the next important step you should focus on. You can now effortlessly create customer segments based on certain defined data-points.
For instance: If you are a fashion retail brand, you can build out laser-focused segments based on geolocation, past purchased products, products viewed but not clicked, products viewed and products added to cart, preferred categories, frequency of purchase, etc.
The more granular your customer segments, the more targeted and effective your multi-channel marketing strategy will be.
2. AI-led Hyper-Personalization
Merely equipping yourself with actionable insights and not identifying the right messaging, products, and offers to showcase your digital customers is a lose-lose proposition!
You need to then convert these insights into profitable actions.
You ask, why?
Let me take Colin Cherry, a cognitive psychologist’s help.
In the 1950’s he coined and explained a term called ‘The cocktail party effect’. At a cocktail party, you will talk to several people and engage in several conversations. You will tune in and out, remembering very little from most of your interactions. But then someone says something that actually interests you and is relevant to you, that conversation will receive your full attention.
Your mind will not only remember the details of the conversation, but you will also remember the person who spoke. This will compel you to come back to that person to have more such interactions.
A 360-degree view of your customers allows you to create ‘The cocktail party effect’ or what we call Hyper-Personalization for your customers.
What will this help you address?
(i) Onsite product discovery is no more a problem:
59% online shoppers believe that it is easier to find the products they like on personalized online retail stores. A few use cases to achieve the same:
(ii) Lower costs and higher customer engagement rates:
59% of marketers are experiencing good ROI after personalizing their online stores.
Our AI engine, Raman will help you analyze the most preferred channels where a customer is more likely to respond based on historical responses with your marketing campaigns.
For instance, if Customer-A is more likely to respond and convert via emails and Customer-B is more likely to respond and convert via app push notifications, spend money only on that - thus lowering your costs.
Orchestrate intelligent customer journeys to effectively engage with customer segments on preferred channels to achieve your conversion goals. Send personalized product recommendations and personalized content on the customer’s preferred channel. This means you can offer customers personalized customer engagement messages offline when the user is not active on your web or app, thus ensuring quality customer engagement rates at a minimal cost.
(iii) Higher retention rates leading to increased CLTV:
56% of online shoppers are more likely to return to a site that offers product recommendations. Go beyond clickstream data. Capture crucial eyeball data-seen and not clicked, other behavioral data like products added to cart, wish list, frequency of purchase offline purchase, etc. to offer predictive personalized product recommendations via product recommendation widget.
Create personalized virtual boutique for every unique user-consisting of product recommendations across categories based on data and Raman predictions. Doing this will help you achieve 120-150% higher CTRs.
The Ball is in Your Court!
We all knew that the world was turning digital, but due to COVID-19 the pace has suddenly increased exponentially.
Fortifying your business strategy in response to the new normal, built on the pillars of Digital Transformation and AI-led Personalization, is critical to survival and future success.
To learn how you can get started on your personalization journey and increase conversions by 8-13%, get in touch today!
June 22, 2020
Identity is A Constant During Continuing CDP Evolution: How First- and Third-Party Data Can Be a CDP’s Key Differentiator
By Michelle Tilton and Jason Ford, Infutor
“What should I be looking for in a CDP?” This is the first question from nearly every brand that’s in the market for a Customer Data Platform. As a leading data provider, we have conversations on the exact subject with brands and publishers every single day.
CDPs are an evolving technology and they can still seem nebulous to the brands that are considering them for their martech stack. But there is a universal way to make a CDP and the brands that leverage it more competitive: provide the best foundation of deterministically linked, updated, complete and enhanced customer identity data possible.
Brands need to know exactly who their customers are -- especially in rapidly shifting online and offline environments. And their marketers demand a blend of authoritative deterministic data as well as probabilistic data resources in order to be confident in their decision making. To keep pace with the evolution, a CDP has to help unlock the power of a brand’s first-party data and deliver the third-party data that fills in the gaps from the moment of ingestion all the way through deployment.
As Winterberry Group has said in its research, among CDPs’ biggest challenges are “wrangling customer data into a persistent, universal profile and making the data available for analysis and action.” Up-to-date first- and third-party data are critical to providing relevant engagements in all channels and improving the customer experience -- especially with the imminent demise of third-party cookies. Linked consumer data with life stage and preference insights is the glue that creates the “stickiness” that will make a CDP an essential part of any brand’s martech stack.
The most competitive CDPs drive identity resolution. They provide authoritative linking, and utilize a combination of client data, third-party deterministic consumer data, and powerful linking technologies. But they don’t do it alone. Here are the top identity-driven factors to look for in a CDP.
Powerful Data from the Start
Unifying customer data across platforms and marketing channels is the crucial first step for any CDP. It ensures accurate linking at the moment that data is ingested into the platform. But ingestion must also include rigorous processes to verify, clean, hydrate, normalize, complete, and score the data, ensuring that a brand’s first-party identity information is complete, accurate, and deployable.
Ongoing Data Optimization
Data can be messy. It must be cleaned and completed upon ingestion in order to build a foundation for success. But that’s only the beginning of data maintenance. CDPs must continuously optimize a brand’s customer data, linking multiple (and conflicting) data elements from disparate silos to ensure that identities are resolved to a single consumer, while keeping the data up-to-date. In maintaining linked, de-duplicated and cleansed consumer identity data, the CDP will empower brands to put an end to data silos and minimize costly data decay. In doing so, marketers are able to reduce waste and maintain their ability to reliably reach the right consumers at scale with relevant and compelling messaging as their lives evolve and change. Maximizing data hygiene now minimizes the mess that customer service agents must clean up after misidentifying a loyal customer later.
Paint the Full Picture
Accurate linkages within a CDP ensure a 360-degree view of consumers. But CDPs should continue to add value by providing a wealth of demographics that marketers -- no matter their industry -- can activate. CDPs that can offer a comprehensive view of each customer that consists of both online and offline indicators provide the greatest value to brands. Aside from identity markers such as name, address, phone and email, CDPs should also include rich attributes to enable personalized messaging and segmentation.
Personalization Is Not Optional
Customers expect personalization in nearly every interaction with a brand. CDPs that leverage the best third-party data available ensure marketers are in a position to create relevant and valuable 1-1 messaging. And a CDP’s lifestyle attribute data and intelligence should not only fuel personalization, but also track changing preferences and needs as life journeys unfold (e.g. marriage, presence of children, new homes) so brands’ messaging can evolve along with their customers. By filling these gaps in consumer records with third-party data, brands are empowered to drive smarter marketing that leads to better conversion rates, ROI, and lifetime value.
No matter the brand’s goal, a CDP’s handling of identity data is a competitive differentiator. CDPs that can verify, score, link, cleanse, normalize first-party data offer the strongest possible foundation for a 360-degree view of the consumer and can become an integral part of the martech stack. CDPs must empower brands to personalize interactions with relevant messaging no matter the customers’ life stage through identity completion and enrichment. Adding rich third-party demographic and lifestyle attributes will help CDPs stand out from the pack so that these CDPs will enjoy the same brand loyalty they enable for their own brand customers.
June 18, 2020
EdTech: How to Keep Learners Engaged Post Lockdown
By Varun Dattaraj, Netcore
We have all been tempted to pick up a new skill or two during the COVID-19 lockdown haven’t we? Whether it’s a result of peer pressure or the situation demanding us to upskill ourselves, we have definitely come to the happy realization that our journey of learning doesn’t stop despite the lockdown. For online learners like business professionals or students, this can be attributed to Education Technology (EdTech) platforms. EdTech and e-learning has opened up a whole new avenue for us to make use of our time productively during the lockdown. And yes, the five-minute food recipe videos on YouTube have been a great source of inspiration too.
The EdTech industry has been a huge beneficiary of the digital revolution in the education sector. EdTech in India alone is a $2 billion industry, while being home to over 4,450 EdTech startups. From basic school education to professional certification programs, there’s something for everybody. EdTech at large caters to learners across generations, and the need to engage them all is a great challenge.
With learners eagerly adopting EdTech platforms during the lockdown, EdTech has garnered a lot of interest. With learners being restricted indoors, their behaviour has also changed. Learners are regularly accessing EdTech platforms on their desktops/laptops for a more immersive experience rather than through hand-held mobile devices with smaller screens.
A report by SimilarWeb in early May this year analyzed the top 35 e-learning websites, to understand the pandemic’s overall impact on the EdTech industry in India. It found that this segment has seen a growth of 25.87% over the last year. Players like Udemy, BYJU’S (Think & Learn Pvt. Ltd.), Coursera, Toppr, Gradeup (Gradestack Learning Pvt Ltd), and Unacademy are seeing these times as an opportunity. Some of the takeaways from the analysis were:
Based on a report by Harshit Kalra
Based on a report by Harshit Kalra
A lot of these platforms have been offering deep discounts and free subscriptions as a part of their initiatives to enable learners to continue their learning journey at home. How can these platforms adapt to a post-COVID situation when learners go back to their regular lives? While remote learning is here to stay, how can they plan for future growth?
The way ahead for EdTech
Artificial Intelligence (AI) in 2020 is a powerful tool to enhance customer journeys with personalization. It can help address core business objectives like user experience for EdTech platforms. End-to-end user experience across multiple platforms like mobile, web and apps are like an infinity loop, they cannot be ignored post the lockdown or COVID-19 crisis. EdTech players should therefore work towards creating a wonderful experience for their users at every stage. This will go a long way in reducing user churn rate in the long term. Additionally, users with higher lifetime values should be identified and prioritized with value offerings.
So what can EdTech platforms do differently to retain their users post the lockdown? Well, they can surely take a leaf out of the playbook that OTT streaming platforms have been using for some time now. While EdTech platforms rely mostly on video-based content like OTTs, it is now the time for them to innovate with personalization technologies. They will need to optimize, personalize and humanize their offerings in the near future. As competition in the segment is already cut-throat, it will become critical for EdTech platforms to know the likes, dislikes and interests of its users by carefully analyzing their digital footprints across devices with the help of Marketing Technology (MarTech) tools.
Here are some ways EdTech can continue to holistically engage its users in the post-COVID world:
EdTech platforms today can be accessed via both apps and internet browsers on a variety of devices. To ensure user engagement and delight throughout, AI led tools can be used to plot personalized customer journeys. Customized recommendation engines can be built which are geared towards optimized course suggestions and a variety of other applications for its users.
For example: if a segment of users on an EdTech platform have signed up for courses related to digital marketing, with the help of AI they could receive:
Customer service can be a make or break for competitive industries like EdTech. AI powered chatbots can be used to quickly address customer queries 24*7. AI powered chatbots in contrast to logic based chatbots are more efficient, not to mention more affordable these days. They can be intuitively used for a variety of use cases.
For example: chatbots can help users get a quick walk through of the courses that a platform offers in a certain domain and guide them to one that best suits their requirements through a series of self-assessment questionnaires.
Gamification is a great way to get users to spend more time on a platform and explore all its features before the trial period ends. EdTech platforms can engage users in a fun and interactive way with the use of dynamic elements like timers, spinning wheels, shareable course completion badges and learning contests.
With all the user data generated during the lockdown, it will become essential to learn about habits of users, interests, their likes, dislikes, wants and needs to stay relevant and competitive in the market. These insights can further be used to cross sell products or services to existing users and retain them.
For example: a learner who has completed a digital marketing course may be interested in a marketing analytics course in the future.
App push notifications may be annoying to some users, but if done right, they can yield great results. Highly personalized messages can be sent to nudge users in a relevant and timely way using AI. Notification reach can be amplified with send time optimized media notifications. User attention can be garnered with customized notifications by testing various parameters with the help of AI.
For example: users who have signed up for digital marketing courses can get app push notifications about course updates and new courses in digital marketing which may be of interest to them.
With the latest updates on popular internet browsers like Firefox and Google Chrome, website notifications have taken a hit. These changes favour quieter notifications on browsers for increased engagement. Quiet notifications are triggered discretely only after the user has completed certain actions on a webpage like read 75% of its contents. Customized and contextual notifications will result in higher intent traffic flowing into websites, better engagement, lower churn rate and a seamless experience for users who are likely to spend more time on a website.
While the lockdown situation has come with its own set of challenges, it offers a great opportunity for marketers to break the clutter with exceptional email campaigns. Alert users about webinars, offers, new courses and industry updates of their preference with email automation. Customized email campaigns triggered by customer actions in real-time can result in an uptick of high-intent traffic on platforms. Check the efficacy of multiple versions of email campaigns with MarTech tools.
The lockdown has created a situation where messaging platforms like WhatsApp have witnessed up to 40% increase in usage. By using WhatsApp Business Solution, EdTech platforms can seamlessly interact with customers on WhatsApp. With features suited for a wide variety of use cases in the form of templates like customer queries, billing, feedback and alerts, instant user gratification is guaranteed with this service.
The power of user created content is relatively unexplored in e-learning. Make use of UGC on EdTech platforms (example - math tricks & shortcuts) to improve peer learning and engagement. Studies have shown that peer learning is powerful. But UGC should ideally be monitored using AI and help drive search relevance on EdTech platforms.
It is important to keep your communication with users as humane as possible during these testing times. Keep users in the loop about all the latest happenings in the industry and help them choose courses most aligned to these needs. This is most relevant to users considering shifting career domains like an IT professional looking at the data sciences job market.
EdTech users may be required to reskill themselves post the lockdown too. Using the best MarTech tools, EdTech platforms can help deliver what their users need the most - the right kind of learning to keep themselves relevant in the post-COVID world.
Ready to learn what MarTech suits you the best? Reach out to us today.
June 11, 2020
9 COVID-19 Campaign Examples and Learnings from Leading Insurance Brands
By Rohit Srivastav, Netcore
The pandemics and crises in the past have shown that the insurance sector has been more than prepared to take the brunt. But then COVID-19 isn’t like any other crisis. The global slowdown is inevitable and with the world GDP dropping, every bulletin is not showing signs of a quick recovery.
But the show must go on, so does engagement between your brand and your customer. Here are a few ways the biggest brands in the insurance industry are engaging with their customer during the crisis.
Business As Usual?
What happens if I have a claim in such times? What if my insurance renewal is due? What if I want to buy a new policy?
So many what-ifs. The COVID19 crisis made us question everything. That includes the operative ability of critical businesses. The brands in the insurance sector had to combat these uncertainties rising in the customers’ minds on time.
And they did it well.
We Care for You
As COVID-19 came knocking the whole country went into a sudden lockdown. No one was prepared for it. No one could have. Neither the brands nor the customers. In moments like these, all you need is empathy and care. And that’s what the brands did right.
There’s a catch with the insurance business - no one wants to be in a situation where they have to use insurance. But then a pandemic is exactly one of those times. And the brands didn’t disappoint. They used all the channels at disposal including emails, SMS, social media, push notifications, and more to spread the right information and to guide customers in the right direction.
Here’s how the brands did it right:
We’re Here for You
With the rising uncertainty about almost everything, the customers needed to know that they can still rely on the brands they have trusted for exactly times like these. Brands responded by communicating the availability information of their staff at select branches.
SMS, the underdog of all communication channels, came to rescue. We saw brands choosing texts to be the most reliable channel for this vital communication.
When the Work From Home became mandatory for almost everyone around the country, the brands ensured uninterrupted support with the help of Chatbots on top of conventional support systems.
Digital is Default
With physical contact becoming highly risky, the importance of going digital became indispensable. All the preparation and investments that the brands have been making in their digital transformation efforts paid off big time.
We saw brands being creative about communicating this and we absolutely loved all of it. Here are a few that demand a mention.
Get Busy Living
The lockdown came with its own set of perks - a lot of family time. People finally had more time on their hands than they can spend. Brands took this as a chance to engage with them in a non-transactional way.
The notifications went buzzing with home exercises, mindfulness tips, kitchen hacks and so much more.
Content is King. Engagement is Noble.
“People will forget what you said, people will forget what you did, but people will never forget how you made them feel.” - Maya Angelou.
With the transactional communication reduced to a bare minimum, the brands had two options - be silent or be innovative. The majority chose the latter. They started engaging with their customer base with creative ideas and offers.
From free e-newspaper subscriptions to virtual fitness sessions, brands went up and beyond to be relevant and engaging. A lot to learn here.
Watching the biggest of brands executing top-notch engagement campaigns inspired us to don our own thinking caps. Here’s our inspired list of a few more campaigns that you can add to your COVID-19 marketing playbook:
Rise of the Bots
WhatsApp as a channel just opened up for business and brands are killing it with customer engagements at an unimaginable scale. We did something really cool that involved Big B, Big Billion Days, Flipkart, and WhatsApp.
However, those were Pre-COVID days. So what now? Especially for the insurance sector?
WhatsApp bots can be your foot soldiers to automate document collection, e-dispatch of policy, payment collection, and all other transactional customer communication and interactions.
#GetPersonal with Your Customers
Social distancing mixed with lockdown has restricted the human advantage that business leverage. How to ensure personalized experiences for each one of your customers?
Work on delivering an omnichannel hyper-personalized experience for experiences based on understanding their micro-engagements.
Make Your App COVID-19 Relevant
Times are not as usual. So will be the use cases for your app. Can you push quick updates on your app to make it relevant in these times?
PhonePe, one of India’s biggest UPI apps changed their app homepage overnight to show just the essentials allowed and necessary during the lockdown.
There’s a play there. Here are a few features the brands in the insurance sector can build:
a. social trackback
b. self-health check
c. locating health networks
One thing that comes out in almost all our customer interviews is: We might never get back to the old normal. Post-COVID world will be a new normal world. The new normal will be empathy driven world. A more humane one.
That needs to translate into your messaging, engagement, and overall brand experience.
The brands that stayed relevant during the crisis were the ones who were agile to respond to the change and build their experiences around the tenets of empathy, creativity, and, personalized experiences.
June 4, 2020
Mobile App Personalization: 10 Ways to Convert and Retain App Users at Scale
By Pradyut Hande, Netcore
We live in an inter(net)-connected world where smartphone penetration, ever-improving mobile network infrastructure, and enhanced access to cheaper data are driving the app economy.
With over 2.7 billion smartphone users and 1.35 billion tablet users globally, there are 2.8 million apps on the Google Play Store and over 2.2 million apps on the Apple App Store. In fact, there were over 205 billion app downloads in 2019 alone.
Sure there’s a massive opportunity to empower users, strengthen your online presence, generate top-line revenues, and counter competition - but, that’s easier said than done.
One fool-proof way to consistently power user engagement, conversions, and retention at scale is by integrating a strong personalization strategy into your mobile (and omnichannel) marketing machinery.
And, personalization - today - has gone above and beyond the obvious. Addressing your users by their first names over a push notification or sending them a discount coupon on their birthdays over an email campaign is great.
But, it’s not enough! And, especially so in the current COVID-19 environment where hyper-competition for users’ screen-share, mindshare, and wallet-share has only heightened.
Your users expect and demand an end-to-end, tailor-made customer experience, right from first-time app launch. This assumes even greater significance across industries such as e-commerce, OTT, and news and media.
At Smartech, we understand how important 1:1 personalization is across platforms, channels, and devices to your omnichannel marketing efforts. And, with that in mind, we’ve bolstered our mobile app personalization module.
Here’s how you can now deliver highly differentiated user experiences at scale on and through your mobile app:
1. Build a solid foundation of and on user data:
Effective personalization is dependent on you gathering the right user data across channels and platforms. Gathering the right demographic, geolocation, and device-type data-points is important. Start capturing these basic data-points at the registration or login stage of your user onboarding flow.
But, you also need to capture your users’ in-app behaviors, actions, inactions, responses and interactions to multi-channel marketing campaigns. This will help you create and constantly enrich a unified view of every user, in real-time.
For instance, if you’re an e-commerce app, you need to log actions and details such as products searched, product categories browsed, products added to cart or wishlist, products purchased, payment mode chosen, and most common paths towards conversion.
If you have a physical store, you also need to ensure your transactional data is funnelling back into your data backend so as to personalize subsequent shopping experiences across both your website and/or app.
Simultaneously, you need to track relevant metrics attached to these actions to gather granular insights - recency and frequency of app launches and purchases, average time spent per screen, ratio of products added to cart and finally purchased, actual conversion rates, etc.
Analyzing these diverse data-points will help you gain actionable insights and develop relevant user segments.
Our AI engine, Raman, can actually help you dive deeper. Now slice and dice behavioral data to arrive at a segment of one, with AI doing all the heavy-lifting for you! Our cutting-edge collaborative and content-filtering algorithms make it possible for us to ingest large amounts of user data-points and behavioral footprints.
Raman also constantly learns from both clickstream and customer eyeball data; i.e. from both live actions and inactions that can be attributed to a user.
That’s not it, though! We also help you map and leverage data-points such as device price and brand of device to further fortify device-related information.
Once you have your user data and analytics backbone in place, you can focus on adding muscle to your personalization strategy.
2. Personalize the app home screen on first-time launch:
Modern marketing has driven home the fact that one size doesn’t fit all. Depending upon your app category and the quality (and quantity) of demographic data-points that you’re able to gather during the first-time onboarding flow - you can immediately start delivering a personalized user experience on your app homepage.
For instance: If you are an OTT music streaming app; data-points like name, age, gender, preferred genres and languages, favorite artists, etc. can be used to curate a first-degree personalized list of content recommendations on your app home screen instantly.
You need to strike the right balance between deploying an onboarding flow that educates new users on functionality, key features, etc. and capturing relevant demographic data-points (without being explicitly intrusive) to start delivering 1:1 user experiences ASAP.
Notice how Hungama Music, one of India’s leading home-grown music streaming apps, does this during the user onboarding process to quickly begin personalizing content recommendations when the home screen launches for the first time.
You can subsequently highlight the most relevant recommendations on the app home screen on future app launches. This can increase your CTRs by 90-120% while uplifting content consumption by 5-7%!
3. Tailor-make the app navigation experience:
Apart from your users’ search, browsing, click behaviour and consumption history, you can personalize how your users navigate across your mobile app.
Other parameters such as gender, buyer personas, geolocation, time of day/timezone, seasons/weather, etc. can be harnessed to personalize the banner images, graphics, CTAs, trending products and offers, etc.
Also, leverage insights from your users’ most common paths towards conversion to further optimize their navigation journey. The more individualized the navigation journey, greater the probability of your users finding exactly what they want, faster!
Check out how AJIO, a leading e-commerce clothing retailer offers a gender-based app navigation experience for both men and women.
4. Personalize the search experience:
Any search made by a user on your app is a solid signal of intent. Intent to purchase a product. Intent to book a ticket. Intent to consume content. And, you need to value these search inputs.
Every search action undertaken by specific users tells you about their instant wants, needs, and preferences. And, your objective must be to direct your users to exactly what they want faster.
With the assistance of our AI engine, Raman, you can now instantly populate product or content recommendations based on the partial or full search terms inputted in the search tab. This would also take into account historical searches made.
These recommendations continue to get more accurate with each subsequent search action that a user takes, helping you to significantly reduce the path from product discovery to purchase.
For instance: As an e-commerce platform that specializes in the online retail of cosmetics, you can start giving the most relevant product recommendations for lipsticks when a user is searching for a particular brand of lipsticks.
Here’s how Amazon leverages search personalization to show me sub-product categories that are most relevant to me.
5. Showcase real-time AI-led predictive recommendations:
Falling user attention spans and quick access to alternate apps in the same category can fuel switching behavior. This is why you need to respect the time an individual user potentially invests when he/she launches your app. Near-instant product discovery and top-of-sight visibility become critical to conversions or consumption.
Our AI engine, Raman, enables you to show the most relevant product or content recommendations across your Home Screen, Product Display Screen, and Product Listing Screen.
Much like our existing onsite dynamic personalization for websites, you can replicate the same for your mobile app. These live recommendations are optimized for mobile display and click, ensuring that you maximize the use of screen-space on every scroll.
Moreover, these recommendations get more refined with each shopping session that an individual user engages in.
Not only does our AI engine account for clickstream data, but it also accounts for customer eyeball data. This implies that negative signals for any product or content recommendations that are “seen-and-not clicked” and “not seen-and-not clicked” feed right back into the AI engine - all in real-time.
This helps you improve behavioral predictions by almost 20%.
6. Re-order product or content categories for greater context:
If you have an e-commerce, OTT, or news and media app; you are bound to have hundreds of products or content options within your product catalog or content library.
Our patented AI algorithms can re-order these categories in real-time for individual users so the most relevant product or content recommendations show up right at the top.
Remember the Golden Rule?
Near-instant product discovery and top-of-sight visibility become critical to conversions or consumption!
In the below example, our AI engine can actually re-order listed product categories to show the most relevant categories from top to bottom, to individual users in real-time.
7. Create an in-app personalized storefront or playlist:
Go one step further and allow Raman to specially curate a mobile boutique - composed of only those products or content recommendations that an individual user is most likely to view, buy, or consume on your app!
Delight your users with a unique online shopping or content consumption experience while increasing CTRs by 120-150%, every time they launch your app.
Neural networks bolster Raman’s real-time, reinforced learning capacity; as it refreshes the list of recommendations every time a user chooses to launch the personalized storefront, watch-list, playlist, or read-list.
Netflix curates a personalized watch-list for each subscribed user that is refined and refreshed after each active session on their app or website.
Spotify actually goes a step further to create multiple weekly playlists based on preferred genres, favorite artists, and music language.
8. Deploy live contextual recommendations via in-app messages:
You can optimize your in-app personalization strategy by triggering relevant product or content recommendations and appropriate offers at individual users through in-app messages.
These can be triggered when a user lands on certain pre-defined screens or when a user scrolls a finite percentage on certain pre-defined screens.
Our AI engine, Raman, intelligently triggers these in-app messages only when they truly make sense, without disrupting the ongoing user experience.
For instance: As an e-commerce app that has a health and hygiene product category, if a user has added hand sanitizers to his/her cart, you can trigger a relevant recommendation for facemasks and hand-washes, as a dynamic product bundle.
Such a strategy can also help you increase your average order value through effective cross-sell and upsell opportunities.
9. Personalize your recommendations across other channels:
While personalizing the entire in-app mobile experience is important, what do you do when your user is not in an active session? You can’t leave that to chance.
The essence of personalization lies in delivering a rich and unique 1:1 user experience across multiple digital touchpoints. This implies that you need to activate other critical mobile marketing channels such as emails and app push notifications to continue delivering these personalized recommendations.
With Smartech, you can now trigger laser-focused product recommendations across these channels to maintain top-of-mind recall, bring inactive users back to your app, and potentially nudge them towards a conversion event.
Here are the kind of contextual recommendations that you can deliver to pursue conversions beyond just your mobile app, especially in the e-commerce space:
Depending upon what channels of customer engagement are working best for which customer segments and buyer personas, you can optimize your multi-channel mix, as well as the send-times for these recommendation campaigns.
Raman evaluates the real-time performance of these triggered recommendations for relevant user segments to offer you deep-dive insights on the preferred channels and send-times that are likely to produce the best engagement and conversion rates.
10. Re-target existing users with personalized ads:
Regardless of your best efforts at encouraging your users to make a purchase or spend more time consuming content on your app, there will be drop-offs and users that straddle dormancy.
That’s the harsh reality of mobile marketing!
But, our AI engine, Raman, can actually target individual users with the most relevant product or content recommendations through optimized re-targeting ads across Google, Facebook, and Instagram - at the right time.
For instance: As an e-commerce app specializing in fashion apparel, you can re-target existing users with the most relevant t-shirt, shirt, or trouser related recommendations that are most contextual to individual male users. Depending upon where a user is most likely to see this ad, Raman will display a mobile-optimized ad, nudging the user to click and re-launch your app.
Personalization at Scale = Stickier 1:1 Experiences = User Retention
Mobile app personalization is not a set-it-and-forget-it project. While our AI engine, Raman, will help you deliver relevant recommendation-led 1:1 user experiences; human intelligence also plays an important role. When AI and human intelligence comes together, you can continue to optimize experiences for a segment of one!
Also, remember that mobile app personalization is another weapon in your larger omnichannel personalization arsenal. Wowing your users just on one platform is not going to cut it! They expect a consistent and frictionless experience across your website, mobile app, and marketing channels (across devices).
To learn how you can get started on your mobile app personalization journey - with AI as your sidekick - like the giants in the industry do; get in touch with our growth experts today!
[P.S. Hang on. We’ve got something special in store for you! We understand that it may not be business as usual for you currently.
May 25, 2020
Use of Predictive Recommendation Models by CDPs
By Vishal Sukheja, FirstHive
To solve the problem of fragmented sources of data, marketers have traditionally used rule-based approaches, but today that is simply not enough. Rule-based approach only fits into those scenarios within the limited set of rules, falling way short of what is needed and keeping the marketer out of the other set of unknown possibilities.
These limitations brought us to introduce machine learning algorithms in our core CDP platform that can support the following use cases.
This is one of the top preferred features among FirstHive users. Using data such as look-alike customers, response rate, demographics, and many other parameters, machine learning algorithms can predict customer segments with formulation cues. These are segments created using automation within the platform. This helps in building more mature segments that would hence be designed for optimization.
Advanced Customer Data Management
Predictive models are used to develop advanced identity resolution algorithms that come with the ability to support multiple logical data stores and apply different rules to them. They come with in-built connectors and capabilities for advanced data transformations. They also carry out complex data management and schema changes on an ongoing basis using a graphical interface.
Offline Aggregation to include Omnichannel Strategy
Only a few of the CDPs like FirstHive also cater to the function of offline data aggregation which most often occurs at PoS terminals, QR codes reading, connected smart devices, and other similar instances. This is critical for content recommendations that are in the offline universe of marketing channels. To estimate which of those offline channels are best for content delivery, predictive recommendations bring in true value.
Integrated systems such as email, ticket resolution, chat, and voice calling that build a customer support interface can be managed better for optimized resolution. Based on customer profile and persona tagging, each customer could be handled in a way that is most appreciated by her. Customer support associates will be equipped with information about how each customer is comfortable with a support channel and their response. If the customer is a first-time support user, then the associate will be informed about her preferences.
Algorithms provide proactive recommendations that the customer support executive can have handy while tackling customer queries.
Real-time Customer Data
Apart from use cases where algorithms are deployed to churn out recommendations and have them stacked in a dashboard, real-time data is also a predictive model capability. Machine learning algorithms are formulated to churn out recommendations using a combination of historic and real-time customer data.
Such data is most often used as a feedback mechanism for campaign activation across different channels.
More Use Cases
Some other use cases where predictive modelling can be actively deployed are outbound marketing campaign support, e-commerce recommendations and optimization, lead scoring and predictive scoring models. Within a CDP the most common models that are put to use are clustering models for customer segmentation, propensity models that determine probability and predictions, collaborative filtering for recommendations, and content-based models that are used at times when your systems lack historical data to build recommendations upon.
Should you have any questions about your specific use case, feel free to reach out to us at firstname.lastname@example.org.
April 30, 2020
5 Ways Marketers Get Better Results with Customer Data Platforms
By Christina Stubler, Arm Treasure Data
If you are a marketer, you may be wondering what’s behind all the interest in customer data platforms? At the top of the list is the ability for your team to transform the way it works, as well as the way customers interact with your brand. CDPs break down martech silos, unify online and offline customer experiences and data, make marketing more efficient, and drive better results. A CDPs capabilities range from collecting and storing comprehensive customer data, to seamlessly integrating with existing marketing technology, and delivering actionable, data-driven insights.
These are just a few of the features that make CDPs a wise investment for any data-driven marketing team – which should be every marketing team. While addressing marketing and IT’s key concerns, CDPs play a key role in improving marketing’s efficiency, data quality, and security.
What Is the Definition of a CDP?
David Raab, founder and head of the CDP Institute, defines the term CDP like this: “The official CDP Institute definition is ‘packaged software that creates a persistent, unified customer database that is accessible to other systems.’” Raab says the following points are critical in understanding what a CDP is:
One critical thing to realize is the distinction between a customer database in general and a CDP in particular: a CDP is packaged software to build a customer database; you could also get a customer database by building it yourself or buying a larger product that included it.
Here are five ways you and your team can get better results with an enterprise CDP:
1. Finely Tuned Segmentation
The more data you have – and the more correctly unified it is – the better you’ll understand your customers, including their purchase behaviors, desires, and motivations. This, in turn, helps you create smart audience segments for more relevant customer personalization.
With clear, unified views of your customers, you’ll know exactly how to market to them and lead them further down the path to purchase – and a CDP can provide you with that view.
2. Real-time Engagement
What’s even better than detailed data about an individual’s history, preferences, and behavior online? Getting that information in real-time.
To deliver more targeted offers and better outcomes, a holistic understanding of customer engagement is crucial. It involves the real-time aggregation and analysis of data across marketing, sales, and customer service. A CDP can deliver this information and even use it to orchestrate campaigns, promotions, and tailored user experiences. As a result, you can more consistently provide personalized omnichannel brand experiences for your customers, no matter what channel they’re on.
A CDP also helps keep your customer data accurate, reliable, and secure by constantly cleaning, translating, and updating data over time. With round-the-clock access to this up-to-date data, you’ll quickly improve the quality of your customer interactions.
3. Omnichannel Customer Experience
With an enterprise CDP, you can unify all customer data securely and provide a consistent, positive experience that helps guide customers through their journey. A good CDP can process information from multiple applications – Marketo, Facebook, Salesforce – and combine them to get a full understanding of what your customers want, buy, read, visit, watch, and more.
Advanced CDPs will automatically analyze new data based on the unique rules you establish within the system. You can analyze and organize data for audience segmentation, customer personalization, campaign optimization, push and pull notifications, syndication, and more.
From there, you can use this newly categorized information to answer your most pressing marketing questions and begin improving results across channels by improving or changing your marketing strategies.
4. Cross-Sell and Upsell
By using your CDP to quickly consolidate information from all channels, you’ll know when your customers make a purchase and it’s time to shift to a cross-sell or upsell strategy. Once a customer decides to buy, you can improve retention with personalized, data-driven recommendations. This can extend your relationship far past the first purchase.
5. Improve Products and Services & Identify New Revenue Streams
Customers expect personalized content and better customer experiences – but not at any cost. Marketers need to get creative and rethink the formats of content used to reach customers across devices. This includes what promotions marketers send – and when – as well as what types of alerts and notifications are pushed when customers step into a physical store.
Customers want special attention and improved products and services that meet their specific and unique needs. A CDP helps you gain a more complete understanding of your customers’ preferences, so you can prioritize your product and service offerings to more closely align with customers’ wants. Or, you can create an entirely new stream of revenue by identifying and capitalizing on trends borne out of your customer data.
Delivering Customer Data and Better Results
Marketers can’t ignore customers’ increased demand for frequent, personalized marketing messages. It’s time to invest in a customer data platform to transform your marketing department and deliver exceptional customer experiences.
Learn how Arm Treasure Data enterprise CDP can help you take advantage of the big opportunities in omnichannel marketing. Request a demo to get started.
April 23, 2020
The Omniscience Option: Next-best-action Recommendations that Work
By Dilyan Kovachev, Arm Treasure Data
What marketer wouldn’t want to be omniscient, especially when it comes to understanding customers? Unfortunately, it’s not yet a martech option, but next-best-action prediction is as close to omniscience as marketers are likely to get in this lifetime. Its purpose is to use data-driven insights and analytics to predict the next action to take, whether the application is a customer service call or a marketing or social media campaign. Often called “next-best-action decisioning,” it’s been a marketing Holy Grail for a while.
But the difference lately is that predictive martech and analytics – fueled by advances in data lake technology, Customer Data Platforms (CDPs), and Big Data analytics – have gotten superbly good at it. So good, in fact, that “the omniscience option” is no longer a science fiction fantasy.
Next-best-action Helps You Market to Individual Customers
Next-best-action is uniquely suited to service, support, and marketing because it typically uses data from all of these functions. It focuses on specific customer preferences instead of positioning a product for larger nebulous groups of buyers. The idea is this: if you can accurately forecast what a buyer wants – exactly when they’re looking for it – and you can figure out how to reach them when they’re ready to engage, you can provide a great experience for them that improves sales and cements customer relationships. Plus, you also deliver all that at the lowest cost. It’s a win-win!
While easily explained in a blog, this vision is a bit harder to achieve in the field. It requires a lot of data about customer behavior, a real-time engagement feedback loop, and the power of prediction. These three capabilities alone are hard to solve, so you can imagine the challenge of putting them together into one seamless process, especially if you have to do all the technical work in your own organization.
While some marketers have prematurely written off next-best-action decisioning as an improbable application for customer analytics, other marketers are investing in the expertise and technologies they need to build next-best-action systems.
The Key Ingredients in a Next-best-action Recommendation System
The basics of next-best-action recommendations require marketers to understand what, where, and when buyer engagement happens. If this engagement is happening through digital channels – which is likely, given that 89 percent of buyers start their process of discovering products and services with a search engine – behavior becomes easily quantifiable. You get to understand what products visitors to your website are viewing and how many. You can determine their interest through numbers of clicks on a page or on an advertisement. As well, you can analyze patterns people display across channels (such as logins to mobile apps and number of previous in-store purchases), that signal they’re ready to purchase something.
With rich data sets that are unified around individual customers, the what, where, and when of engagement is ready for action. We’ll break them down for you in the next sections and dive into the technological capabilities to make “best actions” happen.
Campaign Activity Defines the What in Next-best-action Systems
For marketers, the “what” in next-best-action systems is defined by campaign activity. This is how marketers engage with buyers. So, when a marketer sends an email, delivers a message through an ad, or sends a promotion in the mail, they’re looking for a response. Maybe that response is a coupon redemption or click-to-download content.
Sometimes, a buyer’s response is to do nothing. This is important to know for next-best-action decisioning, so you need to ensure that your methods of aggregation can handle “null” values without a lot of translation and workarounds. No matter the reaction, what that buyer did (or didn’t do) to respond is going to determine what the next offer should be. Are you collecting what buyers do in response to your campaigns? And, where is all of that response data?
Most likely, that data lives in the systems you used for the individual campaign tactics – your email system, your social site, or your agency’s database. Some of it might be in your loyalty program database or point-of-sale systems. And, I’m going to bet that it’s not all in one place, unless you’re using a CDP, in which case you might already have a set of accurate, unified customer profiles.
The Where and When Requires Predictive Analytics
Once behaviors are analyzed and patterns are discovered, marketers can make some decisions about the next touchpoint. Do they want to retarget people who visited the site two or three times but never purchased anything? Do they want to send a follow-up email to people that stopped by their booth for samples? Maybe they want to do both, but when do they initiate the next touchpoint? Generally, marketers reach people by using a couple of different strategies:
Both have their merits, but what’s really important is where and when a person is most likely to want to engage with your content, message, or offer. This is the context of “where” and “when” in the next-best-action recommendation system, and if the prediction is accurate, it can save you from unnecessary campaign activity and free up time for more meaningful projects.
Again, the details of your campaign data can be put to work here to help predict which channels matter the most to specific people and when they can be reached. Where do they respond and which channels do they ignore? Are they more likely to engage at night while they’re online? Through machine learning, daily or even hourly data can be analyzed to produce a channel propensity score. A high score represents a strong correlation to a particular channel (such as social site or email) and time. These scores can then be added to an individual buyer’s profile.
To use those scores, a marketer will need a way to discover individuals according to their behaviors (what) and their propensity to engage in a specific channel (where) and at what time (when). Just as they have for years with demographic and household data, marketers can use segmentation tools to discover people with similar behaviors and preferences. The process of audience segmentation is the same, save for one major adjustment; it requires the flow of large, fast moving data sets to place individuals into specific segments automatically.
Without the ability to dynamically segment people, using behavior as a basis for analysis would be very hard for marketers. Can you imagine the time you would need to discover when shoppers abandon their carts and sort them one by one into groups for your next email campaign?
Dynamic segmentation tools allow marketers to zero in on precise groups of people as they exhibit the behaviors to be followed up on, for example, all people who browse a site without purchasing. As well, marketers can use those tools to drill down further into where and when those same people are most likely to respond to another message, offer, or piece of content.
Putting the Next Best Action Pieces Together
Making next-best-action decisions requires the “what, when, and where of buyer engagement.” Marketers want to make smart choices to engage buyers, and they want to make those decisions quickly and effectively. There are important capabilities and knowledge needed to support the process of using behavioral data for next-best-action decisions. Here’s what you need to know to make the recommendation system work.
First, event-level data needs to be captured continuously and timestamped for understanding the sequences of those events. Visitor behavior on your website needs to be streamed in and organized alongside email and ad response data. This unification process isn’t trivial and requires data management expertise, but the outcome is an individual profile of every visitor, customer, and potential buyer. It reveals their behavior and when they exhibited good and bad responses (such as downloading content or abandoning a cart).
Second, machine learning algorithms can pick up on signals marketers wouldn’t be able to discover on their own. How likely a buyer is to engage in a given channel at a particular time can be more accurately determined by moving past frequency analysis to propensity modeling, which captures more data points based on how correlated they are to the behavior you want to predict. With greater accuracy, marketers make more confident decisions on where and when to engage a buyer.
And last, dynamic segmentation tools can automatically discover behaviors as soon as they’re exhibited and help marketers initiate communications, offers, and other touchpoints quickly. Propensity scores add key information about when and where to best reach people for the greatest impact. With scores and event-level details at their fingertips, marketers can get as granular as they’d like with their segmentation and target people with the best offers, in the right places, and at the optimal time. With greater precision, they can be more effective, fast.
For more on what makes next-best-action recommendations work, watch Become a Data Pioneer with Arm Treasure Data.
April 20, 2020
5 ways your digital analytics strategy is hindering your customer experience
By Tiffany Carpenter, SAS
These days, an ever-increasing number of customer interactions are taking place over digital channels and every single digital interaction offers an incredible source of customer intelligence for organizations to tap into.
With every visit, customers leave a valuable trail of digital breadcrumbs. These breadcrumbs give organizations the ability to follow each individual customer journey and each customer’s experience along the way. With every browse, click, like and share your customer creates their own digital footprint. And with their consent, brands can harness this rich source of data to anticipate and deliver on the needs of each individual customer, optimize each customer’s journey, and unlock new competitive value for the organization.
Of course, this data must be treated as personal data and companies should provide comprehensive cookie notices to educate users on how they plan to use their personal data, on an opt-in basis.
But despite many customers still opting-in to share this data, organizations are struggling to tap into this readily available digital intelligence in a meaningful and effective way.
The reason? These five recurring challenges create barriers to unlocking the true value of digital data:
Organizations leading the field in digital intelligence are opting for a single view of individual customer-level behavioral and experiential data across digital channels that can be easily joined up to offline data to gain much deeper insight into the customer journey.
The ability to analyze data at this level of detail is helping these organizations go beyond the “what” and “how” of traditional digital analytics and answer the more valuable “who” and “why” questions. Who are my most and least valuable customers? Why do they behave as they do on my digital properties? What simple changes could I make to alter some of this behavior?
By capturing granular, time-stamped customer-level data from every digital interaction about everywhere your customer went, everything they did and did not do and everything they see and did not see, organizations can optimize their customer experience and create competitive advantage.
April 16, 2020
5 Types of Ad Tech Marketers Need to Know
By Henry Li, Arm Treasure Data
Digital advertising has reached new heights of complexity. Today’s omnichannel ad campaigns reach across many different platforms at once, from publisher websites and mobile apps to search engines and social media. Yet paradoxically, campaigns have become more and more personalized, using highly tailored, targeted ads to reach specific audiences.
All of this makes for an intricate process with many different participants, from advertisers and publishers to third-party vendors.
As you move further into ad tech, you’ll find yourself navigating a complicated and ever-changing ecosystem. Here are some of the most important types of technology you’re likely to encounter, including important differences in function and usage.
1. Demand-Side Platforms
As the name suggests, demand-side platforms (DSPs) serve people who want to purchase ads from publishers. If you’re in marketing, that means you.
A DSP is an automated system that vastly simplifies the buying process for advertisers, who can purchase targeted ad impressions from many different sources through a single interface. DSPs usually use real-time bidding – that is, automated auctions that take place in just milliseconds.
To make targeted ad buys, DSPs may use audience data from other sources such as publishers, advertisers, and external data providers. A DSP can use these datasets to guide targeted ad buys. The system can also track campaign performance and use this data to improve ad targeting and media buys.
So where do DSPs find ad impressions to buy? DSPs may buy ads directly from publishers, supply-side platforms, and ad exchanges.
In recent years, DSPs have come under pressure to provide more transparent pricing, better ad quality, and more reliable data about campaign performance. Ad fraud continues to be a major challenge – as it is for the entire industry – and leading DSPs have tried to differentiate themselves by providing buyers with better protection against phony publishers and malicious ads.
2. Supply-Side Platforms
While DSPs serve advertisers looking to buy ad inventory, supply-side platforms (SSPs) serve publishers looking to sell ad inventory to generate ad revenue from their websites or apps.
Publishers use SSPs to sell ad impressions through various external platforms. These may include DSPs, ad exchanges and ad networks. An SSP integrates with all of these technologies, so publishers can manage and fulfill all of their inventory sales through a single interface.
Ad impressions are only a means to an end, though. What publishers really offer is access to audiences. And that requires data.
Through an SSP, a publisher can share a wide range of data with different buyers, including advertisers, ad networks, and DSPs. For example, publishers can share information about the type of content being displayed, or concerning the demographics, location, and purchase behavior of website visitors. Such information helps advertisers segment their audiences and hone their targeting.
Like DSPs, SSPs have faced complaints about low-ad quality, high pricing, and fraudulent ads. These trends have pushed large providers to develop better auditing technologies, more transparent fees, and better industry standards – although these efforts are very much works in progress.
3. Ad Exchanges
An ad exchange is an online system that provides a marketplace for ad buyers and publishers. Such platforms operate a lot like online stock trading platforms. On an ad exchange, publishers auction off the inventory they haven’t already sold to ad networks. Buyers then compete to acquire these unsold ad impressions, often through real-time bidding.
All transactions are highly automated, since it’s extremely difficult for humans to perform the calculations required to trade so many targeted ad impressions in real time. While making their trades, buyers and sellers also exchange data that allows advertisers to segment and target their audience.
Some ad exchanges provide open marketplaces for buyers and sellers. But this model makes it hard for publishers to control what kind of ads their audiences see. That’s a drawback for companies that want to protect their brands.
As a result, publishers are seeking alternatives to open ad exchanges. In some cases, they are using private exchanges that limit entry to select buyers, so they can prescreen who places ads on their properties. Other publishers are turning to a “programmatic direct” model. This method uses automated systems to enable more efficient publisher-to-advertiser sales, without any auctions.
4. Data Management Platforms (DMPs)
Data management platforms (DMPs) enable their users to store, manage, and analyze data about ad campaigns and audiences. Unlike a DSP or SSP, a DMP does not help you buy or sell ad inventory. Instead, a DMP feeds useful data to these other platforms, enabling both marketers and publishers to make more effective decisions.
For marketers, a DMP and a DSP complement each other to make more effective ad purchases. The DMP provides information that helps the DSP manage and direct ad buys. The DSP, in return, sends back valuable campaign performance data. Some DSPs include DMP functionality, creating hybrids that fuse aspects of both systems.
Using a DMP, marketers can create temporary user profiles and target audiences based on demographics, behavior, or other characteristics. As advertising platforms, DMPs are restricted in their ability to use personally identifiable information. As a result, they mainly use anonymous, short-lived data acquired from third-party vendors to build their profiles.
Recent privacy laws have limited the usefulness of platforms that rely on third-party data, forcing DSPs to change how they operate. In particular, the European Union’s General Data Protection Regulation (GDPR) has made it harder to acquire and use certain kinds of data for targeting. Meanwhile, the most advanced DMPs continue to add features and develop new capabilities – for example, the ability to use richer sources of first- and second-party data.
5. Customer Data Platforms
Like a DMP, a customer data platform (CDP) ingests data from other systems and builds profiles that can be used to target audiences. The difference is that a DMP is primarily designed to improve ad targeting for new prospects, while a CDP provides insights into your existing customers’ journeys and informs every aspect of your marketing.
A CDP (such as Arm Treasure Data) consolidates all your customer data and presents a single, actionable view of every individual customer. That enables you to develop more targeted, effective, personalized experiences for audiences on all channels.
Unlike a DMP alone, a CDP helps you build complete profiles of known individuals based on personally identifiable information from any source. An enterprise-level CDP can collect and store unlimited amounts of data from every system that interacts with customers, both online and offline. In addition, it continually maintains and enriches its data, giving you a full history of every customer. The best ones are integrated with more than 100 off-the-shelf integrations with data coming from sources as diverse as CRM and ERP.
With its detailed customer insights, a CDP can enhance the performance of personalized ad campaigns. For example, a CDP can be used to identify the company’s most valuable customers and create a lookalike audience based on their behaviors and attributes. It can then push this information to a DMP to use in making targeted ad buys from SSPs, ad exchanges, and ad networks.
In short, a CDP doesn’t replace other ad tech platforms. Instead, a CDP can activate and orchestrate such systems, improving the reach of your ads and integrating your campaigns with the rest of your marketing. Some companies, such as Subaru, Wish.com, and Kirin, are already using CDPs this way.
As the preceding discussion makes clear, ad tech is growing increasingly diverse, and it is automating much of the drudgery of campaigns, often at a pace faster than humans can hope to operate. Some martech, such as CDPs, is evolving to help produce better control and higher-quality marketing insights. Find out more about your options for increasing efficiency, targeting and insights.
April 9, 2020
Customer Data and CDP Martech: What Does a Unified View of the Customer Really Mean?
By Tom Treanor, Arm Treasure Data
Every marketer wants to know their customers – from online and offline and across multiple social channels and devices. But what does a “unified view” of your customers really look like?
So how do you know when you are seeing the whole picture and not pieces of each customer? A unified customer view will uncover opportunities that used to be missed while also building deeper brand loyalty and increasing purchases and revenue.
Here’s what we believe a true unified view looks like – and how you can get there with your customer data platform.
Customer Data and the Unified View
There are already parts of the customer journey that marketers can see with great clarity: We can track social media traffic to a blog or product page, we can retarget ads to those who have visited the website, and more. Creating a complete, unified view is about filling in the gaps; it’s bringing together data from across the organization, sanitizing, consolidating, and making it useful.
Finding Customer Clones
What do these five people have in common?
In many organizations, these five people appear in different databases in different departments. Marketers might only concern themselves with the first two. The sales department may only be aware of the middle two. Customer service might only know about the last guy.
When you start consolidating data, including offline and siloed data, you can see the truth: these five people are all the same person. Each point of contact was a stop in the customer journey.
But that’s still not the whole view. Let’s add in three more people:
It’s easy to imagine how much more persuasive, relevant, and robust your contact with this customer could be. Your next email drip could include helpful tips for the product they called customer service about. It could suggest accessories for their purchase and fresh content that’s relevant to their interests.
Making Marketing Smarter
Using this data wisely can give your marketing some memory. Think of it this way: You know that feeling when you run into acquaintances and they blank on your name? And you have to say, “It’s Grace… we went to the same middle school… I drove you home after Amber’s birthday party… we have worked in the same building for three years…” And eventually they pretend to remember?
That experience doesn’t make you feel special or valued. But that’s often how marketing treats consumers. We send an email that says, “Hello, VALUED CUSTOMER, would you like some SHOES? Here’s an article on BUYING SHOES.” And they’re thinking, “It’s me, Grace… I bought a pair of shoes last week.”
How much better would it be if your next email said, “Hi, Grace, I hope those new stilettos are working out for you! Here’s an article on how to rainproof the velvet, and a link to a handbag that matches perfectly.”
Instead of an awkward blank stare and a sales pitch, now you’re being helpful, and maybe even initiating a conversation or deepening the relationship.
Consolidating marketing, customer service, and sales data makes it easier to solve problems, inspire loyalty, and drive referrals and repeat business. A complete view of customer data levels up relationships at every stage of the journey:
A Single ‘Golden Profile’ for Every Customer
It starts with seeing the customer as a single, complete, multifaceted person, not a series of unrelated brand contacts. That means not just pulling data from multiple sources, but also combining, sanitizing, and consolidating that data, intelligently using it to fill in your blind spots, so that you arrive at a single “golden profile” of each and every customer. Each profile can then be updated with each purchase, phone call, loyalty program awards, mobile app data, and more.
Outstanding data management is the foundation of a truly unified customer view, and enterprise Customer Data Platforms (CDPs) are designed to help businesses unify, analyze, and activate all of their data.
Want to see what unified customer data looks like in action? Learn how retailer Muji used customer data and an online app to increase in-store revenue by 46 percent.
March 26, 2020
Understanding Customer Data Platforms
By Rohit Srivastav, Netcore
“Customer is indeed the king, but running a kingdom is no child’s play
A marketer’s job isn’t easy. With each passing day, your customers’ expectations are rising, the amount of customer data available to you is increasing, and the challenge to acquire, engage, and retain customers is becoming more and more difficult.
Well, just like every challenge comes with a solution, so does this one. And the core solution lies in knowing your customers well. The more you understand them, the better positioned you are to win their trust and loyalty.
There is no dearth of customer data today. It depends on brands how efficiently they collect the information and leverage it. And with a proliferation of data collection methods, sky is the limit when it comes to uncovering customer needs and decoding what exactly they want.
As per a recent article by McKinsey Global Institute, data-driven organizations are 23 times more likely to acquire customers, 6 times as likely to retain customers, and 19 times as likely to be profitable as a result.
However, leveraging customer data for marketing can sometimes prove to be a double-edged sword – marketers across industries often find themselves struggling to make sense of data when there’s too much of it.
While customer relationship management (CRM) platforms definitely made it easier to manage data, extracting meaningful and readily-usable insights from customer journeys is still a challenge. Now, Customer Data Platforms (CDPs) have emerged that offer promise to fill the gap, not as a replacement for other tools, but to augment their functioning.
What is a Customer Data Platform?
A CDP is a database software that organizes customer data across numerous touch points and interactions to create a unified view of the customer, accessible to other systems and software. A CDP builds a 360-degree picture of customers on an individual level by collecting real-time data from a multitude of sources – CRM, DMP, social media, web forms, email, website, et al.
This single customer view can then be accessed by third-party tools such as marketing automation tools to execute strategies and measure performance. A CDP is primarily meant for marketers like you, who can use the tool with little technical support.
The CDP Edge
Customer Data Management tools have been around for a long time now. When the CRM software was launched in the 90s, organizations found they could manage interactions with current and potential customers with ease, apart from being able to perform data analysis to drive retention. However, its major limitation was that it managed data for registered clients only, using predefined first-party data. The next frontier was the launch of Data Management Platforms (DMPs) in the 2000s, aimed at planning and executing media campaigns. DMPs could segment anonymous data and work with second and third-party data.
The industry soon realized the need for a more sophisticated tool for delivering an improved customer experience through omni-channel strategies. CRM and DMP platforms created data silos that presented a challenge to marketers. CDPs solved this problem by creating a unified view of the customer on a single, comprehensive platform.
What Kind of Customer Data Does a CDP Collect?
The sheer volume of digital data overwhelms traditional database software. A CDP is built to manage customer data from various channels and touch points. Following are the primary kinds of customer data that CDPs collect and organize:
As is evident, CDPs collect a wealth of customer data, most of which depends on a company’s business and industry.
Difference Between CDP & Other Data Management Platforms
While there is some overlap between a CDP, CRM, and DMP, there are clear differences.
Both CDP and CRM collect customer data for sales and marketing activities. However, CRMs focus on intentional customer data and interactions; for instance, a customer’s telephonic interaction with a salesperson. A CRM collects general customer data, not huge data sets from multiple touch points. On the other hand, a CDP focuses on the lifecycle of a customer’s actions. While a CDP collects offline data, a CRM cannot retrieve offline data unless manually entered.
A DMP sorts and analyzes customer and ad data from multiple sources, with the aim to aid you in learning about customer demographics and buying triggers. DMPs focus on anonymous data (devices, cookies, IP addresses), looking at general behaviours rather than customer-specific ones. While CDPs are built for marketing, DMPs are meant for advertising. While DMP data typically emerges from third-parties, CDP data is collected via a company’s internal user base. As a CDP collects more data, it gets more powerful. On the other hand, DMPs store data only for a short period since ad targeting changes quickly and data soon becomes outdated.
How to Use a CDP – Use Cases
Deploying a CDP can help you achieve both high-level goals as well as lower-level ones. Here are some of its most important use cases:
Using CDP to Improve Customer Lifetime Value
Fostering customer loyalty rests on delivering a quality, consistent, and personalized experience. CDPs make this possible at scale, allowing for nurturing loyalty by solving the problem of siloed data. When data is siloed, creating a consistent and omnichannel customer experience is impossible. By unifying customer data, CDPs make it accessible to everyone in an organization, at all times. By gathering first-party data i.e. information directly from customers, CDPs enable the most effective marketing decisions informed by accurate data. In essence, CDPs equip marketers like you with a powerful tool to manage customer relationships accurately and effectively.
Customers today have high expectations of businesses – personalized services, consistent experiences across channels, and tailored recommendations. To be able to deliver the experience a customer is looking for, and keep them coming back to you, investing in an excellent CDP that offer deep insights, is now a necessity, not a good-to-have thing. Are you CDP-ready already?
March 23, 2020
Change Management Strategies for a Successful CDP Initiative
By Ryan Greene, ActionIQ
According to research from McKinsey, almost 50% of digital transformation initiatives fail to achieve their expected value, with only 10% exceeding expectations. If you’re planning to leverage a CDP to help transform your company into a customer-centric organization, then your initiative is no exception to this somewhat daunting statistic.
People and Process are Critical to CDP Success
Fortunately, there’s an almost surefire way to beat these odds. At ActionIQ, we developed and deliver one of the industry’s leading CDPs. But curiously enough, the solution to this challenge doesn’t lie in technology. Rather, it’s dependent on people and process.
Based on our experience in helping deliver CDP-centric transformations at some of the largest and most respected brands in the world (like Michael Kors, The New York Times, Pandora, Saks Fifth Ave, Verizon and more), we’ve learned that the most successful CDP initiatives always lay out a strong change management plan at the outset.
“In fact,” says James Meyers of ActionIQ, who previously served as an analyst and CDP advisor at a leading international research firm, “the vast majority of inquiries I received over the years were from executives who were far more concerned about change than they were about technology.”
(Read more about James’s experiences advising senior executives about customer centricity and CDPs in his blog post Why Your CDP Vendor Must Bring Change Management Expertise.)
The Importance of a Change Management Plan
Why is a change management plan so important? Put simply: changing people’s behavior is difficult. You can run a flawless, buttoned up project to swap out old martech and bring in new systems, but if users don’t adopt the new systems, your project is a failure. And, getting people to adopt new CDP technology is about much more than simply training them to use it.
Here’s why. Your CDP puts your customer at the center of everything you do. It takes today’s multi-step, multi-department processes (like determining which audiences merit prioritization for various campaigns) and enables marketers to operate with100% self-service. It breaks data silos. But for companies and teams that are still organizationally siloed and focused only on traditional metrics – say, aligned around a channel and measured purely by its P&L – customer-centricity is a foreign concept. They don’t have the right org structures, goals, incentives and skill sets in place to realign around the customer. So they fail to maximize the benefit of using the CDP.
“Reflecting on my 20+ years in the field of customer analytics, I’ve come to the realization that one of my biggest challenges has always been getting my business counterparts in the organization to look at a customer holistically,” says Tamara Gruzbarg, head of ActionIQ’s change management practice. “Very few are ready to accept the fact that the customer couldn’t care less who owns the P&L, or whether she is considered a ‘store customer’ or an ‘online customer.’ She wants an experience that makes sense for her. And if it doesn’t, she will leave.”
(Get more organizational change insights from Tamara in her blog post CDP Change Management: Change Comes from Within.)
Setting Up Your CDP Transformation for Success
Without a well-constructed change management plan – backed by the full support of your company’s top executives – the inertia of the status quo will win. Users will stick to the old systems and processes, and your customer-centric vision will remain unrealized.
So what goes into a well-constructed change management plan? It starts with the realization that people and processes require equal weight to technology when designing your transformation.
“In the early days of ActionIQ, our most forward-thinking clients began to ask us for help with change management,” says ActionIQ’s Ryan Greene. “Back then, we assisted in more of an organic ‘roll up your sleeves and get it done’ kind of way. But over the years we’ve developed repeatable best practices that are now a standard part of ensuring the success of every client implementation.”
(Read more about the best practices that make up ActionIQ’s change management approach in Ryan Greene’s blog post Planning for CDP Change Management: A Proven Approach.)
Here are three foundational change management elements that will get your CDP initiative started off on the right foot:
Lean on Experts for CDP Change Management Guidance
Of course, this is a very high-level framework. Mapping these practices to your specific organization takes thoughtful planning, diligent execution and a lot of hard work. Just like you lean on partners to provide and implement technology, you can also lean on partners to help with the people and process aspects of your CDP initiative.
When looking for a partner, you should only trust people who have deep experience as marketing, analytics and data practitioners, who have a track record of implementing organizational change, and who can help you mitigate risks before you encounter them.
If you’re interested in more detailed change management frameworks, including:
then be sure to download our CDP Change Management Best Practices ebook now.
Have any change management war stories, or just want to get in touch? I’d love to hear from you. Contact me at email@example.com.
March 19, 2020
Customer Data Platforms vs. Data Management Platforms: A Definitive Guide
By Rohit Srivastav, Netcore
Modern brands share a relationship with their customers vastly different from the ones that traditional organizations did. If one were to outline each interaction (both physical and digital) between a brand and its customers, we’d find an extensive map with multiple touch points. With these numerous contact points comes the dilemma of delivering a seamless transition between different channels and a consistent, unified messaging throughout. Though brands have been relying on Data Management Platforms (DMPs) for achieving this goal, the emergence of Customer Data Platforms (CDPs) has presented a compelling alternative to marketers. By the looks of it, the two may seem similar but they hold major differences. However, one cannot necessarily replace the other.
This guide will help you understand each of these platforms and the value they deliver.
What are Customer Data Platforms?
The CDP market is growing rapidly. According to a study conducted by the CDP Institute, the CDP market size grew by more than 50% during the year 2018 alone, to a net worth of around $740 million. And the growth is not merely limited to numbers. A study conducted by Forbes Insights involving 400 marketing leaders found out that 44% of them believed in the power of CDPs in driving customer loyalty. So, what exactly are Customer Data Platforms?
Say, you have been given a 500-piece puzzle which you easily solve. To your surprise, you’re told that it is simply part of a bigger puzzle and you’re given 200 pieces more as a result. You carefully finish that as well though it is relatively time-consuming. But, before you could take a deep sigh of relief, you’re asked to expand the puzzle further with 500 more pieces!
Well, let’s see how a Customer Data Platform (CDP) fits into this puzzle analogy. Consider each piece of the puzzle to be a data point about an individual customer and your “understanding” of this person keeps expanding in varying degrees (with every new puzzle piece). Now, the final puzzle will represent the unified profile for that customer and the board/area on which you’re building the puzzle will depict the CDP database.
In technical terms, a Customer Data Platform or CDP is a marketing system or software that creates a unified and steady customer database accessible to other systems. The purpose of a CDP is to gather all customer data and to stitch them in order to create unified customer profiles that can be used for marketing campaigns and customer service initiatives.
What are Data Management Platforms?
The Data Management Platform (DMP) market size is expected to have a net worth of whopping $3 billion by the year 2023 with a Compound Annual Growth Rate (CAGR) of 15% between the years 2017-2023. Let’s take a slightly different approach to understand the platform with such impressive prospects. Consider the example of a dealer or merchant whose employees wear blindfolds and earplugs full-time. As a result, they are unable to see or hear their customers. All they do throughout the day is punctually stock the shelves with products and take care of the register. There’s no contact whatsoever with the customers. In fact, they don’t even know who they are! Hence, naturally they are clueless regarding their customers’ preferences and likes/dislikes. And after a “good” day’s work, when they sit down to tally the register, all they can figure out is the number of goods sold and at what cost. They have no clue of who bought what and why (was it for self-use or maybe as a gift?). And the worst part is not oblivion but indifference – the dealer doesn’t know and he couldn’t care less. This is exactly what a marketer or business professional that does not use a Data Management Platform (DMP) looks like.
A DMP is more or less like a data warehouse – it is a system or software that gathers, analyzes, stores, and delivers useful customer information to marketers, web publishers, and businesses. The data managed by DMPs is used to generate audience segments which are in turn used to target specific users in online marketing campaigns.
Aren’t they the same?
Well, they do sound the same and a superficial understanding can even give one the impression that they indeed are the same but nothing could be further from the truth. CDPs and DMPs differ on several grounds:
Do they have anything in Common?
Well, yes! Now that we’ve looked into the differences between CDPs and DMPs, let’s take a look at the several similarities:
CDP or DMP: Which one should you choose?
Since CDPs help in engaging and improving relations with existing customers, marketing departments of individual companies will find them the most profitable to manage customer data and to generate personalized messages via different marketing channels.
DMPs are your best bet if you need to manage and process large sets of audience data and wish to extend your target group to external databases. DMPs prove to be the most profitable to web publishers, marketing agencies, media houses, etc.
Furthermore, as mentioned earlier, CDPs and DMPs do not necessarily replace one another but are complementary in nature. Meaning, data collected by CDPs can be enriched for better segmentation using DMPs and CDP data can help in creating better lookalike audience segments when used within DMPs. Hence, depending upon your marketing needs, choose either one or both of these valuable platforms.
March 16, 2020
Data Privacy Has a Day – And Companies Better Pay Attention
By Lisa Loftis, SAS
Data Privacy Day, occurring every January, is an international effort to raise awareness and promote data privacy and protection best practices. It originated in Europe in 2007 and was adopted by the US several years later. While searching for quotes on data privacy to honor the day, I came upon an eye-opener from 2009 by former Google CEO Eric Schmidt:
“If you have something that you don’t want anyone to know, maybe you shouldn’t be doing it in the first place.”
To be fair, the quote was in response to a conversation about how tech companies share information with authorities, but the context was that the amount of information said companies really know about consumers would “shock” and “confuse” them. We really have come a long way on data privacy - or maybe not.
The largest fine levied under the GDPR so far, $57 million, came shortly before last year’s Data Privacy Day, and was given to Google for not properly disclosing to users how data is collected across its services — including Google Search, Google Maps and YouTube. The regulators claimed that Google did not meet the requirement of obtaining clear consent and that consumers are largely unaware of the data collected and shared by Google. Note that Google disputes the claims.
Unfortunately, I think I know what the regulators mean. Early last year, a Google screen popped up on my phone asking me to rate places and businesses including a law firm, a retail store and a national park. These were all places I had visited recently. It turns out that every location I had physically been to in the last several months with my cell phone in tow - which is almost everywhere I went - had been tracked, stored and visible to me and who knows who else. I certainly never knowingly gave explicit permission for them to track my physical location. Even worse, rescinding this permission was an arduous and non-intuitive process that involved navigation across six different screens.
This is the antithesis of clear and unambiguous consent. I don’t mean to pick on Google here – because in our data-driven world this type of tracking is the rule rather than the exception. We must change our thinking on this. Both consumers and legislators are demanding it.
Consumer Expectations are Significant
While my informal poll of non-tech working consumers indicates that most are not aware of International Data Privacy Day, they do have definite expectations around data privacy.
A recent survey of global consumers, CX2030, illustrates how focused consumers are on the issue. CX2030 did not focus on privacy specifically, but instead was designed to predict what customer experience would look like into the future. Privacy, specifically trust, came up as one of the pillars that companies will be increasingly compelled to deal with. Unfortunately, the overriding sentiment from consumers was one of concern:
When consumers use phrases like “out of control” and “hiding bad things”, companies had better sit up and take notice.
Legislators are Paying Attention As Well
Consumers are not the only ones paying attention.
In another European privacy enforcement action last year, German antitrust regulators ordered Facebook to seek users’ explicit consent to combine non-Facebook data from Instagram, WhatsApp and various 3rd party websites into a comprehensive social media profile. Facebook must submit compliance proposals or face significant fines of up to $5 billion. Facebook plans to appeal, however, the top antitrust regulator for the EU has indicated that it is watching this case.
Facebook is also facing numerous lawsuits over data misuse and ad targeting including one brought by Washington, D.C., Attorney General Karl Racine, accusing the social media giant of wide-ranging privacy violations. They are also under investigation by the FTC to determine if they violated a 2011 FTC consent decree requiring them to give consumers clear and prominent notice of how information is collected and used and to obtain consumers’ express consent before sharing information beyond established privacy settings.
Both Google and Facebook have been sued multiple times for violating the Children’s Online Privacy Protection Act which imposes requirements on companies on collecting data on children under 13 years of age. Moreover, the City of Los Angeles has sued the IBM subsidiary, The Weather Channel, for “covertly mining the private data of users and selling the information to third parties, including advertisers.”
A battle is also brewing in the US over state and federal privacy laws. Several states have passed laws aimed at data privacy and ethical use. The most prominent and restrictive of these is the California Consumer Privacy Act of 2018 - taking effect now and billed to be the toughest data privacy law in the country (incorporating many GDPR-like restrictions). Silicon Valley has lobbied hard against this and other state bills, pushing for less restrictive measures and asking that a uniform federal law supersede all state legislation. To this end, both the US Chamber of Commerce and the Internet Association, which represents companies like Amazon, Facebook, Google, and Twitter, have released their own recommendations for a federal bill. The Data Care Act introduced by a group of US senators, a competing congressional bill, The Information Transparency and Personal Data Control Act, and the White House Administration’s recommendations round out the plethora of proposals.
Regardless of where we end up in terms of data privacy regulations – several things are clear. The privacy mandate is expanding. Consumers expectations are increasing. And there will be regulation here in the US as well as in Europe. If you don’t keep up, there will be consequences.
March 12, 2020
Augmented Analytics To Transform Big Data Into Smart Data
By Rohit Srivastav, Netcore
It was in 2005 that Roger Mougalas coined the term Big Data. Ever since, it has captured the imagination of industries across the spectrum, across the globe. After more than decade, the world is now staring at the next frontier in data – Augmented Analytics.
It was back in 2017 Gartner predicted Augmented Analytics to be the future of data, and in 2019, it already is the number 1 trend in data analytics. As per a research published by Allied Market Research this year, the global market for Augmented Analytics will reach USD 29.86 billion by 2025.
Data On Its Own Holds No Value
Data-driven insights are no longer just good-to-have, they are crucial for staying ahead of the curve. Most future-ready organizations today have embraced data analytics to deepen their understanding of customers and drive bottom-line growth. The problem is, their ability to leverage the power of data is severely limited, leading to a failure of data analytics projects. According to Gartner estimates, an incredible 60% of big data projects fail!
Data, by itself, holds no value for a business.
Say, a company’s data reveals that sales figures are dropping by 5% every month. But what does that really mean? This decline could be attributed to a failure of advertising methods, industry trends, or something entirely different. There is no way to figure the cause out unless you take a deep dive into the issue to uncover the real reason behind the decline in sales. For instance, you may come to the realization that your paid ads are less effective and need a different approach. Now you have an actionable insight that tells you exactly what to do.
The lesson – you need actionable insights, not simply informative data.
A data analytics project involves a number of processes – data aggregation, extraction, cleansing, pattern analysis, insights generation, to name a few. While the process itself isn’t so complicated, the tricky part is generating the right insights. Because data scientists are in short supply, other than being an expensive resource, companies need an advanced yet affordable tool for analysis at scale.
By harnessing the power of AI and ML, Augmented Analytics offers freedom from the tedious process of processing, aggregating, and visualizing data. Naturally, it is the next big disruptor in the world of business intelligence.
What is Augmented Analytics?
Augmented Analytics (AA) presents a novel solution to businesses to make sense of swathes of chaotic data. Augmented Analytics combines Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) to automate the process of insight extraction from data.
The AA-driven tools organize, manage, filter, and analyze datasets to produce actionable insights, speeding the process of turning data into digestible information. Owing to the reduced manual involvement and dependence, businesses can rapidly analyze data at scale, and easily obtain patterns and trends.
Augmented Analytics reduces an organization’s dependence on data scientists and other manual processes by automating this crucial process with little to no supervision from a technical expert. Essentially, it cuts down on the human intervention part, weeding out less relevant insights automatically. Thus, the risk of missing important insights or making errors is vastly reduced, resulting in a streamlined and reliable data analytics process.
Augmented Analytics is set to create a new standard for business growth as organizations consume and generate massive streams of data from multiple sources but face challenges in making the data readily usable. Let’s see how.
How Does Augmented Analytics Work?
A business’ engine needs data to fuel growth. By automating a crucial part of the insight generation process, Augmented Analytics fuels this engine at an accelerated rate. When repetitive data cleaning and organization tasks are automated, data scientists will have more time on their hands for strategic analysis and decision-making. Additionally, this shrinks the scope for human error.
Smart data, fueled by Augmented Analytics, brings together the whole picture. When an organization’s data is siloed i.e. distributed across several different platforms, it presents a hurdle in smart decision making. To solve problems and identify areas for improvement, the decision makers must be able to view how the engine works on a whole, not how different parts work separately. By integrating data points into a unified system, decision makers and CMOs can track the entire picture on one platform.
In a paper published this year, Gartner outlines the different facets of augmented analytics:
Benefits of Augmented Analytics
Augmented analytics takes the benefits of business intelligence to the next level, with unprecedented efficiency and accuracy:
The automated insights generated by augmented analytics can thus be leveraged to assess business performance, identify growth pockets, and understand how a brand compares to the marketplace, thus contributing to a solid business strategy.
Ultimately, this results in cutting-edge insights driven by algorithms that would otherwise demand a huge investment of time and energy. This means data is democratized so that data scientists aren’t the only people in an organization that can make sense of the results.
Democratizing Data Analytics for all Stakeholders
Augmented analytics is fast becoming a popular data analytics tool, one that doesn’t need the involvement of data scientists, effectively collapsing the wall between asking questions and getting the right answers. One of the biggest advantages of embracing augmented analytics is the democratization of data.
Data scientists and analysts enjoy freedom from repetitive and low-value tasks like running routine reports. Instead, they can focus on solving complex queries and data science projects, offering critical business insights to the relevant stakeholders.
For small companies that don’t have the resources to build a team of expensive data scientists, augmented analytics will infuse accessibility and affordability into data-driven insights.
A bigger advantage lies for marketers like you. Augmented analytics is set to change how you make sense of customer data on a daily basis. Unlike earlier you don’t have to rely on an analytics team for in-depth research and reporting, a dependency that made your work time consuming and inefficient. With augmented analytics tools, you can regain control and track the entire customer journey, right from acquisition metrics to retention insights.
With augmented analytics, everyone in the organisation will hold the power to make informed and data-driven decisions, without having to depend on data scientists to furnish the required information. Naturally, this opens the doors for businesses to accelerate their growth at an exponential rate.
How can Marketers Benefit from Augmented Analytics?
Augmented analytics reduces the gap that existed between data scientists and other business users. The benefits of advanced data analytics are now available to every employee in every department, including marketing.
Businesses of all sizes have something to gain
Traditional data analytics platforms come with a major disadvantage. Before one can even get down to uncovering patterns, a great amount of manual labor is involved. While a business may onboard a data scientist to uncover insights, it may soon find that they spend most of their time cleaning and harmonizing data, not extracting insights from it.
Augmented analytics is on its way to transform the way businesses analyze data. Marketers, in particular, have much to gain. They can finally regain control of massive sets of data and meet customer expectations with personalized communication and experiences.
Ultimately, this can have a significant impact on the business’ bottom-line. By automating large-scale analysis and allowing marketers to generate insights, Augmented Analytics is paving the way for a more productive business landscape.
It is important that modern businesses understand the benefits of augmented analytics – speed, democratization, and insights. Armed with these, businesses are better equipped to anticipate what customers want, improve business processes, and lay the groundwork for success.
The Road Ahead For Augmented Analytics
In the present landscape, businesses are producing such a large volume of data that it has become impossible for data scientists to explore it on their own. Manual data exploration always runs the risk of missing key insights.
With augmented analytics, organizations have a tool to explore all possible hypotheses from the collected data and automate a great deal of data science tasks. When data scientists and augmented analytics work together, data insights will become democratized i.e. become available to a wide pool of business users.
There is no doubt that augmented analytics is here to set a new standard for business growth. The quicker you leverage this technology, the faster you will reap its benefits and be able to exploit growth opportunities.
March 9, 2020
How to Pick the Right Customer Data Platform (Learn from CDP Experts)
By Jordan Torpy, Exponea
You’ve decided a Customer Data Platform is the right tool for your company. It’s a good choice: the market has matured and there are many vendors to choose from.
But that also means…there are many vendors to choose from. And of course you want to choose the right CDP for your company. It’s a tough decision, and the wrong choice can yield poor results and create an impression that CDPs are all hype.
But the right choice will create new possibilities for your company, a better experience for your customers, and improved cross-departmental collaboration.
To help us navigate this topic we talked to some of the top CDP experts and picked their brains to help you with the processes of choosing and implementing a CDP.
They’ll discuss two important things:
Let’s introduce our expert panel. It’s a group with decades of combined experience in making CDP projects successful.
Hugh Kimber – Global Sales VP
Hugh leads a global sales team and has over 18 years of experience in the digital marketing industry. He understands how a company can make new technologies successful.
Daniel Viglas – Solutions Manager
Daniel helps clients get the most out of their chosen CDP. He identifies where a CDP brings value and creates solutions that bring fast results.
Bruno Gorgulho – Head of Solutions
As the Head of Solutions at a major CDP company, Bruno leads a team that helps bridge the gap between project initialization and implementation. His experience allows him to speak with confidence about what makes a project work.
Dale Farrey – Senior Sales Manager
Dale uses his years of experience in SaaS sales to ensure that companies have a successful CDP implementation, and he’s sharing that knowledge with us here.
Step One: the Purchase
The first step in the process may seem overwhelming: choosing the right vendor. How should you proceed? What should you look for?
Below, the experts share what your first steps should be after deciding to go with a CDP.
Question #1: How would you proceed if you were the one (CEO, CMO, etc.) in charge of getting a new CDP?
Dale Farrey, Senior Sales Manager
First I’d ask my team to provide insights as to why they believe they need a CDP. I’d then turn those insights into a brief with 5-10 key deliverables for the CDP, which would form the basis of the review criteria I’d use during the selection process.
Bruno Gorgulho, Head of Solutions
I would first validate the key value proposition of a CDP and make sure it aligns with the business problem I’m solving. If I truly require a system that can centralize customer data for a B2C business, provide analytics-driven insights, and then send that intelligence to other systems to trigger campaigns, then I need a CDP.
Then I would map existing internal capabilities - which systems I have and why. Then I could see which systems I could eliminate entirely, which could be replaced, and which are critical for my business. I would have to build a system analysis independent of my existing teams, since new technology is likely to disrupt the way my teams are organized.
Once the business needs, teams, and systems have been mapped I could start talking to vendors who could also help shape the requirements.
Now that you know exactly how you’ll use a CDP, it’s time to find the right one. With a larger and larger pool of vendors, how do you narrow down your choices? We asked the experts to weigh in.
Question #2: How would you evaluate different CDP vendors? What are some of the indicators that tell you a vendor is right for you?
Bruno Gorgulho, Head of Solutions
I’d want an answer for the following:
Hugh Kimber, Global VP Sales
I’d want to know about their customer base — do they work with companies like mine? What kind of experience do they have? I’d want to see evidence of businesses growing thanks to their technology (and not just 10% increase in conversions, but how have they helped a business over a period of 12+ months).
What’s the cost of change: how easy is the implementation? Will it make my company more efficient?
Daniel Viglas, Solutions Manager
I need to know if the company can create a single customer view. Then I need to look at my short- and long-term goals, and compare them to what the vendor offers. These are the areas you might consider when thinking about CDP relevancy:
Customer data platforms are still new, and how we define a CDP continues to shift. This leads to some misunderstandings about CDPs and just what they can do for you.
What are some of these misunderstandings? Our panel outlines several below to help you with your selection process.
Question #3: What are the most common misunderstandings about CDPs out there?
Dale Farrey, Senior Sales Manager
Within the early stages of the sales process, the CDP is commonly brushed off as having the same functionality as a marketing automation platform or marketing cloud, but it’s actually much more. The customer is typically fully educated by the end of the second meeting.
Bruno Gorgulho, Head of Solutions
A common mistake is when people expect the CDP to provide some of its own data (which is usually the scope of a DMP or similar platform – a CDP works with a company’s data). Also, some companies expect the CDP to be able to resolve customer identity, but their existing processes to contact the customer are completely unstructured - remember that the CDP will only read the real-life events between your company and your customer.
Question #4: What would your advice be to anyone buying a new CDP?
Daniel Viglas, Solutions Manager
Think about what you want to achieve with a CDP, short-term and long-term. Maybe in the short-term I want to improve my emailing with behavioral data, and that may give me an answer about which CDP can satisfy that. Maybe my long-term play is something else, and that narrows down my options even more.
Hugh Kimber, Global VP Sales
Make sure the CDP you are choosing is future-proofed. A single customer view is an immediate requirement, but you need a CDP that can act on the data or integrate with existing suppliers you wish to keep.
Question #5: What else can help companies choose the right CDP?
Daniel Viglas, Solutions Manager
I would ask myself how a CDP could benefit my company from these perspectives:
Dale Farrey, Senior Sales Manager
Take into account the history of the provider. Some companies positioning themselves as a CDP are built on a flat file database because 95% of their customers use them for email only...they were built that way historically. Consider a =more modern CDP approach built in the era of the CDP SCV (Single Customer View).
Hugh Kimber, Global VP Sales
Clearer differences between CDPs; some push “AI everything!”, some push execution. Look for a deeper explanation of different CDPs and what they are good for. Who is a ‘jack of all trades, master of none’ and who is a specialist and why?
Step two: After the Purchase
The post-purchase phase is crucial to making a CDP work successfully for your company. While every company is different, most successful CDP implementations follow a similar pattern. We spoke with two more experts to find out what that pattern is.
Expert 1: Adam Lebeda – Global Head of Partnerships
Adam currently works to help find valuable opportunities for businesses looking for advanced marketing platforms. Previously, Adam was the Senior Manager of Digital Services at T-Mobile Czech Republic, where he successfully implemented a modern CDP.
According to Adam, there are three key points that lead to a successful implementation of a customer data platform:
It’s unrealistic to think you can buy a platform and immediately change all the processes of your business, especially with a larger company or stakeholders involved. An effective strategy is to start small with quick time-to-value use cases. This proves the value of the tool to the people who work with it and leads to buy-in throughout the company.
Create a habit
When the people who work with it become internal advocates for the platform, their enthusiasm catches on and helps to make the new CDP a habit. Then it’s much easier to onboard the rest of the organization.
Use a tool people love to use
For all of this to fall into place, the CDP must be a tool marketers really enjoy using. A good CDP gives marketers the ability to execute their own ideas, without needing to overly rely on the assistance of more technical employees. This is what led to a successful implementation at T-Mobile, for example.
Expert 2: Peter Solnicka – Client Success Manager Lead
Peter leads a team of CDP Client Success Managers. His experience implementing CDPs at multiple companies has given him great insights into what leads to the success of a project. He tells us what he believes are the most crucial aspects of the implementation process:
Know who the key stakeholders of the project will be, both internally and on the side of the vendor. Implementing a CDP requires buy-in from multiple roles: IT people, data analysts, CRM owners, digital and campaign teams, project managers, and more.
All these internal stakeholders need to have a clear counterpart on the vendor’s side. For the project to succeed, alignment across all of these teams is a must.
Mindset and attitude
Implementing a CDP will require your team members to embrace changes. Processes will look different and routines will be upended; don’t expect things to stay static. This will be a time to discover new capabilities that weren’t possible with your previous solution. This might require a mindset and attitude shift in your organization.
A good vendor
A good vendor will not blindly agree to everything you request. They should be willing to challenge you in a beneficial way, out of respect and a desire for your growth. They will be more a partner, focused on reaching mutually set goals and delivering value.
Finally, be sure that your vendor is future proof. Study their product roadmap and ask, will they accommodate my needs in the future? You don’t want to get locked in to a contract with an outdated solution.
If you can establish good starting logistics, have the right mindset, and work with a good vendor, your odds of success are significantly higher.
The processes of choosing and implementing a CDP can both be daunting, but there are things you can do to make them easier and give yourself a higher chance of success. Interviews with the above experts pointed to several similar steps:
Taking on a CDP project is big. But with the right preparation, success is much more likely. Set yourself up for a positive experience from the very beginning, and take advantage of all the capabilities a modern CDP has to offer.
March 2, 2020
How will enterprises drive effective engagement during these times of cookie extinction?
By Ricardo Rengifo, FirstHive
Webkit browser engine which powers Apple Safari and all IOS browser has released its latest update to Intelligent Tracking Prevention (ITR) feature – ITP 2.3. It follows in the footsteps of previous releases beginning from 2017 which aims to provide the users with a higher degree of privacy by restricting how Ad tech companies track users online and the duration of time data can be held for a user, and is in line with the general direction of a cookie-less future in the industry.
A quick look at changes brought by ITP so far:
Now, let’s look at how these changes are disrupting today’s marketing ecosystem. If your organization is leveraging any of the below systems or technology, you will have to take a relook at your current marketing operations.
One open question that is asked in the marketing universe is where to slot CDP in midst of these changing privacy landscape, is it a beneficiary of the changes or is it one of the systems that is bound to be negatively affected by it.
In this context let’s look at how a CDP works; put simply CDP builds unified customers views based on their interactions across multitudes of source systems and interfaces deployed by the organization and makes it available for analysis and action. CDP breaks down data silos within the organization so that every system connected to it starts seeing the customer, their preferences and behavior consistently; helping to craft consistent experience across each touch point.
Organizations are now re-evaluating their entire marketing stack to identify the systems impacted most by these changes and evaluating ways to ensure they are still able to market effectively to their customer base. Organizations are coming to be increasingly dependent on CDPs and its capabilities to archive the same.
The upshot is that even though CDP systems started off with the objective of solving the marketer’s problem with even increasing touchpoints, it has evolved into a system of intelligence for the whole organization. In this light, the recent changes to ITP will act as a constant force of evolution on CDPs but will not take away from it what it really does well and this is why the CDP segment continues to grow and is now over a US$ 10 billion market.
We are always interest in comments from the industry, so please let us know what you think here, or drop us a note at firstname.lastname@example.org
February 25, 2020
Why you should take a leaf out of HSBC’s book and remember your email system is ‘not an island’
By Anthony Botibol, BlueVenn
There’s a lot of hype these days about omnichannel marketing and the importance of crafting a consistent customer experience across every platform. The fact is, though, that all channels are NOT equal when it comes to customer engagement. According to industry analysts Gartner, an Adobe 2018 Consumer Email Survey found that 50% of consumers named email marketing as their preferred brand communications platform, compared with a maximum of 20% who said they preferred any other channel.
So, to maximize sales it is important to get your email targeting right – especially since Gartner’s own 2018 State of Personalization report found that, while 86% of consumers surveyed were fans of personalized communications, 48% said that they would unsubscribe from irrelevant or annoying emails. Meanwhile, the same survey found that brands saw a 20% increase in commercial benefits when customers perceived their communications to be helpful.
In other words, just addressing your marketing email to ‘Mary Smith’ won’t cut it anymore. These days, Mary will expect you to send her personalized and relevant content, carefully tailored to match her interests and speed up her path along your brand journey.
The challenges of personalization
However, that’s not as easy as Mary might think. Why? Because Email Marketing Systems are, by nature, siloed. Everything they know about the customer comes from the one platform, and remains within that one platform. This means that marketers are able to determine which emails a customer has opened, what time they tend to read their communications and which links they have interacted with, but won’t be able to share that information with their other platforms to help enrich the engagement with that customer across the journey.
Nor will they be able to tell that the customer has just bought the advertised product, which might suggest that further email communications should be either suppressed, to stop the customer receiving ads or offers for the item they have just bought, or changed to promote different products.
As well, a lot of information might be held about the customer on other platforms, such as their last purchase, what they’ve looked at on the website, products they’ve liked or commented on in your social media, and what they’ve swiped their loyalty card for in store. However, if you’re to use that information to help you select an audience for an email campaign or personalize your email creatives, you’ll need to leave the email platform, collect together the necessary insights, and then manually insert them into your emails on re-entering the platform. If interesting results come in from your email campaign, you’ll have to perform the same process in reverse to share them with other platforms.
What you need is an email marketing channel powered by a Customer Data Platform (CDP) that integrates your email marketing with your other channels, and is therefore able to utilize all the customer data, transactions and interactions from across the organization - not just the data residing in the email platform itself.
The benefits of CDP-powered email marketing
A shared databank
Being able to see everything your company knows about the customer – their demographics, location, preferences and past transactions/interaction history – in a CDP will make it much easier to understand what your customer actually wants from your brand and how you can give it to them. Having every byte of data about your customers available for the segmentation and personalization of an email ensures that your communications will stand a better chance of reaching the right person with the right information, whilst also bypassing the arduous or expensive process of data wrangling to move data between systems.
Actioning your insights
With CDP-powered Email Marketing you’ll be able to better segment your customers into suitable audiences to receive bespoke campaigns. Emails designed to engage women aged 30-50, who are residents of Portland, Oregon and have a propensity to respond to discount offers by email, for example, will be sent only to people within that niche bracket, resulting in incredible open and conversion rates. Better still, that same email can be highly personalized en masse, using dynamic content driven by all the data variables from across your business, to ensure that each recipient gets content relevant to them or will be sent their promotion via an alternative channel if they have repeatedly ignored your past emails.
Boosting sales with real-time interactions
It’s not just about how you sell the right product, it’s about when you sell it too. If a customer is on your website looking at perfumes, then providing them with a website pop-up or nudge to use an exclusive discount code is proven to be a great real-time personalization tactic. However, if they ignore the pop-up, or perhaps don’t see if for whatever reason, now might be the time to send them a discount code by email. After all, the margin on such products is huge and a bottle can last for several months, perhaps even a year, so it pays to strike before they have a chance to browse for competing products and retailers. And, if someone has just bought a new bike in your store, they might genuinely appreciate an email containing advice on how to maintain the bike, along with recommendations of associated products they can purchase to help them do so.
Similarly, if an online customer abandons the items in their cart, knowing that immediately will give you a chance to trigger an email to jog their memory, or send them a small survey to determine what their concerns might be (such as price, slow checkout or a lack of product information), so that you can attempt to allay these or identify issues with your website.
Continually improving your omnichannel approach
With CDP-powered email marketing, data can be utilized from any system and shared with any system too. That means you’re not only able to glean information from your CMS, SMS or social media channels to enrich your emails, but have the freedom to feed back the insights gained through your email marketing campaigns. As a result, it won’t just be your emails that will hit the spot with your target audience more consistently, all your marketing efforts will become more refined.
Together, your marketing efforts will thrive
Just as HSBC argues that Britain’s international character makes it stronger, so achieving a two-way flow of information between all of your Martech systems and platforms will enrich every campaign you run.
A CDP-powered email marketing channel will overcome all of the challenges of personalization by ensuring that your email campaigns are powered by data insights gleaned from a 360 degree Single Customer View, so that customers will only receive emails that are relevant to them. It will enable an offline purchase to trigger an online action, it will mean a complaint or call center interaction can suppress the request for a product review, and it will ensure that the 80% of your audience who don’t interact with your email are automatically selected be marketed to via another channel next time, to maximize the impact of a multi-channel campaign.
To find out more about how better data insights will help you to get more opens, clicks and conversions than ever before, read about what our new email module has to offer now.
Discover how a CDP could be your route to real-time, cross-channel marketing with our live demonstration.
Join us at 10am EST / 3pm GMT on February 27, 2020 when we will cover:
February 17, 2020
Third Party Data Is Not Dead Because of Customer Data Platforms. Here’s Why
By Jenna Devinney, Webbula
Over the past few years, the industry buzz has been about the rise of Customer Data Platforms (CDPs) and how they’ve extended the value of first-party data, while third-party data took another bad rap.
In short, we aim to prove that while first-party data certainly has its benefits, relying on it alone will only get you so far.
Think about how often your data goes bad? Every month about 3% of customer data becomes obsolete due to changing conditions. Customers move, get married, change names, or pass away. Whatever the reason, this fact is why marketers are always in need of updated and accurate customer data.
Let’s go through the facts on why we need third-party data more than ever.
1) Fraud lives in first-party data
You may think that your own data is the cleanest, safest data you can have, but the truth is your first-party data will show its true colors.
Ask yourself how often an individual misrepresents their identity or gives you false information when filling out forms, or surveys when applying for that discount, free sample, or any other special offers. You receive that data from the consumer, but that doesn’t necessarily mean it’s correct.
Even when it comes to a simple email address, there are many ways for fraudulent data to end up in your email marketing lists such as:
Disposable Domains: These are temporary emails that WILL deliver to the inbox. An example can be a shared public email account with no passwords for different people to use for a multitude of reasons. These emails are used for marketing purposes and are a waste of resources if you send to them.
Bots: Programs designed to locate signup forms on the web and submit fake email addresses and other information. They are purely malicious and mess up your metrics, and they provide no real usable information. As such they are not only pointless to market to but can also be dangerous.
Moles: Are fake emails that report campaign stats to real-time blacklists.
Seeded Trackers: Are addresses used in marketing campaigns to track delivery rates. They are there for a simple reason of someone tracking your activity, so if you send to them, they won’t help your open or click-through rates, because they aren’t real.
2) You Need More Data
Automotive dealerships are receiving leads every day, but they aren’t exactly sure which leads are qualified or not when measuring with their own metrics. The good news is they do have a good understanding of the target audience, but when a lead comes in there are two potential outcomes, is the consumer browsing or are they a potential buyer?
Another industry that has obstacles with first-party data is the Consumer-Packaged Goods (CPG) Industry. If you’re familiar with the industry most CPGs don’t have the rights to the consumer data, the retailers do.
We live in a world where data is what is keeping personalized shopping experiences moving, and CPGs will need to find ways to stay on top of this issue and not get left behind. Ultimately, they need more consumer insights to move forward.
If these industries want to know their customers, they need to create a wider view of their customers and not just rely on their own data.
How do they do this?
Third-Party Data Wins Thanks to CDPs
CDPs’ appeal to marketers is their ability to collect and connect first-party data from multiple sources across the organization.
What they often lack is keeping your data up-to-date, and this is where certain CDPs and organizations considering a CDP purchase partner with third-party data providers to help clean and add additional insights to their customers’ data such as purchase history, demographics, interests and much more.
There isn’t anything bad about third-party data providers, the data simply is just someone else’s area of the customer picture. You’re always going to have those low-quality data providers who tarnish the industry reputation that you must watch out for.
Everyone wants to work with trusted, compliant data quality providers. We understand the fear, but you’re never going to run successful campaigns if you’re not working together to capture the full picture of your customers and prospects.
Third-party isn’t dying, it’s only getting better. We forecast there will be more CDPs and CDP customers in the future willing to work with third-party data providers to help enhance data.
See www.webbula.com to learn more.
February 13, 2020
How Unified Comms Can Improve Business Productivity
By Stefan Docherty, Call & Contact Centre Expo
Unified Communications refers to the integration of various real-time communication channels such as voice, video, text and data into one solution, which aims to boost communication and collaboration within a business. Unified Comms come in many forms, including the integration of a company’s customer communication channels (i.e. email, social media, web and chat) or simply the pairing of various internal channels such video conferencing and instant messaging (i.e. Skype for business).
Improving productivity is a challenge all businesses face, and Unified Communications proposes various ways it can address this challenge. A study found that businesses adopting UC experienced on average a 52% improvement in workplace productivity as well as a subsequent 25% increase in profit. Sounds promising, but how does UC help achieve this?
Firstly, Unified Comms can significantly add speed, and therefore productivity to business processes. For example, in contact centres, UC can help by improving customer response times. With all of an organisation’s communication channels merged onto one platform, (i.e. social, email, web, chat, telephone, SMS) it becomes much easier to pick up these queries/conversations as well as respond to them. For sales teams, unified comms can be vital as it means each team is alerted of queries much quicker and so have a better chance of converting each ticket coming in. With the help of UC, staff across various departments now address more queries during their working hours, thus boosting productivity.
Secondly, UC can have a positive impact on workplace collaboration and communication. Presence apps let you know in real-time if your colleagues are available for meetings, calls/call transfers, or collaboration on projects. Having this information can be very practical, as often you cannot physically see your colleagues and therefore determine whether they are available to speak or not. Presence apps can eliminate unnecessary call transfers and can also ensure that when a colleague needs to assemble a team or communicate or reach various people, they know when and how they can do so, which improves internal communication and productivity.
In addition, video, visual and messaging capabilities integrated together (onto one platform) enable employees to remotely facilitate virtual meetings with each other, eliminating the need to move between offices/ locations and ensure everyone is present at the same time. This helps speed up internal communication and can significantly improve productivity.
Lastly, implementing UC can enable employees to work remotely, accessing their data and work files even when they are not in the office. An article written by the Telegraph found that many employees who commute to work also like to get work done whilst making their journey in. More than a fifth (22%) said working on their commute allowed them to get more work done, and more than a quarter (29%) said that they were able to do so because of the access they had to technology. This goes to show how UC can create tangible benefits for productivity, by giving workers access to their work station even when they aren’t at the office.
Similarly, a nine-month study by HBR found that productivity in call centres increased when employees were given the ability to work remotely from home. The study found that those who worked from home completed 13.5% more calls than their counterparts in the office, and the business itself saved around $1900 per employee on furniture and space over the nine-month period. Working from home couldn’t happen without enablement from UC technology, and so its implementation can facilitate home working which can boost productivity.
To sum, Unified Comms can boost business productivity by improving speed in the contact centres, which can lead to quicker response times and more conversions. In addition, UC can boost internal communication and collaboration, making teams more efficient and effective which can boost productivity. Lastly, UC enables remote working which means employees can work even when they are not in the office (ie commuting or at home) which has been shown to increase productivity.
Stefan Docherty is Marketing Executive at Call & Contact Centre Expo which runs in London, March 18-19, 2020). Information is at www.callandcontactcentreexpo.co.uk/
February 10, 2020
Sometimes, Bad Customer Data Is Worse than None
By Tom Treanor, Arm Treasure Data
To remain competitive in today’s data-driven world, sales success is highly dependent on a superior omnichannel customer experience. That means your customers have a consistently positive and seamless experience across your brand’s many touch points as they journey from awareness through evaluation to finally making their purchase decisions.
Unfortunately, many companies still struggle with integrating and analyzing data from the disparate technology platforms and apps that make up the omnichannel experience. In fact, a new Arm Treasure Data report, “2019 State of the Customer Journey,” found that nearly half (47 percent) of respondents struggle to gain insights from their marketing data due to silos.
Making sense of the data
The marketing technology landscape has exploded in recent years, with more than 7,000 vendors offering martech solutions. These companies provide tools for independently managing everything from mobile advertising to interactive content to influencer marketing activities (plus dozens of other sales and marketing functions). Due to the rapid growth of tools and data, it is no surprise that businesses are struggling to access and properly analyze the data.
The report also found that 54 percent of companies say they don’t have a full picture of their data and thus their customer journey. These blindspots put achieving an exceptional customer experience at risk. It’s like trying to finish a puzzle with only half of the pieces. Without a complete view of each customer journey, the odds of sending the wrong marketing message at the wrong time increase considerably.
Other key findings from the report:
● Customer journeys are complicated. Most (61 percent) report having three or more pre-purchase customer touchpoints, with about a third of all respondents (32 percent) reporting six or more touchpoints.
● Many don’t know what works. Nearly half (48 percent) say they are not using a formal attribution strategy, making it difficult to determine which of their efforts produced a sale.
● It’s a marathon, not a sprint. Long buying cycles make it critical to keep track of customer journeys. About 40 percent report the timeline from first engagement to purchase is four months or longer. Nearly a quarter (23 percent) of respondents don’t have any idea how long their customer journey takes from first interaction to purchase.
● Unreliable data sources lead to marketer confusion. The lack of a clear data picture means companies have potentially misplaced confidence in the effectiveness of their own marketing channels. Respondents cite salespeople and the company website as two of their three most influential marketing channels, while simultaneously acknowledging those aren’t the primary channels they turn to when making their own purchase decisions.
Why is it so hard to get the customer journey right?
Siloed data, combined with gaps in data analysis skills and lack of resources in marketing, technology, and data science, makes it tough for many organizations to develop accurate pictures of their customer journeys. Companies with many different types of marketing technology often suffer the most from the silo effect because data in one system is difficult to use with software and data in other systems. For example, an email marketing solution might not be able to easily share data with an advertising platform, decreasing the usability of the data.
To help break down silos it’s important to integrate the data you already collect. Useful marketing technology, such as customer data platforms (CDPs), combine data from many sources, online and off, to create a full picture of the different customer journeys your buyers and prospects take. With more unified data you can find out what compels customers to buy and why.
Silos Might Be a Big Point of Failure in Marketing Programs
Some of the questions CDPs and unified profiles can help answer are everyday problems. Ask yourself: Is something off about the responses you get to your marketing campaigns? Do you sometimes get odd or conflicting results?
You’re definitely not alone if you do. Our recent research on the state of the customer journey found companies are still struggling with data integration. More than half of those surveyed (54%) say their biggest barrier to leveraging data is fragmented or siloed data, which makes it difficult to get an accurate, integrated view of the customer journey.
And it’s no wonder — customer journeys are often long and complex. Most of our survey respondents (61%) report having three or more pre-purchase customer touchpoints, with about a third of all respondents (32%) reporting six or more touchpoints. And these touchpoints frequently happen over a course of several months.
With complicated buyer’s journeys becoming the new normal, you’d expect to see an increase in the use of multi-touch attribution strategies to ensure companies understood the path to purchase for their customers. (A multi-touch strategy divides up credit for sales or conversions among lots of touchpoints, rather than just using the last customer touchpoint as “the cause” of the conversion.) Yet nearly half (48%) say they are not using a formal attribution strategy at all, let alone one that can track multiple omnichannel interactions with the customer. This makes it increasingly difficult to determine which sales and marketing efforts produced a sale.
Relying on the Unreliable
Unfortunately, in the absence of a reliable source of integrated customer data, people tend to rely on unreliable sources — even though they know better. That’s why we see marketers reporting on easy-to-access vanity metrics or only tracking the final touch before a sale, and guessing at what came before it. That easy-to-come-by customer data can lead to some costly but ultimately avoidable customer data marketing missteps:
● Email platforms make it easy to see which emails garnered the highest open and click-through rates. But you often have to dig deeper to understand what is being clicked on. That means you may not realize that your high CTR email, the one that shows up in the top-line reporting, is driving people to unsubscribe or to view the email as a web page due to poor formatting or image sizes.
● Social media platforms make it easy to identify the customers who engage the most with content you post and your most engaged followers. But without a layer of sentiment analysis — and looking at what the engagement actually entails — you can end up boosting content that your ideal customer has actually been annoyed with, or showcase content that’s trending, but as an example of what not to do.
● Website analytics can show you how a customer that was ready to make a purchase found you. But, if you aren’t using advanced tracking, and integrating other channel data, you may decide to double down on your website or search engine advertising and not realize those customers were hearing about the product in a podcast or through influencer word-of-mouth and searching for your brand name specifically as a result of that initial engagement.
When we use easy customer data to make decisions, it makes us feel better than just going with our gut. But it may actually cause us to just make bad decisions with more confidence.
Gartner Estimate Says Poor Data Costs $15 Million
Gartner research found organizations believe poor data quality to be responsible for an average of $15 million per year in losses. Marketers at Shutterfly ran into this when they made some big assumptions presumably based upon browsing data. They sent emails congratulating new parents on the addition to their family — only, many of the recipients definitely hadn’t just had children. While some of the people on the receiving end were amused by it, and took to social media to post at the brand’s expense, that email also likely ended up in the mailbox of customers who weren’t able to conceive, had miscarried, or had lost a child.
Timing Is also Everything
Also, not all bad personalization is off the mark solely due to its message. Sometimes timing is an issue. Like the pair of shoes that follows you all over the web...starting the day after you purchased them from that retailer online. This sort of customer journey mismatch is caused by a lack of real-time data integration.
Between the time that customer viewed the shoes on the web, had them sitting in their shopping cart, and ultimately bought them, that customer’s data had to make its way through your internal processes and systems to eventually fuel a retargeting campaign.
If your data had been up-to-the-minute, you could instead be pitching that same customer on buying the shoes in another color, or on purchasing a completely different pair that your customer data shows is popular with purchasers of the initial pair of shoes.
Subaru, for example, used a customer data platform to unify all of its marketing and sales efforts. Not only was the company able to easily distinguish those who were ready to buy from those who weren’t, but the buying process was streamlined and accelerated, and when the sale was finally closed, Subaru didn’t have to waste resources on someone who had already bought a car. Rather, the company could begin automated marketing efforts steering customers back into dealerships for service, upgrades, and after-market products.
“Blasting Emails to Everyone” Doesn’t Work, But CDPs Do
Good, complete customer data, on the other hand, helps you tease out subtle shifts in customer attitudes and behavior, something Shiseido learned when it began using its own customer data platform (CDP) to unify its loyalty, browsing, and ad campaign data.
"Our new customer data platform built on Treasure Data is fundamentally changing how we communicate with our customers,” says Kenji Yoshimoto, Chief Analyst for Direct Marketing, Shiseido. “Blasting emails to everyone who tried samples or bought a particular product won’t lead to customer delight. Detecting a mood swing in each customer and changing the tone of push notifications does.”
And of course, when you rely on bad data to make significant business decisions, you not only miss out on that opportunity to delight the customer, you may even permanently turn them off to your brand. You’re not only missing today’s opportunity; you’re risking an unsubscribe or losing to a competing offer that forecloses on tomorrow’s opportunities.
Consumers want to buy from brands who provide them with an omnichannel “know-you” experience. But to deliver on that expectation, brands must invest in both data integration and tracking customer activities throughout the purchase process to ensure accurate attribution to use for future marketing decision-making.
February 6, 2020
Experience 2030: The Future of Customer Experience is... NOW!
By Wilson Raj, SAS
Modern technology has upended the way brands and consumers engage. New products, services, consumers, and competitors have arrived and keep evolving. Consumer behavior, likes and dislikes continue to change. What will the customer experience look like in 2030? And how will brands evolve to meet the expectations of future consumers? These are some of the questions addressed in “Experience 2030: The Future of Customer Experience” by Futurum Research and sponsored by SAS, the leader in analytics – free to download now.
The research found that technology will be the major driver behind the reimagined customer experience (CX), and that brands must rethink their customer ecosystems to keep pace with empowered consumers and evolving consumer technologies.
According to the research, consumers expect to embrace new technologies by 2030:
Additionally, by 2030, 67% of engagement between a brand and consumer using digital devices (online, mobile, etc.) will be completed by smart machines rather than humans, according to the research. And by 2030, 69% of decisions made during customer engagement will be completed by smart machines. This agility and extreme automation will drive the customer experience.
For more findings and insights, click above to download the global report, which highlights the findings from a global survey of more than 4,000 panelists, spanning three dozen countries across a range of consumer, industry and government sectors.
February 3, 2020
CDPs Could Revolutionize the Way We Drive — And Improve Dozens of Other Everyday Products Too
By Lisa Stapleton, Arm Treasure Data
Self-driving cars could prove to be a wonderful technological advancement, but what is there to help drivers in the meantime, before the technology becomes generally available and accepted everywhere? The answer might just lie in the same AI, analytics, and data technology that customer data platforms (CDPs) use to help marketers understand their customers. An interesting example is a new Advanced Driver Assistance System (ADAS) that electronics giant Pioneer recently introduced.
The Pioneer ADAS system uses real-time data streams from a variety of sources — weather data to determine driving conditions from moment to moment, car instrument data on the driver’s abilities and habits, road data, IoT data, and more. With the help of AI and machine learning, the Pioneer system comes up with a constantly updated display that shows how likely the driver is to have an accident at any given moment. Drivers can influence the “score” by slowing down, driving less aggressively, and by generally being more cautious.
How Pioneer’s ADAS Uses Predictive Modeling to Help Drivers with Real-world Hazards
But how would that look in our everyday lives? Picture this scenario.
You’re driving to work, the same route you take every day. But this time, a storm passes over: you’re suddenly faced with heavy rain and reduced visibility. All of a sudden, the “accident score” meter on your car’s dashboard moves into the red. You ease off the gas, move out of the passing lane and your score drops down to amber — you can’t get it into the green due to the adverse weather conditions — and it stays yellow as you finally pull safely into the parking lot — and breathe a sigh of relief.
Such real-time monitoring requires leveraging many different technologies, including data management of sensors powering the Internet of Things (IoT), AI, and machine learning. Arm Treasure Data enterprise Customer Data Platform (CDP) integrates all of these technologies to provide predictive customer scoring, which Pioneer is leveraging as part of its Intelligent Pilot offering for the Asia-Pacific region.
What’s at the Heart of Real-time Predictive Modeling?
Advances in predictive modeling and predictive scoring — in retail customer personalization, fraud detection, finance, and insurance — are stimulating heightened interest in using AI in accident mitigation. Accidents are also an important emotional issue, both for their impact on victims and for the many citizens that feel more needs to be done to reduce the problem. And some of the causes of accidents will only get worse over time, as cities become increasingly traffic-bound and rural roads don’t always receive the timely upgrades and maintenance, making the driving environment even more challenging.
Why Do We Crash? Customer Data Helps Calculate Individual Scores
Professor Kazuya Takeda, a well-known expert on analyzing accident data and part of the team that developed Pioneer’s Intelligent Pilot, an AI-driven ADAS, has found that factors highly correlated with accidents include a variety of behavioral data specific to each driver — such as reaction time, age, overall approach to driving, and other individual influences.
Environmental factors such as weather changes can dramatically up the odds of a collision. Rain, snow, or obscurants such as smoke or fog can block views, and water or ice increases stopping times. In addition, limitations in the streets and driving surfaces themselves can create a risk; some roads are better designed and maintained than others. The amount of traffic can also dramatically change the risk of having an accident. And even the most skilled drivers can be a little “off” in their reactions if they’re distracted, angry, or just having a bad day.
Capturing all of these variables to make decisions that positively affect the outcome of a journey — whether it’s a physical journey or what marketers call a customer journey — is no easy feat. Yet Pioneer, using Arm technology, is looking at how it can be achieved — and in real time.
Pioneer is exploring the intersection of converging data through Arm Treasure Data’s enterprise Customer Data Platform (CDP) with Pelion Data Management. This platform crunches data from multiple sources to provide easy predictive customer scoring.
Predictive Analytics Power Risk Scoring
Using this technology, Pioneer has developed “YOUR SCORING,” an in-car display feature that shows drivers a real-time, constantly changing estimate of their accident risk. The score is based on external map and road-condition data as well as the behavior of the driver. By combining and analyzing these data sources to create an overall picture of a journey in real time, YOUR SCORING is able to help drivers reduce their risks.
The AI and data management capabilities help Pioneer to quickly gather and analyze numerous data feeds (such as street layout, traffic signals, telematics, and third-party data) and apply machine learning (ML) techniques to deliver a single score through the system. At any given moment, the system could be processing reaction-time data from the driver, factoring in previous driving history and combining this information with terrain and current weather data from the nearest IoT sensors.
Personalized Data-driven Displays Could Be Next
Arriving at an accurate accident-risk score is one thing. But delivering this real-time, potentially life-saving information in a well received, actionable way is another important technical feat as well. Data plays a major role in this respect as well, and displays could be the next frontier for personalization.
Research has shown that people vary quite a bit in how they react to different types of displays and instructions — just as they react differently to various types of sales initiatives and targeted marketing efforts. A simple thing like the design of the risk display can affect some people’s willingness to take instructions.
These displays and user interfaces can now be personalized to people’s preferences and refined by the AI’s monitoring of a driver’s compliance as the display is varied. The next generation of driving technology might be individually tailored in many dimensions, to meet such individual customer behavior and preferences, just as retailers use the same technology for personalized customer journeys and customer experiences. Full-featured CDPs with the ability to integrate many different types of data, are obviously a critical technology for enabling such on-the-fly data unification and real-time analytical decision-making.
So, if you’re hearing a lot from the backseat drivers in your life, stay patient and calm. Someday, they might relinquish their roles to a smarter — and potentially much less annoying — AI. And unlike your current backseat chorus, the AI won’t ask “Are we there yet?” — because it will already know.
January 30, 2020
If You Want Customer-Centricity, Dismantle Your Data Silos
By Lisa Loftis, SAS
It is not surprising that CDPs have rocketed to the top of many marketers’ wish lists. Marketers have a very real need to corral customer data currently residing in disconnected silos both inside and outside the organization. In an HBR survey on using real-time analytics to improve customer experience the top challenges marketers faced were legacy systems, data silos and multichannel complexities. In a Forbes Insights study on the rise of CDPs, only 1 in 5 executives surveyed considered their companies to be leaders in customer data management and only 13% believe they fully utilize customer data. These difficulties persist despite the fact that we have been trying to uniquely identify customers and consolidate first-party customer information since the late 1980s.
Marketers are not the only ones facing these challenges. Data silos and misaligned technology also make every list of issues impeding companies as they try to move toward customer-centricity across all their business lines. The sheer number of technologies in today’s martech stack, many of which create or store their own copies of customer information, is astounding. Add to that the customer-oriented data warehouses and data lakes facilitating analytics and master data management applications facilitating operational activities that we still see, and the magnitude of the problem is clear.
Even with the adoption of CDP, if we are not careful, the technology alignment problem will simply continue to grow.
While there is no silver bullet for this problem, there are steps that companies can take to ensure their technology enables customer-centricity rather than disabling it:
Develop a customer technology strategy. A strategy for customer tech is critical. Customer experience (CX) leaders should work with their IT partners to answer questions such as the following:
Once the existing data silos are understood (this includes marketing and CX technology, sales and service automation applications, web and mobile applications, and analytics), the assessment should expand to include gaps in technology, disintegrated sources of data, incomplete information, and independent applications that are not or cannot be integrated. The CX team can then use customer journey maps as prioritization tools to fill the gaps and realign the technology.
Get tough on acquisition. The vast set of shiny new tools available to the CX team can be very tempting, but if the goal is to dismantle data silos, tough decisions will have to be made. The technology strategy can help. Every potential purchase should come with a clear integration plan and budget. If the money or resources for integration are not available, the purchase should be reconsidered. Vendor solutions, including CDPs, should be closely examined for their ability to integrate with other applications. Closed applications or black box solutions should be considered only as a last resort.
Understand that there is no virtual view. Of customers, that is. The shortcut that no CX leader can afford to take is skipping the single customer view. Identity management is the top requested capability for CDPs for a good reason. It is difficult to achieve, and digital channels complicate the situation significantly. The temptation to implement multiple customer profiles is quite strong today because many large technology applications come with their own customer database. Attempting to match across these applications on the fly is difficult at best and fraught with peril at worst.
Picking a single customer master application like a CDP and doing the block and tackle integration work as applications with customer databases are added is a critical step to tearing down customer data silos. Multiple disintegrated customer databases are never the right answer, and looking to them to solve a CX problem will add to customer-centricity challenges rather than helping to resolve them.
Becoming the de facto integrator between siloed business groups will be increasingly critical for the CX leader of the future. Start planning today for what your organization will need tomorrow.
January 27, 2020
MobilityWare Uses CDP and Predictive Scoring to Learn What Makes Some Games So Irresistible, Play After Play?
By Lisa Stapleton, Arm Treasure Data
What makes a game stay appealing, play after play after play? That’s what mobile games purveyor MobilityWare wants to know — and it’s using a Customer Data Platform (CDP) and predictive analytics to help figure that out.
If you’ve ever played games on your phone, chances are you’ve played a MobilityWare game — either its flagship Solitaire or any of the many other games the company offers, such as BlackJack, Spider, or Jigsaw Puzzle. Founded in the 1990s, the company’s continued success and platform transitions are due in part to its deep understanding of its players and their customer journeys. And lately, MobilityWare’s creative use of customer insights is getting an assist from Arm Treasure Data enterprise CDP.
How to Improve Customer Lifetime Value? Keep Players Playing
One of the toughest things for a gaming company to do is keep its best customers playing, but that’s pivotal in increasing customer lifetime value (CLV). And the answer is usually highly dependent on the game, the demographics of the players, and even individual customer journeys and player histories. Teasing out player incentives or other customer retention strategies requires careful customer data analysis, the kind that CDPs can quickly and automatically perform.
The other problem games sellers face is how to monetize their games, and a major concern with any contemplated change is not cannibalizing revenues from current play. MobilityWare makes money from in-app purchases and ads, plus rewarded video.
Predictive Scoring & Modeling Help Dissect the Churn Problem
The challenge that the MobilityWare team faced was this: How do you figure out when a customer is likely to quit playing? And which player incentives could delay or stop customer churn?
Chris Densmore, director of analytics for MobilityWare, set out to get answers. He focused primarily on “dedicated players,” who had installed the game more than two weeks ago, but who had not played in the previous two weeks.
“Dedicated players can be incentivized, they’re more valuable, and we have a larger behavioral dataset because they’re more likely to have played a lot in the past,” Densmore says.
“Also, we can do more to influence the outcome, which is important,” he adds.
Densmore used MobilityWare data and analytic features from the company’s Arm Treasure Data CDP. Then he chose a logistic regression model, because it can still produce valid results even if some variables are correlated — as they usually are in real, live users — and because logistical regression makes it possible to interpret contributing factors as well. Another bonus: The coefficients that come out of regression can be easily placed in SQL scripts to produce predictions.
Customer Data Insight #1: Coins Don’t Work Like Boosters
Densmore’s work got some surprising results. Rewarding people with a coin that can be redeemed within the game wasn’t the clear winner. It did reduce churn, but had a negative impact on monetization. People tended to hoard them for unspecified “later use,” rather than play more and use them in the game. And who wants to take steps that hurt revenue if they don’t have to?
Customer Data Insight #2: Gamers Want Boosters, Not Coins, and They Pay More If You Help Them
The real surprise was that giving someone a “booster,” or a small assist or tool they can use to win, was almost as effective as coins in combating churn, PLUS it increased ARPU (average revenue per user) by more than 450 percent for those with a probability of churning between 60 and 80 percent.
A little bit of help — but not so much that it devalued the accomplishment of winners — was the key to reducing churn and renewing player interest in the game. And without the CDP data, it would have been tough to get the customer insights that pointed the way to the right customer experience. With it, hypothesis testing and predictive modeling pointed the way to higher profits.
Just think how many other product “churn” problems could be informed by similar analysis. Many other areas where fashion or trends are short-lived come to mind. For example, Shiseido uses a CDP to analyze loyalty data and combine it with other data feeds to predict when people might be open to new beauty products — or even a major makeover. Telecommunications companies, which live or die by the difference between new customer acquisition rate and churn, could also use CDPs plus predictive analytics to gain insight about what makes someone switch to a new plan or provider. The insights that CDPs can provide are only just beginning to be harnessed for better marketing and even improved product design. Predictive analytics are clearly more than a fad, and with the help of CDPs, they are fast becoming an easier-to-use tool in every marketer’s workshop.
January 23, 2020
Customer anonymity can’t be guaranteed, but differential privacy can help change the game
By Susan Raab, CDP Institute
The relationship between companies and consumers is increasingly complex, with concern about privacy growing among consumers and pressure increasing on marketers at companies to use customer data more to engage with and find new customers. The key question is how to achieve a workable balance between the two.
With consumer experience projected to become more important than price or product in ensuring customer loyalty, the more trust you engender the more likely you are to grow your customer base. Customers want to understand how their data is being used and to know how to participate in the process of protecting their privacy.
The first step on the part of the marketer is to recognize that data is at risk whenever it is shared. You’ve heard about studies that have shown that even anonymizing data doesn’t always work. Back in 2007, Wired magazine reported that, “Netflix published 10 million movie rankings by 500,000 customers, as part of a challenge for people to come up with better recommendation systems.” The data was anonymized removing personal details, but researchers at the University of Texas were able to de-anonymize that data using only a small set of public data from the Internet Movie Database (IMDb). Last month, the New York Times’ Privacy Project showed how cell phone company data of GPS pings from the cell phones of 12 million Americans could identify almost anyone when matched with public address data.
This has been a topic in the privacy field for decades and has been done with all kinds of data, including medical, financial and genetic. It’s scary and seems overwhelming, yet companies are still being asked to work to meet legal requirements laid out in legislation, including GDPR and the new California Consumer Privacy Act (CCPA) and to show they are putting appropriate privacy protections are in place.
While the nature of data security is that it can inevitably be breeched, regulators want to know that a company is being proactive in their compliance and consumers value companies that are transparent with their process.
This doesn’t mean you can’t use data for anything, but it does mean that you have to be careful to anonymize it effectively. Experts say there is no guarantee, but one powerful approach is a statistical technique called differential privacy, which is used by Apple, Google and other big tech companies via an algorithm that “adds random data into an original data set” (Digiday, April 2019). In September 2019, Google announced that they were allowing access to their differential privacy library to help developers at other companies achieve this.
Robin Röhm, chief executive officer of apheris AI GmbH, a start-up that develops artificial intelligence algorithms for biomedical data says, “what we’re addressing is really an operational question about whether a company has defined a top-down logic to ensure semantic-enabled data privacy protection. This means everyone in the organization needs to adhere to the process. Then you have privacy by design, in which the process is incorporated into the company’s architecture with algorithmic rules customized to its needs.”
We’re in early stages of understanding how best to ensure individual privacy both tactically and legally, but those who are focused on this area agree that it takes collective knowledge and ongoing diligence to evolve strategies that work in the present and over the long-term.
January 20, 2020
eTail 2020 Report: Retailers Are Relying on Personalization and CDPs to Go for the Win
By Lisa Stapleton, Arm Treasure Data
Is retail finally getting serious about customer data? Are businesses growing impatient with getting only vague ideas about who their customers are, and how to maximize the chances they’ll buy, or buy again? And are they serious about using data throughout their organization as a strategic weapon in the fight for profitability?
A new retail report from eTail, WBR Insights, and Arm Treasure Data reveals an industry that’s betting big on leveraging data-driven technologies to drive more tailored and personalized customer experiences (CX). In doing so, retailers aim to better engage with their customers throughout the purchasing lifecycle. The responses suggest that for some retail marketers, using customer data to create customized customer journeys and experiences is not only a big part of their strategy, it’s the whole game. And to do all that, they’re increasingly turning to customer data platforms.
Customer Data Is Transforming Retail Operations
The report, “Retail CX and Data Management Strategies in 2020,” is based on a roughly even mix of department heads and senior executives at VP level or higher, all at retail companies with more than $1 billion in revenue.
Most of the surveyed executives believe better customer data, smartly applied, is critical to crafting customer experiences that lead to greater sales — and they plan to go all-in on data-driven customer experience (CX) in 2020. As one department-store VP emphasizes, his company’s investments are largely oriented toward personalized omnichannel CX, and “are mostly made to provide customers a more localized, personalized, and smarter shopping experience.”
When asked about their most important strategy for retail success, 29 percent of respondents said that empowering CX teams to build better relationships with customers was their highest priority. Over a fourth of respondents (26 percent) are prioritizing creating a single view of their customers across all their touchpoints, including physical and digital. Another 26 percent of respondents are prioritizing improving their analytics to unlock the full potential of their customer data.
Majority of Retailers to Implement a Customer Data Platform (CDP) in 2020
But making the right decisions about how to appeal to each individual customer is tough, especially when retail databases can include thousands or even millions of customer touchpoints. Data silos across businesses add to that challenge, creating an environment where retailers may unknowingly have multiple interactions with the same customer (for example, via an email campaign, social media reply, customer service engagement, etc.).
Perhaps that’s why more than three out of four retailers surveyed either already have a Customer Data Platform (CDP) or plan to invest in one in 2020 to help unify all of their customer data and build a single, holistic view of each customer. This is critical in enabling retailers to provide more personalized experiences, offers, and service to their loyal customers regardless of whether they shop on mobile, in-store, or online. But this trend is new, evidenced by the fact that so many respondents — nearly half — are apparently just starting to invest in CDPs, compared to only 31 percent of respondents that already have one.
Early Adopters Realize That All CDPs Are Not Created Equal
Advanced retailers are beginning to understand the benefits of implementing a CDP for delivering better customer insights, and they are prioritizing tailored solutions that address specific business needs. According to the study, more than half (51 percent) of retailers mentioned they look for a CDP solution that will accommodate the specific needs of their organization, compared to 31 percent who want an off-the-shelf solution.
CDPs and Customer Data Are a Critical Retail Link to Customers, Profitability
Retailers realize that creative use of customer data — particularly to drive customer experience — is their best chance for retail growth, and they see CDPs as increasingly central to their operations. The head of a large department store sums up the critical connection: “A Customer Data Platform is the most important link between digital tools and the customers themselves.”
Once retailers have unified their customer data, they can use it for better experiences such as clienteling, delivering more targeted and engaging experiences (for example, augmented reality) and providing choice of checkout methods (whether that’s human interaction or automated quick-checkout through use of mobile phones.)
With all of the potential of a single, unified customer profile, it’s no surprise that 64 percent of respondents with centralized systems were satisfied with the quality of their data, compared to only 52 percent of those that had decentralized systems. This speaks to the importance of choosing a flexible CDP, for easy integration with other existing martech solutions already in use.
Expect More Customer Data Collection in 2020 — Especially from Mobile Devices and Sensors
The trend toward collecting ever more data for tailored user experiences is accelerating, as retailers try to supplement the online shopping, loyalty, and demographic data they already have with more mobile phone and sensor data from IoT devices.
As one C-level executive at a specialty retailer puts it, “Our stores and examining devices will carry more sensors to help us provide the best possible product suggestions to our customers.”
Retail Moves Toward Data-Driven CX
It’s an approach that’s changing both customer experiences and all of the back-end operations needed to increase profitability. “Customer data forms the base of transforming retail operations,” wrote a C-level executive in specialty retail.
A different respondent wrote, “effective use of customer data does help in increasing store profitability.” And perhaps the best survey explanation of the trend toward data-driven CX is a C-suite responder’s comment that, “the goal is to connect customer data across our operational platform so that our people in operations can make quicker decisions.”
Another C-suiter from a specialty retailer has a much broader strategic goal: “Aggregating data from multiple channels and creating a single fabric of customer data is what we’re focusing on with our Customer Data Platform.”
That’s a sentiment that’s becoming increasingly common for companies undertaking major personalization initiatives. For example, a recent Forbes study on personalization found that retailers who are ahead of the curve in implementing successful personalization initiatives — people Forbes calls “leaders” — value both CDPs and personalization as important parts of their strategies. In addition, 76 percent of leaders believe a customer data platform is critical to their personalization initiatives.
Could a CDP Be a Part of Your Success This Year?
So as you plan your strategy for making your numbers in 2020 and beyond, consider the findings of this complimentary eTail report. The findings are clear: retailers are going big on customer data, and CDPs are central to the customer-experience strategies of the future. Are you ready too?
January 2, 2020
Building a Modern Marketing Organization
By Joshua Neckes, Simon Data
How an integrated marketing team can drive transformation
The idea of a “modern marketer” is always a moving target. New channels and new technologies fuel an ever-changing set of customer expectations. What’s clear is that in a world where no consumer is untouched by data-driven giants like Google and Amazon, the modern marketer needs a modern marketing team – one organized around producing holistic, data-driven, customer-centric experiences – to compete. But whether you’re a digital-native challenger or an iconic legacy brand, building the type of team that can deliver these types of experiences presents a challenge. At Simon Data, we focus on supporting great customer experiences everywhere. Along the way we’ve learned what it takes to build a team that can deliver.
Let go of legacy structures
At Simon, we’ve worked with numerous emerging and legacy brands. While it’s clear that they come at the challenge of building a modern marketing team from different places, they nevertheless encounter similar challenges. For established brands with legacies that extend to the pre-digital era or even simply to the pre-web 2.0 era, the marketing function likely didn’t spring into existence in a fully integrated form. Instead, as new channels emerged, marketing teams added new roles and functions to accommodate them, creating silos. For example, a team responsible for email communication may have no communication with the team creating advertising content. Even if those digital functions sit under the same umbrella, it’s possible that they have no meaningful contact with the group responsible for creating in-store experiences at a physical location and no way to share insights gathered from their respective efforts.
However, while much has been made of progressive brands’ talent for adapting to new channels, many of today’s digital-native challengers still struggle to integrate new channels and functions as they grow. In some cases this might mean learning to buy more traditional forms of media, or linking a digital experience to a newly launched physical retail space the siloing of new functions and new data channels is still common. In many cases the pace of progress means it’s hard enough to plug in a new capability let alone find a way to fully integrate it into a team.
Overcoming this history can be difficult since it requires taking on institutional inertia to rethink the marketing team org chart. But changes to team structure can often spur new ideas, facilitate better communication, and enable the kind of cross-functional data sharing required to execute a multi-channel campaign. Technology solutions can also play a role, using a customer data platform to unite and make actionable all of your customer data from new and legacy channels.
Build a team around experiences, not channels
Once we break down the silos between marketing functions, we’re still left with an open question: namely, how should the resulting team be structured to best take advantage of its newfound openness and cross-functional accessibility. The answer, for many of the most progressive brands that we’ve partnered with, is building their new marketing team around customer experiences and using stages of the customer lifecycle, rather than broad messaging channels, as their key organizing structure.
Instead of channel-based teams, these progressive brands create cross-functional pods set up around moments in the customer journey. For instance, a marketing organization might feature a pod solely focused on the experience of new customers. This group, encompassing advertising teams, performance marketers, brand strategists, creatives, data scientists, engineers, and in-store experience experts, would be responsible for all aspects of the new customer experience ensuring that all aspects of the onboarding process are consistent and oriented toward the way consumers are interacting with the business at that stage. Data collected from these similar interactions can be used to optimize for retention, upselling or moving customers into the next phase of their relationship.
Orient yourself toward behavior, not demographics
In the past it was useful to view customers demographically, as men or women, as 18–34, as urban or rural. Those descriptors spoke to customer behavior in a meaningful way, and marketing teams were built to think of customers as demographic segments. But modern consumers don’t fit so neatly into boxes. The rise of personalized experiences, from Netflix queues to Amazon recommendations, has changed the way consumers experience commerce and reset their expectations. Regardless of age, gender or location, customers are increasingly expecting experiences to be tailored to their individual needs and preferences.
Many businesses are finding it more advantageous to orient themselves toward a psychographic view of their customers. Looking to the way individuals interact with content rather than trying to form wide customer buckets based on external characteristics. It’s often more advantageous to think of customers as browsers with an interest in skincare products than as women aged 18–34. However, understanding all of these interactions requires a holistic view of your customers and a team that’s able to understand and apply data across all your channels and touchpoints.
Successful modern brands are more communicative on a one-to-one basis than ever before. They ask you, “What’s important to you? What information or experience is most valuable to you?” Asking these questions early and often, especially of new customers, takes some of the guesswork out of marketing communications. This questioning could take the form of explicit customer surveys and requests for feedback, as well as through testing and experimentation to determine which types of information and offers customers respond to. Regardless of the collection method, an agile modern marketing team can communicate with customers across channels, collect the right information, and use it to inform how and what each customer is served.
This type of responsive, real time communication with the brand is now possible at scale. You don’t need to call customers individually or survey them in the store. Everyone has access to websites and apps in their pocket that enable use to keep human customers in the loop and collect real-time feedback. And if you can’t get your customers the information they need, a real human contact can be a single touch away. The brands that are able to offer and clearly articulate this blend of automation and human interaction will be the most successful and creating fully integrated experiences.
Transformation is continuous
Finally, it’s important to remember that transformation is an active process, not a single end goal. Building a centralized plan isn’t enough to ensure that you continue to see the benefits of transformation. Modern marketing teams should be designed to test, experiment, pilot new programs, and iterate on an ongoing basis. For a modern marketing team, the ideal state isn’t one of endlessly future-proofing by adding new channels tools, channels, and skills, but rather on of consistent reinvention which delivers consistent results.
December 26, 2019
Three Big Barriers to Scaling Personalization
By Jason Davis, Simon Data
Whether they were hosting a dinner party or sending a wedding gift, the etiquette columnist Emily Post often advised her audience to add “a personal touch.” Emily Post authored most of her famous columns at the turn of the last century, so it might surprise her that the people most in need of her advice today aren’t society hostesses and housewives, but rather modern consumer-savvy businesses. Personalization is no longer just an aspirational goal for modern marketers. With each passing day, customized experiences are becoming more commonplace, whether they’re in our streaming queues and search results or on the retail floor. These customized experiences have the cumulative effect of raising the bar on consumer expectations. Brands that can’t clear that bar risk not only missing an opportunity but also looking a bit rude by comparison.
In truth, most brands know that personalization across all their channels is critical to success. What’s more, brands have access to more consumer data now than at any point in history. Most digitally progressive brands interact with customers across multiple digital and physical touchpoints, all of which yield signals and customers data points that can be used to fuel a personalization strategy. However, despite this abundance of data and a definite will to succeed, personalization efforts have run into some fairly persistent stumbling blocks. Here are the three biggest barriers preventing brands from fully hitting the mark in personalization:
One of the biggest challenges for brands looking to implement fully realized one-to-one personalization is the inability to access data. Recent years have seen many forward-thinking businesses drastically amp up their data collection efforts. As a result, much of the data required to execute a scaled personalization strategy is already available, but the siloed nature of many marketing teams makes it difficult to access, let alone build a unified view of the customer.
Today’s marketing teams are often assembled in a piecemeal fashion, over a period of years, to accommodate new channels and objectives. As a result, their data is collected and stored in a commensurately piecemeal fashion, distributed across different systems, tied to specific channels, and often managed by a variety of different teams. This siloing of data makes challenging to deliver consistent messaging across channels which can, in turn, lead to inconsistencies in communication both within teams and between brands and their customers. Since continuity between marketing channels, customer support, and on-site experience is increasingly key to customer acquisition and retention, it’s critical for savvy brands to unsilo their data and bridge gaps between teams. This is one of the primary challenges that customer data platforms have emerged to solve. By uniting data from disparate sources, a CDP can build a unified customer view of the customer enabling a more integrated approach to marketing.
While breaking down data silos is a significant step in the right direction, marketers do face other challenges on the road to personalization. Getting data into marketing systems is still tremendously difficult, even today. Many marketing systems were not conceived to handle the scale of data needed to implement a one-to-one personalization strategy and therefore can’t accommodate the scale of enterprise data or the complex forms in which it is represented. These technical limitations significantly hamper the way brands are able to leverage the data they already own.
Many of these challenges are endemic to businesses managing digital transformation. As data-driven growth strategies become paramount to the survival of businesses, it’s important to remember that there’s only so much one can achieve with a point solution based on cookie-cutter one-size-fits-all strategies. The most effective road to greatness is one that builds towards long-term strategies while also showing immediate ROI.
Technology and Innovation
The final hurdle standing between us and true one-to-one personalization is a little harder to clear. Personalization technology has evolved rapidly in recent years, producing breakthroughs that have made a more unified customer view possible for many brands. But going all the way is going to require another technological leap. Specifically, we’ll need the kind of next-generation AI technology that we’ve just begun to scratch the surface of. From today’s vantage we can see the beginning of the path that will ultimately lead us to the personalization promised land. Just seven years ago deep learning experienced a huge emergence, and today those same algorithms can drive cars and defeat human chess masters. We may be just one major innovation away from a similar AI explosion.
The marketing technology of the future will need to be scalable and flexible enough to account for rapid growth and to anticipate new phases of digital transformation. We expect these next generation solutions will focus more on actionable insights built on a wider range of data inputs including real-time behavioral, historical, product, and deep access to content.
Despite the stumbling blocks, true scalable one-to-one personalization is close at hand. Brands increasingly understand what their organizational and technological challenges are, and as a result they’re moving to solve them. By restructuring marketing teams to reflect a more integrated approach to customer interactions, the old channel-based silos are starting to come down. Similarly, a new generation of marketing technology has emerged to help scale in-house data operations and manage the ever-expanding array of data sources and inputs needed to create a complete unified customer view. Meanwhile, a string of promising advances in AI combined with the huge investment of resources the technology has attracted, promise to soon crack the type of next generation technology that will tie all these threads together.
December 23, 2019
How to keep your sock drawer (and your customer data store!) tidy this holiday season
By Anthony Botibol, BlueVenn
Matching, de-duplicating, disparate storage locations… is this your customer data? Or your socks?
Have you ever been afraid to open your sock drawer because of the chaos you know will greet you? Tights tangled around socks, a potpourri of colours, types and brands, not to mention the dreaded assortment of single socks spilling over the edges, with no partner in sight.
An unkempt sock drawer is, in many ways, very similar to a messy customer database. Duplicates, mismatches, holes and the general wear and tear that happens each year… if you don’t regularly clear and maintain it, that drawer becomes a jumbled mess! And how many of us routinely put off the dreaded task of re-organizing our sock drawer, just as we do updating our database?
So, we thought the best possible holiday gift from BlueVenn would be some helpful advice on how to maintain a Single Sock View and keep your sock drawer tidy, functional and easy to navigate in the festive season.
Let the holiday sock incursion commence!
Just as the Yuletide spending spree provides retailers with a flood of new customer data, so the holidays bring a massive influx of new socks for everyone.
These traditional stocking fillers and token gifts are given to folk throughout the land, which means that it will soon be time to brace yourself for the dreaded annual reorganization of the cluttered sock drawer, an essential task if you’re ever to find a matching pair in the New Year.