Exploring the future of data-driven marketing
Grow Your Business Through Enterprise-Wide Customer Centricity
In this session, you’ll learn how to put customers at the heart of your intelligent enterprise and facilitate experiences that drive lifetime value. You’ll see how an enterprise-wide customer data foundation bridges organizational silos, enables real-time customer insights where decisions are made, and fuels personalized experiences that show that you truly know your customer.
All the energy for the first 10 years of the customer data platform era was focused on marketing and advertising. Deep profile data and precise audience segmentation led to better performance in social ads, email campaigns, and customer journeys. Lately, we’ve seen CDPs also start to drive more connectivity between CRM applications for better customer experience. Now, we’re beginning to see the next phase of customer data management: Creating better, proactive customer experience by preventing bad experiences.
Today, smart brands are unearthing interesting signals from customer data and applying them for various use cases. For example, why market to customers that have an unresolved issue in the call center? They probably don’t want to be sold anything new until their issues are resolved. By unifying marketing and service data, the CDP provides a way to execute this use case without making it a costly IT endeavor.
This is all great stuff, and exactly what customer experience managers should be doing. Leveraging loyalty data to drive e-mail campaign content (“one more flight gets you to platinum”), leveraging purchase data to drive next-best offers (“customers who bought this, loved this”), and using survey and preference data to personalize messages (“you’re seeing this, because you viewed that”). CDPs should be the glue that connects customer experience together, and it’s encouraging to see the technology finally start to cross the chasm from marketing to broader use cases. That said, perhaps we’re focusing on the wrong things. While these use cases are extremely valuable and certainly revenue generating if deployed correctly, maybe there’s an even more noble use for CDP?
I’ve written about the concept of “data exhaust” – the notion that digital consumers continually give off thousands of signals as they move through the world. Advertising clicks, video views, interactions with IoT-enabled devices, pin pad transactions, GPS-guided car travel, and restaurant check-ins are just a fraction of the digital exhaust we emit daily. Collected at scale, and unified to a single people-based identity, they offer a rich snapshot of customers.
These data provide an analytical view of customers that’s also entirely contextual – the signals I give off on a Monday morning (commuting, consuming business content, sitting in an office, eating lunch salads) may entirely differ from my Friday night persona (fine dining, searching for live music, located near a beach). While much of the customer profile can remain static and change infrequently (home address, job, income, number of children in the household), many of our attributes change minute-by-minute, or day by day (location, time of day, and weather). Brands need to consider how they balance their customer view and leverage CDPs to help bring behavioral data into their systems, balancing the long term “truth” of their profiles with intent signals that offer needed context. This is powerful stuff and is key to unlocking better performance across sales, service, commerce and (of course) marketing and advertising. But, if you look at what we’ve just experienced during the pandemic, it doesn’t get us to completely proactive customer experience.
Say you ran a popular truck rental service, and business was booming as people fled shuttered cities for remote working paradises in the suburbs and beyond. You set up effective marketing campaigns to likely movers, gave them a simple way to plan their move and book a truck. You offered them the ability to pre-pay, submit their insurance online, and add extras like boxes and moving blankets. You delivered an ideal customer journey from inception through purchase, but when they showed up for their pre-scheduled time…the truck was not there. (Cue sad trombone sound). All the data in the world didn’t prevent turning a delighted customer into a glaring red account with a negative NPS score. But what if it could?
What if all the rich “data exhaust” we collect and unify in the CDP profile could be sent into the key backend systems that mange the truck inventory, maintenance, and physical location of the fleet? This is known as the ERP system. As I noted in a previous column, commerce failures during the pandemic had nothing to do with a broken cart or check-out processes, but rather shortages due to the collapse of supply chains.
In our truck rental scenario above, the CDP would invisibly enrich customer experience – not by driving more demand and personalization, but by alerting the inventory management system via demand signals and throttling new bookings until supply issues ceased.Preventing a bad experience before it happens is deep, proactive customer experience – and one of the first steps on the way to enterprise CDP.Customer data pointed at the core systems that drive manufacturing and supply chain management is just one simple use case. There are hundreds more ways in which this powerful data can add intelligence and automation to backend of business processes. It’s an exciting new era!
Direct mail legend Charles Stryker recounted a famous story in 2014 about data that every leader managing digital customer engagement should know.
Years ago, the US Postal Service discovered they were spending millions of dollars a year to deliver mail to deceased people. Charles was hired to help them get a handle on their address records and create a “Do Not Mail” list. The work involved A/B testing to ensure he made correct assumptions about the data. To test early iterations, Charles sent direct response mailers to groups of people known to be dead, and similar groups that he could prove were alive. The results were astonishing.
The dead people responded at nearly twice the rate of the living.
Multiple tests produced the same results. Researchers eventually determined what happened: successful mailers went to households where the husband of the family died, and their elderly spouses were taking great care to go through the mail of their deceased partners. The widows wanted to make sure there wasn’t anything important in those letters—and probably were emotionally connecting with their husbands through that simple task.
As we think of today’s complicated digital journeys, where we seem increasingly dependent on algorithms and attribution models for targeting and measurement, are we forgetting the real, human element of customer experience? The human element is fundamental to delivering a 360-degree experience. We must understand both people’s expectations, and how high the bar has been set for customer engagement.Real customers, engaged with your brand: Enter data authentication. In a cookieless future, data authentication is key to creating customer relationships and understanding the intent of collected data in downstream systems.
Today, brands must consider Amazon the gold standard for customer experience. You buy almost anything in the world available for sale on Amazon, do so quickly with one-click ordering, pay seamlessly, expect ultra-fast shipping, track your orders, easily return products, and get relevant product recommendations. The more you buy from Amazon, the better it knows you. The experience of being an Amazon customer is so powerful that over 200 million people have paid $139 a year to belong to Amazon Prime to get free next-day shipping, and each subscriber spends an average of $1,400 per year. More eye-popping Amazon stats: 175 million people subscribe to Prime Video, 55 million subscribe to Prime Music, and the company controls up to 85% of the e-book market through its Kindle reader and store. What’s the secret sauce? Data gravity: the notion that the more data you have, the more you’ll attract.
Through purchases, streaming choices, music listening, and reading habits, Amazon learns a lot about us. Using that digital customer engagement data, Amazon personalizes our experiences so that we’re willing to give them even more information. This becomes a virtuous cycle. After a while, the cost of switching to another provider seems too high. To acquire all that granular data, there’s a value exchange. For $139 per year, you get unlimited free shipping, which JP Morgan estimates to be worth about $1,000. With Prime Video, you get one of the largest libraries of video and music content in the world, and Amazon keeps its content pipeline fresh by funding original content – to the tune of $13 billion in 2021 alone. The result of the data gravity effect? Amazon’s revenue for the twelve months ending June 30 was nearly $486 billion, netting more than $33 billion in net income.
In the post-cookie world, brands should rethink their approach to customer data collection by amassing less, but more meaningful data. Of course, not every company has 1.5 million employees, thousands of data engineers, and endless amounts of customer data. But every one of your customers wants exactly the type of customer experience they get at Amazon. They want BOPIS options at their favorite retailers. They want a Dunkin Donuts App that remembers their favorite breakfast sandwich and how they like their coffee. They want Uber Eats to show their highest rated meals from their favorite restaurants, make it easy to order them again, discounts for their loyalty, and—more than anything—to be rewarded for coming back.
This kind of digital customer engagement is incredibly important for the younger generations of consumers. Looking at just the U.S. population, more than 200 million people are deeply digitally embedded Gen Xers, millennials, and Gen Zers. While Gen Xers might have gotten their first iPhone at the ripe old age of 42, they became the first generation to massively adopt technology, and are now the demographic with the most disposable income. Millennials, the first digital natives, became young adults during the social media revolution, dated using Tinder, and used Facebook when it was still considered cool. The oldest Gen Zer was born after the founding of Netflix. Generation Z consumer behavior: What brands need to know Gen Z consumers are beginning to flex their economic muscles, bringing different perspectives and expectations than previous generations. Brands need to adapt.
In short, most consumers today bear almost no similarity to older generations when it comes to digital customer engagement and the way they consume media, purchase products, socialize, and interact with brands. They’re used to heavily personalized advertising and marketing, and their expectations of what “good” means have been driven by the world’s largest companies with the most access to technology and data. This is the reality for every company trying to provide a customer experience today. Digital natives: How to win the trust of Gen Z and Millennials73 percent of digital natives are involved in B2B product or purchase decision-making, and about one-third are sole decision-makers. Learn how to win their trust.
Only a few years ago, brands had a difficult, but simpler, remit: build the brand and the consumers would follow. Absolut Vodka was about as undifferentiated a product as anything on the market, but great packaging and a clever ad campaign made it a power brand. It thrived because the world worked on the principles of Byron Sharp, who posited that a marketer needed two things to succeed: availability in the consumer’s mind and availability of the product on the shelf. For decades, most brands had the perfect formula for creating demand: produce a great piece of content in video or print and create massive awareness through advertising. Brands that succeeded in creating availability in customer’s minds (mindshare) and had enough presence on store shelves (retail availability) tended to grow.The battle for consumer hearts and minds was waged with brand advertising and scaled product distribution. When the customer got to the store shelf, the biggest factor beyond price for choosing Tide detergent over Wisk was brand loyalty that one-to-many advertising campaigns created.
That system is dying rapidly, as mass media channels become fragmented into thousands of websites, apps, streaming media channels, and experiences we don’t even understand yet.As a marketer, you can’t buy eyeballs like you used to .This changing paradigm is largely responsible for average CMO tenure shrinking from 44 months in 2017 to only 40 months today. CMOs must be prepared to insert themselves along steps of the consumer journey and capture each tiny piece of digital exhaust that consumers’ gadgets and gizmos throw off, helping to inform their understanding of how they engage with a brand. This is key to building a customer 360 view for modern digital customer engagement. Connected business benefits: Intelligent customer service, happy customersConnected business benefits include happier customers and happier employees – that’s the philosophy of intelligent customer service.
But today’s consumer not only demands personalized engagements across channels – but they also want them to be effortless. Back in 2013, Gartner surveyed 97,000 consumers to understand how customer service interactions with brands impacted loyalty. The results were surprising. Many brands made gigantic investments trying to “delight” their customers, spending as much as 20% more in operational costs, but people didn’t care. Customers just want their problems solved. The study also showed that customer satisfaction scores (CSAT) turned out to be a weak indicator of loyalty – up to 20% of “satisfied” customers planned on moving on to another brand anyway. Also, customer service interactions themselves were four times as likely to drive disloyalty than loyalty, mostly because only unsatisfied customers end up talking to a service rep.
Brands must rethink their approach to digital customer engagement to reach the high bar of digitally native consumers. Nothing has changed since that study, except that we now understand that the delight strategy doesn’t work – customers just want an easy way to solve their problems and move on.What drives loyalty? The simple things:
As we explore what customer 360 really means, we need to look beyond typical customer interactions in advertising, marketing, sales, and service. We need to think about the mechanics of how customers and brands interact in processes like booking a hotel room, returning a product, renting a car, or approving a B2B purchase. We must change our definition of customer engagement to be more comprehensive.
[This post was originally published on The Future of Customer Experience]
Moving from next-best-action to next-best-dollar requires richer data, actionable intelligence, and pervasive automation
As Heraclitus reminds us, “No man ever steps in the same river twice, for it’s not the same river and he’s not the same man.” This is arguably even more true since Heraclitus uttered the phrase, given the rapid and abrupt changes we have seen in the world lately and their impact on global business. The global pandemic humbled many retailers that were slow to adopt to digital, as digital-only interactions grew to 72% in a short period of time. Suddenly, “buy online, pick up in store” wasn’t just a nice-to-have convenience feature for customers – it was a requirement for doing business in the height of the pandemic.
Did anyone predict the how rapid and dramatic the rise in global inflation would be? Businesses across the world had to radically alter their pricing and delivery models; as input costs rose, global supply chains tightened – all while consumers were tightening their own belts due to the corresponding rise in prices.
The framework for managing through this is broadly called “enterprise agility,” or an organization’s ability to quickly adapt to market changes. Do you work in an agile business? If you are not sure, think of how well your company would do if your cost of goods surged by 25% in a single month, or how you would react if one of your key markets closed overnight due to a geopolitical conflict, or if the government’s response to a pandemic shut down your business. Of course, all of this has happened recently, and many businesses discovered just how agile they really are.
Amid rapid change, marketing always seems to lead the way. Whether it’s figuring out how to message a price increase to customers, changing a product roll out based on regional market changes, or reacting to the sudden unavailability of a product based on a supply chain shortage, companies need to account for how their customers will react to change, and manage customer experience delivery as appropriate. Product, Sales, and Marketing are the three-legged stool in most organizations – and only one can move fast enough to react immediately to crisis. Changing and introducing new products is time consuming. Changing a sales organization that has been trained, hired, and has set annual targets can’t happen overnight. What can change? Marketing budgets, campaigns, website messaging, webinar content, search keywords, etc. Marketing is the only part of the organization that can turn on a dime.
When times change, and the CEO picks up the bat phone, he’s calling the CMO first. So, what does the agile marketing organization require?
Let’s take an example: A popular outdoor retailer runs a promotion for a new hiking shoe that is a “collab” with a trendy brand — and it goes viral. Suddenly, a sneakerheads worldwide go crazy and start buying. This company, used to a steady and reliable seasonal buyer, is now flooded with orders, running out of stock, and confronting a flood of new customers. While most brands beg for such a moment, it is the ultimate test of business agility, and a critical moment in time. You can win lots of new loyalists – or quickly become a flash in the pan.
What are the three foundational elements needed to succeed?
Data
It starts, of course with customer data management. First, you need the scaled ability to capture first party data with consent. Every new sneakerhead coming to the website and mobile app must be encouraged to authenticate and start engaging with the brand. This involves offering a give-to-get for new customers (free shipping on orders, or a discount) and, more importantly, a scaled mechanism to capture that user’s permission to message her in the future. The experience must be seamless and explicit, as well as completely transparent.
For returning customers, you must have the ability to unify everything you know about them on the surface (SKUs viewed, lifetime value for commerce, loyalty points and status) but also go a level deeper. What is the true value of a customer? How many times do they return an item, and by what method so they return items? How often are they willing to pay full price? This data is only accessible by connecting the backend (financial ledger and supply chain) data to the profile. With a limited amount of a new item, you want to sell out – but you also want to reward your most loyal and truly valuable customers. This uplift is only possible by connecting the backend of business data to the frontend of customer engagement – call it ERP to CDP.
Intelligence
If a company has managed to create a unified data model across the systems I described above, and has, using data science, created models that can predict true customer value, and is able react to changes in behavior and market conditions – you still need to scale intelligence. In other words, given the above requirements, every customer cannot be evaluated individually, and every decision cannot live with a data science team. How strong is your organization’s ability to implement a machine learning framework that updates customer segments based on new information? In the background, ML models need to be continually tuned to changes in engagement across channels, understand how pricing and availability for specific products change behavior, and overlap segments to understand how different buyers of the same product react to campaigns, creatives, and different outlets for marketing and advertising. Lifetime value scores need to be calculated against ever-changing baselines – LTV can change based on product and customer mix over time, making yesterdays big spenders tomorrow’s regular shoppers. Going beyond marketing and advertising, what type of intelligence is required to create success in the call center, or an ecommerce site, or a sales call? Models are only as valuable as their ability to create value in the endpoint of a specific application.
Automation
After intelligence comes automation. How do you action the insights you have aggregated? Low value customers need to be suppressed from the campaign for the popular shoes. When a certain colorway or size becomes unavailable, customers with those preferences must also be suppressed – or encouraged to pre-order. Customers who are the most loyal need to be notified to “buy now” or invited to use their loyalty status to get placed in the front of the line. They need to be put into the call center queue first and, when they visit the website, have a one-click option for putting the right-sized shoes into their shopping cart, with their shipping preferences already pre-filled. When loyal customers come into the store and can’t find what they are looking for, the point-of-sale system must give the retail associate a next-best-offer or action that has a high probability of success. This is the new battleground in marketing – the ability to utilize intelligence at scale to render the right decision across both offline and online channels, in near-realtime.
At some point, every organization comes across a situation that tests their agility, and the customer experience team is often on the front lines. Your customer profiles need to get progressively richer, starting with marketing and advertising interactions, including cross-CRM data from sales, service, and commerce touchpoints – but also go deeper to leverage insights that can only be derived from the backend: ERP data. Intelligence must go beyond data science-provisioned models and scale with machine learning, such that the customer profiles can be frequently updated as lifetime value and propensity scores change based on realtime inputs. To adapt to a fast-moving market, driving that intelligence into the action surfaces of business must be as automated as possible.
This next phase of customer data management, that brings the backend of agile business process together with the frontend of customer engagement, is not about the next-best action or offer. It’s about finding the next-best dollar.
[This post originally appeared in The Future of Customer Engagement on 12 April 2022]
Ben Bloom’s recent post on the Gartner Blog (“CDPs Don’t Eliminate Friction With Customer Data”)shined a light on a topic we rarely hear about in the CDP world – the cost and effort associated with building a first-party data asset, and the possibly diminishing returns of building a complex “Customer 360” view.
Amidst the hype surrounding the “cookieless future” (albeit warranted by brands’ general lack of preparedness for it), the answer seems to be “collect more first-party data with a CDP.” While not wrong, the singular focus on data collection to make up for the scale and accessibility of third-party data misses the point. Bloom correctly argues that the more you enrich the profiles in your data store, the more friction you create in the process. In a nutshell, while it may be relatively simple to go from A to B (moving from authentication to opting into first-party cookies, as an example), getting to C and D (setting preferences, opting into a loyalty relationship, et al) makes things more complicated.
This is fairly obvious, but in the CDP boom we are living in, many brands are caught up in the notion that more data is better, and not considering that better data (and less of it) may be more impactful. One of the lenses through which we can view this conundrum is the concept of data gravity. Put simply, “data gravity” is when you reach a certain point in your collection efforts in which the more data you have, the more you attract.
As an example, the more I buy my groceries from Instacart, the better they know me. After the third time shopping with Instacart, I can basically pre-select a list of core items I have ordered previously, and consistently end up taking 75% of their “you might also like” suggestions before I check out. They have me nailed. I keep giving them just enough shopping data, and they return an excellent experience. Instacart is amassing data gravity in the same way Amazon does – by appending my profile with the exact purchase and behavioral data needed to power next-better offer recommendations.
The basic model is a value exchange. I give Instacart both my weekly grocery business and the data related to it, and they save me countless hours shopping. Brands looking to provision personalized experiences across channels need to think about what customers actually find meaningful. What is the minimum level of value that needs to be exchanged to amass more data gravity – and the right data needed to power that experience?
For a delivery business like Instacart, knowing my address, stores I frequent, and items I tend to buy frequently are the core data at the heart of driving experience. Instacart doesn’t need to collect 500 more attributes about me to provision my experience – just the kind of data needed to deliver it. Not more data, just better data fit to its use.
Brands that want to go beyond the “more is better” methodology and start to amass meaningful data gravity must consider a few things before they decide on the value exchange equation, however.
Companies that go into their start with these three keys going into their digital transformation efforts will have the basis to discover the actual cost of data collection and determine at which point the friction introduced in the process erodes, rather than adds, value.
Is more data better? This question will increasingly be answered by customers themselves. In the new world of first- and “zero-party” data, the answer is that customers will give you as much data as they receive back in real value.
[A version of this post appeared on the Future of Customer Engagement]
Ever since Gartner’s Doug Laney coined the “3 Vs of Big Data” back in 2001, there have been endless iterations and attempts to define the terms of engagement. The core three were always “volume, velocity, and variety.” In subsequent years, there has been a consensus around at least two more: veracity and value.
In general, it’s easy to agree that we need the ability to manage ever-increasing amount of data (volume), collect and activate it in real-time at the speed of the modern consumer (velocity), manage increasingly diverse types of data (variety), understand the truth inside of data to make it relevant (veracity), and make it actionable in the different endpoints that impact business (value).
A lot has remained the same since 2001, and this structure continues to pay dividends as a framework for the broader discussion of data management. But how should modern CMOs, CIOs, and “Chief Data Officers” be thinking about this in 2022?
The excitement and growth in the CDP category have provoked some interesting questions about how to think through customer data management, so let’s apply the lens of current context to think through the “five.”
When it comes to leveraging data to drive customer experience (arguably the primary and most important use case for today’s CDPs), we are basically in the very first innings of a long and interesting game. There are so many possibilities for unleashing data on modern CX challenges. Loyalty data, commerce data, and marketing data are just the tip of the iceberg. Going a step further, imagine in-store purchase data, call center data, and social data combining to provision a truly rich “360 degree” view of customers. With such a profile, a smart marketer could do practically anything.
The ugly truth is that most marketers use relatively tiny amounts of CRM and e-mail data for personalization. While some more advanced marketers have managed to combine commerce, loyalty, and marketing interaction data at scale to drive better CX, many are stuck in the closed-loop of using marketing data to drive better marketing use cases and activating that data in channels such as display media and e-mail.
Another truth is that ERP data is hardly being used at all. That’s a huge opportunity. Enterprise resource planning systems are the foundational systems that store the real data tied to business outcomes that directly impact customers. Is the product in stock? How much does it really cost to manufacture and ship it? How soon can I get it there? ERP systems – and “supply chain management” in general – seem to offer the missing variety to power impactful experiences. This argues against CDP being an evolution of CRM, especially if we look at CRM tools as endpoints that only manage engagement between the enterprise and its customers.
I think there is an interesting argument to be made that the foundational data layer of customer experience starts a layer below with ERP — and connecting ERP and CRM through data is where the magic of CX might happen.
How do we connect people to the enterprise, not just the marketing department?
Managing data at scale has always been a challenge, but I don’t think even the most forward thinkers in 2001 could envision the truly massive growth in useful customer data over the past several years as mobile usage, and the “Internet of things” continues to create and make available massive amounts of highly granular data that can be pointed at customer experience use cases.
Marketers have indicated that they are using X data types today and estimate that they will use X in 2022. Those data are increasingly coming from contextual and behavioral signals – many of which need to be consumed and activated in real-time to be useful. Another element of volume hard to envision even a few years ago was the maturity in machine learning as a method of extracting value from these massive data sets.
Why did Google make TensorFlow – arguably one of the world’s best library of ML models – free to any developer in Silicon Valley? Basically. Because it’s not the algorithms that create value (they are largely undifferentiated), but rather the volume, scale, and fidelity of the data they need to run against. In short, more data means better results. And not just more – but more valuable. Looking at these three elements that drive success in AI and machine learning, it seems like “volume” is only as important as the quality and fidelity of the data. Yes, they need to be unified at the customer profile level, but the attributes also need to be connected to the business, beyond marketing and advertising inputs.
For example, I may be able to attach petabytes of data related to online advertising interactions at my CDP – but is the processing and storage of such ephemeral data worth the expense? Are there more valuable attributes that can contribute to enterprise data volume that is tied to the enterprise? We need to consider that better data is greater than more data. Better data is data that is connected to the business, and that can create better outcomes in endpoints like Sales, Service, Commerce, and Marketing.
How do we create engagement at enterprise scale?
When we think about the velocity of data, it’s tempting to focus on real-time engagement use cases that require a CDP profile (personalizing real-time journeys across text and e-mail, or website/app personalization based on behavior, as examples), and that is not wrong. There is a ton of low-hanging ROI to be gained through personalizing faster. Every CMO wants to connect their customers the business in real-time and, given the incredible amount of channels customers can engage a company on, it is starting to become a necessity.
The missing element when it comes to data velocity, however, is related to overall business agility – not just the ability to match an e-mail offer with web content. What do I mean by that? Think of today’s approach by most brands: they have a roster of new products they are going to put into the market, and they configure their outbound marketing plans to reach customers on the channels they are engaged with. New sneakers coming out are put into broad-scale national campaigns, with each channel slightly optimized for personalization. Makes complete sense. What brands market is directly related to what they try to sell in campaigns.
The hidden layer when it comes to delivering meaningful velocity in data management has more to do with data that can unleash true business agility. Instead of answering the “what do I market” question based on products alone, businesses need to add some additional context. What’s in my actual inventory, and how many of each SKU can I actually sell (supply chain). Also, what is my potential profit if I am successful (finance)? To whom can I sell, based on their permission (identity)? And also, where can customers buy it, and how quickly can orders be fulfilled (commerce)?
How we can give the people not only what they want, but what they actually ordered?
If the rise in CDPs has done anything, it has put a laser-like focus on the importance of unified data. Whether you call it a “customer 360,” a “single source of truth,” or the fabled “golden record,” enterprises have a mandate to connect as much data to a universal ID or profile as possible. Obviously, doing so eliminates the problem of stitching people together with their many different postal, e-mail, and pseudonymous IDs – and creates value in terms of unifying data for the purposes of cleaner analytics. At its core, getting to the “veracity” or truth behind the data is an identity challenge.
That said, if you think about how most non-technical folks think of “identity” when it comes to customer experience, the term has taken on a decidedly “marketing” flavor. Back when 3rd party cookies were in vogue, “identity” meant cross-device identity (stitching cookies and device IDs to the universal identifier), or maybe was thought of as data “onboarding” (matching hashed e-mail addresses to an active cookie ID). Or, when we thought of “identity enrichment” we were mostly thinking of big marketplaces where 3rd party data attributes like “car intender” could be appended to first-party data. Obviously, these tactics still have a place at the table in the identity conversation, but we need to broaden our thinking.
Now that we live in a world in which 3rd party cookies cannot easily be shared, and first party-data, when collected, must be done with consumer-driven permission, it is clear that the top-down approach to identity (I buy identity services by companies that capture data) has to change to a bottoms-up approach (I use tools that give consumers the ability to grant me permission to use my data). In a word, we have entered the CIAM era of identity management, and the new token of value is “zero-party” (permission-based) data.
In this new world, zero is everything.
Finally, as we think about the value delivered through modern data management, trends are becoming readily apparent. CDPs that are good at personalizing online experience – mostly marketing, advertising, or loyalty experiences – are shrinking, and “enterprise” scale CDPs that go beyond marketing to offer personalization in channels like sales, service, and commerce are on the rise. This makes sense, especially considering how much more meaningful “real-life” touchpoints are to consumers. In short, I’d rather you got my name right and knew my order number when I call your 800 number than get just the right ad in Instagram.
This trend is not going away, the big CRM companies will continue to expand their data offerings through tangible connections to CRM-based customer experience delivery. But, for every valuable use case that can be delivered by connecting marketing and call center data, there are dozens of opportunities to go a layer deeper and connect real enterprise data to CX.
As above, who is doing personalized ecommerce using “available to promise” data about products hidden in the supply chain? Or leveraging real-time inventory data from the manufacturing line to influence campaign execution? The answer is almost nobody, which is also the opportunity.
Is “enterprise data” hidden in the chain become the true driver of “enterprise CDP?”
We are in an interesting new era of marketing and technology. The last 20 years have been really fascinating to watch and be a participant in. I started my career in the first “walled garden” era, in which big publishers tightly controlled access to their audiences through the gates of salespeople and insertion orders. Big advertisers paid big money, upfront, to access television and print audiences at scale through the mechanism of salespeople.
Slowly, we entered the digital age where the same business model (salespeople and insertion orders) controlled scaled access to newer walled gardens like Yahoo! I remember signing some seven-figure deals for homepage inventory as a young marketing director. Those were the glory days.
Later, some really smart people figured out how to turn the industry upside down. With the browser cookie, marketers could gather access to people data and create their own segments — without paying the Yahoo! tax. Early ad networks started to find “business readers” and “soccer moms” at scale all over the internet, and the power shifted to the buy-side.
Soon thereafter, even smarter people figured out how to trade digital media programmatically, and the power shifted to technology companies and agency “trading desks” which took the lion’s share of advertising out of publishers’ pockets and into their own. Publishers were lucky to get 30 cents on the dollar for their premium digital inventory.
A long period of arbitrage-driven media occurred until a funny thing happened (but slowly, and over time): the principal mechanism for trading people data, the cookie, started to become less valuable and important. DMPs, the machines that publishers and advertisers used to manipulate this currency, faded away. First-party data become ascendant. E-mail addresses, postal mail identifiers, and mobile numbers became – once again – the currency of people data. Thus began the “CDP Era” in marketing, probably around 2016, and we are living in it now.
I recently wrote a book on the topic with my friend Martin Kihn (called “Customer Data Platforms” for lack of a better title), and we speculated that this era in which CDPs are the dominant technology for organizing and managing customer data, would be around for a long time and create many fundamental changes in how companies go to market, engage their customers, change the way we think about marketing and advertising, and even threaten to solve the seemingly impenetrable problem of attribution. Are we finally ready to deliver on the promise of the “right person, right message, right time?”
The answer is, “probably.”
The “probably” part depends on how we think about CDPs, and how we define the problem space. Here are some thoughts on what the new era might look like, based on some questions I have been asking myself.
What is the purpose of CDPs?
Typical answer: CDPs are a data management technology for capturing, transforming, unifying, segmenting, and activating first-party people data. Very true, and not wrong. But why? Generally, the answer is “to improve customer experience,” and that is not a wrong answer either. Better CX drives revenue, reduces churn, and helps people love brands. That’s why modern CMOs are investing, and why there are over 150 “CDPs” listed by the CDP Institute.
The problem is that most of us have too narrowly defined “customer experience” itself. While one of the primary drivers of this technology is, and will continue to be, driving more personalized experiences in marketing (better e-mail) and advertising (more relevant ads), true CX must go beyond those channels, and consider more human interactions – call center conversations, sales interactions, moments between an in-store customer and a clerk at the point of sales terminal. Even what happens at an ATM screen.
This “more than marketing” approach lies more in the domain of enterprise software companies, especially those with a CRM focus, as “customer relationship management” is the basis for provisioning those types of “real life” experiences. Traditional CRM is maturing quickly towards digital, and laying the infrastructure foundations to support more realtime capability, storage and processing of massively scaled data, and adding capabilities for pervasive intelligence (AI and ML) and automation to their stacks. How these advances over the next 3-5 years among the leaders in CRM will have the greatest impact on the category, as most software buyers are eager to consolidate their technology stacks, rather than be buffeted by yet another expensive “era” in data technology.
CDPs are Foundational
If we agree that CDPs are here to improve customer experience, and we also agree that we must define CX more broadly with a “beyond marketing” imperative, then the next question we need to ask is, “what are the things that will drive success in CDP?”
The first thing is obvious: identity. As I wrote 5 years ago, building a modern data management infrastructure begins and ends with mastering customer identity – the notion that knowing your customer is the key to everything and is the basis for the modern technology stack.
In this oversimplified view, identity – both known and unknown data management – is the foundational layer, driving success in intelligence (scaled, unified data drives success in machine learning) and orchestration (one “golden record” that can be activated across many disparate systems).
But the “know” element of the modern technology stack can’t just be about managing the many different keys and identities of consumers. Although it is critical to create a single profile from dozens of different identity keys (e-mail addresses, cookies, device IDs, mobile advertising IDs, et al), the real challenge is mastering the ability to continually enrich that profile with the metadata that drives value in experience. Namely, what are the last e-mails someone opened, the last five SKUs they bought online, the last three calls they had with the call center (and their outcome), and how many loyalty points do they have? Taken individually, any of those data points can drive outsized performance in any number of channels. Taken together, they can reveal true intent and can completely change the way brands engage consumers and go to market.
If we agree that better, unified customer data is the key to driving better intelligence and engagement), then what types of data truly matter, and how should we think about the next phase of CDP? I believe there are three fundamental shifts that are occurring in the CDP space, and each requires a new way of thinking:
Rethinking the Loaded Term “Identity”
The first thing to focus on should include recasting the way we think about “identity.” In a world where digital-only interactions with brands have skyrocketed from a baseline of 25% to over 50% in the past several years, accelerated by the pandemic (see McKinsey), the way customers access digital experiences is more important than ever. It’s very odd that a key foundation of customer identity management – the CIAM category – has not had a real seat at the table with marketers and advertisers.
Customer Information and Access Management, or CIAM – services including customer registration, self-service account management, consent and preference management, single sign-on (SSO), multi-factor authentication (MFA), access management, directory services, and data access governance – has lived almost exclusively in the domain of the IT buyer. For players in the marketing technology and adtech spaces, “identity” meant cross-device graphs, user matching with big platforms like Google, and maybe “onboarding” through services like LiveRamp. It’s interesting to consider that all of those critical services were delivered without the intervention of the customer. As we move into a world in which first-party data is ascendant, and privacy regulations demand more direct consent from customers themselves, CIAM capabilities become the core building block of the “know” layer of the stack.
This fundamental shift envisions much more collaboration between the CMO and CIO, a further alignment between traditional CRM and the marketing stack, and a much faster evolution of CDP from a marketing-specific packaged product offering and a fundamental layer of the overall enterprise software stack.
Rethinking “Commerce” Data
Why is “retargeting” still such a big market? Why do we seem to see ads for products we searched for, talked about, clicked on, or otherwise merely thought about appear in our social media feeds and on display ads? It’s obvious and effective – even a small amount of real purchase intent is a great predictor of what people will buy.
“If you bought this, then you’ll buy that” product recommendations and the marketing and advertising tactics they drive will continue to be a big part of the landscape going forward. However, as backend enterprise systems migrate closer to the marketing technology stack, there are opportunities to go beyond the obvious (retargeting, product-driven customer journeys, data-driven call center recommendations) and start thinking more broadly about what types of data drive revenue.
Consider this: Every commerce operation that fell to its knees during the pandemic had nothing to do with a failed shopping cart or broken check-out process, but rather, was a result of empty product catalogs caused by the collapse of supply chains. In other words, eCommerce failed during the pandemic not due to leveraging data-driven marketing tactics (commerce and marketing systems have been closely linked for some time), but due to the lack of connectivity between backend inventory management systems and front-end engagement tools. Basically, companies were marketing products that could not be fulfilled and delivered.
What if there was a tighter connection between the systems driving manufacturing on the back end, and the marketing of products on the front end? For centuries the way commerce worked was people predicted how many of something could be sold, manufactured it, and used marketing to create demand. Over the next decade, we will see that flipped on its head. People will buy what they want, and near-realtime supply chains will manufacture exactly what was ordered. This requires a new way of thinking. Show people products that don’t yet exist, create demand, manufacture, and deliver on-demand. This is starting to happen today, and it threatens to overturn everything the traditional marketer understands about demand generation. Plugging supply chain data into “CDP” is maybe the most interesting opportunity in the category.
Rethinking CDP as a Category
One of the arguments we made in the Customer Data Platforms book was that there were three types of CDPs:
The first category we called Systems of Insights, which are described systems that were principally concerned with managing customer data, creating a common information model, segmenting customers, and analyzing and activating data. These types of CDPs are most akin to CRM and MDM, and are considered “systems of record” for customer data.
The other type of CDP, a “System of Engagement,” maintains a realtime customer profile, and is mostly concerned with making sure the right customer gets the right message, offer, or action in realtime. These are systems more akin to journey management or Realtime Interaction Management (RTIM) systems. They are great for creating personalized engagement at scale, but not really where an enterprise would actively manage their first-party data.
The third category imagines an “Enterprise” strength offering that is the next of both worlds. Big enterprise software companies are working on building these today–systems where pure data management capabilities are structurally intertwined with the systems that deliver engagement (marketing, commerce, service, et al). In a nutshell, what if you had an infrastructure where a persistent customer profile and the ability to activate it was the foundation for all of a company’s systems? That would solve the problem of siloed data, provision an incredibly rich customer profile, make ML and AI smarter, and help drive experiences at scale across every touchpoint.
As the “enterprise” vision of the CDP becomes more of a reality it means that the way we think of “CDP” today is more of a framework for thinking about “data management” writ large, it will render many of the “CDPs” in the market today as point solutions, and it will transform the way we connect the backend systems (broadly, “ERP”) to the front end (even more broadly, “CRM”).
The New Era
In this new era, we will see identity redefined as consumer-driven through preference management, and more intelligent ways of connecting user data with the systems that need it. We will finally start to see the supply chain management and customer experience coming together – threaded through data and identity infrastructure, and we will start to see the emergence of what we can think of as true “enterprise industrial-strength CDP” as businesses seek to connect the way they operate their enterprise with how customers interact with it. It’s the beginning of an exciting new era.
[A version of this post appeared here on the Future of Customer Engagement]
BY DOUG DANIELS
There’s an iconic scene in Steven Spielberg’s Minority Report (a sci-fi thriller) in which Tom Cruise walks through a shopping mall, a steady stream of jabbering digital billboards scanning his retinas and instantly plying him with personalized advertisements that call his character out by name and even reference past purchases.
That movie is set in the year 2054. But today, we already have much of the technical capability to create the type of intrusive consumer experience Spielberg’s film envisioned, generating unprecedented opportunities for businesses and organizations to reach their customers, clients and audiences with precision-targeted strategies. This can be a double-edged sword, offering consumers more-personalized marketing and advertising and introducing them to products they love while also presenting a potential slippery slope into privacy violations and misuse of online data.
“The challenge is striking a balance,” says Chris O’Hara ’90, P’24, VP of global product marketing in the Data and Identity Group at Salesforce, a cloud-based software company that provides customer relationship management services. “People really want personalization, but they also want privacy. And now, either because of new state-level laws or because of voluntary company policies, organizations are looking at how to start collecting data from consumers in a more responsible way and still offer that personalization.”
Salesforce, with its headquarters in San Francisco, has grown exponentially since its founding in 1999, with 60,000 employees and more than $21 billion in revenue for the 2021 fiscal year. And as the world’s digital reliance continues to spread, Salesforce and its subsidiaries are expected to see steady revenue growth in the years to come.
Tech giants like Apple and Google are dramatically changing the way they use and share personal data, and many companies are now scrambling to adjust to the new challenges to marketing and advertising as their access to that data becomes increasingly limited.
But while the privacy landscape is certainly changing in the U.S., not everywhere in the world is following suit. While Europe led the way with its highly restrictive General Data Protection Regulation, or GDPR, Japan has taken a different approach. There, billboards have been equipped with facial recognition cameras so they can identify a person’s gender and age as they’re walking past and instantly tailor ads to them, not unlike the scene in Minority Report. The technology is intended to be anonymous, but it isn’t a giant leap to totally personalized advertising, and that raises enormous privacy concerns. Facial recognition technology is becoming so common that many of us unlock our phones with it now. Beyond the business and advertising implications, there are also questions about data obtained by government and law enforcement entities. The FBI alone has access to nearly half a billion images for facial recognition uses.
Traditionally, data from various websites is aggregated and profiles are built based on site visits. Lots of the browsing data on laptops, and desktops are captured via “cookies,” or little snippets of code that identify users and store their data. Mobile phones and other web-connected devices (even some refrigerators) also capture behavioral data. Widely available for purchase on open marketplaces, this consumer data can include credit scores, online behavior, a person’s specific interests, particular websites they’ve visited—all information collected and shared almost entirely without the knowledge of the precise individual. If you have ever looked at a pair of shoes online and then noticed ads for those exact same shoes that follow you to a series of other websites, such as news sites, that is part of what cookies do.
A big development in data regulation came in 2018, when California passed the California Consumer Privacy Act, requiring more transparency and limits relating to data use. That’s when you may have started noticing the ubiquitous pop-ups on websites asking you to opt in or out of a company’s data collection or cookies policy.
Google has said it plans to enforce new policies by next year, depriving marketers of the ability to purchase the type of data they’ve long treasured. Exactly how consumer relationships are built moving forward and how marketers and advertisers ply their online trade will most likely involve a blend of new tactics and strategies that will replace the use of third-party cookies, which have been a central component of online advertising for years.
“One idea is something called FLoC—Federated Learning of Cohorts,” O’Hara explains. “The way that would work is Google will identify somebody as belonging to one or multiple cohorts, which are basically groups based on people’s interests. So you might be in the fashionista cohort, or the traveler cohort, or the outdoors-enthusiast cohort, and you’ll be assigned this random number and Google will build these various FLoCs as long as there are enough people who meet the criteria.”
This type of group-level web tracking would represent a significant shift for how advertisers identify new potential customers. Instead of companies targeting the browsing history and hyperspecific consumer data about individuals, they’ll now be forced to settle for information about various cohorts as a whole.
What will these new privacy rules mean for big publishers and big marketers, and how will they collect and use data responsibly?
Say, for example, you are identified as a European-travel intender and antique-thimble collector. Instead of having your personal information shared, you would simply be assigned anonymously to a larger group that could include thousands or millions of antique thimble collectors. No individual-level data that could violate privacy would be included.
“The advertising industry is pushing back on this idea of cohorts, because obviously they’re used to doing things a certain way, and it has sparked a really big, raging debate about what the future is going to look like and how it’s going to work,” O’Hara says.
“What will these new privacy rules mean for big publishers and big marketers, and how will they collect and use data responsibly? Advertisers need to learn to use Google, Facebook and Amazon in new, effective ways that are still responsible and ethical.”
One important ongoing debate, O’Hara says, revolves around whether companies such as Facebook, Twitter and Google are objective “platforms” for content or they should be considered “publishers,” since they do exercise some editorial control and create their own algorithms and other structural elements that can control who sees what information.
O’Hara has been working in data-driven marketing and advertising from the earliest days, when the concept seemed more science fiction than concrete business necessity. An English major at Conn, his passions resided in creative writing, philosophy and drama. But a post-college job as a writer and editor for a cigar magazine introduced him to emerging marketing and advertising strategies just when the internet was beginning to broaden its reach, in the 1990s.
He quickly discovered he had a knack for sales and advertising and began learning about data-driven tools that would later prove revolutionary. He joined Salesforce four years ago, when it acquired Krux, the startup where O’Hara led data strategy, for $800 million. Krux, a data management firm, helped clients in the marketing industry better understand, analyze and target the customers who visited their websites and apps so that they could improve engagement and enhance customer relations through the use of far more precise ad targeting.
He and the co-founders of Krux wrote Data Driven, a book that examines the ways in which new data is transforming marketing. His latest book is titled Customer Data Platforms: Use People Data to Transform the Future of Marketing Engagement. The extensive line of marketing products Salesforce offers includes software for marketing emails, mobile ad campaigns, social media advertising and analytics, making it a one-stop shop for digital marketing. O’Hara’s role involves marketing and strategy for the products related to data.
“My job is to meet with clients and discuss data strategy with them and determine how to better understand people through their data,” he says. “This way, I can help them build better experiences across different touchpoints for their customers.”
A big part of what O’Hara does for clients at Salesforce is coordinate customer data in ways that improve customer service and incorporate more efficiency. For example, few people have been spared the misery of speaking to a call center representative who has a boilerplate script yet no knowledge of your history with the company or how to resolve your individual issue. Large companies often have disparate databases where customer info is stored. Salesforce helps consolidate data and use it more effectively so that when somebody contacts a call center, the person on the other end of the phone instantly has access to the caller’s order history, their interests and the marketing campaigns they’ve already been exposed to. The customer doesn’t have to start from scratch with each new person they speak to, repeatedly providing order numbers and other details.
“It may sound simple, but that’s really what creates value,” O’Hara says. “That’s what makes people more likely to stay as a customer, and more likely to spend more money, and it makes them happier and more satisfied. And that’s really, from a broad perspective, the type of stuff we’re working on.”
O’Hara contends that the Covid-19 pandemic has accelerated and forced changes in data collection. While some companies may have been reluctant to embrace a “digital first” model, the pandemic has left them with little choice. The challenge remains to find balance when it comes to privacy and managing data. Prior to Covid, people at least had some control over their marketing experience, in the sense that they could choose to log on to Facebook or Instagram, or to use a phone app to make purchases. But during the pandemic, to help ensure social distancing, many daily tasks—like getting coffee—have transitioned into the digital-first approach O’Hara mentions.
“I love that I can choose to place a coffee order with Dunkin’ Donuts through their iPhone app, go pick it up at the drive-through, and not have to exchange any money to get my order,” O’Hara says.
“But that’s my choice, and I know [Dunkin’ will] be using that data to suggest new items for me to try, for example. Where things cross the line is if I’m just walking by a Dunkin’ Donuts, having never opted in to anything, and my mobile phone lights up within 50 feet and says, ‘Hey, Chris, take 20 percent off a large coffee and a donut.’
“That’s an invasion of privacy, and at that point, you’re just like Tom Cruise walking through that mall.”
{This originally appeared in Connecticut College’s CC Magazine, Summer 2021 issue)
Chris O’Hara is V.P. of Global Product Marketing at Salesforce, focusing on the data and identity suite of products including Audience Studio (a DMP) and the Salesforce CDP. A well-known speaker, pundit and author, Chris has written eight titles including six on culinary pursuits (listen to the episode for more on this fascinating jaunt in his personal journey), “Data Driven” with Krux co-founders Tom Chavez and Vivek Vaidya and “Customer Data Platforms: Use People Data to Transform the Future of Marketing Engagement,” co-written with Paleo Ad Tech co-host Martin Kihn. The latter is the #1 book on the hottest category in marketing technology today. It’s also one of the only books on the category, but let’s not quibble.
After a smoke-filled start as cigar review editor at Smoke magazine, Chris held various sales roles at publishers including MediaBistro, at the time a thriving content and job search site for media mavens, before finding his way to ad tech via start-ups such as Traffiq and nPario. The latter was an early DMP/CDP that provided the data spine for WPP agencies and Xaxis. It was launched by ex-Yahoo and SAS execs in 2010.
An encounter with Krux co-founder Tom Chavez while writing a position paper on DMPs for eConsultancy led to a position as head of DMP marketer sales for that pioneering platform. Meanwhile, Chris was a prolific writer for industry publications such as AdExchanger and his own blog, The Devil’s Work, a reference to “idle hands” (we think). Krux was acquired by Salesforce in 2016, bringing Chris to his current bivouac.
In this frolicsome episode, Marty and Jill follow Chris up the dot-com boom and back down again, as his family grows and he’s out there “hustling” for ad sales, perfecting his writing and pitching voice, and earning his ad tech pedigree. He shares what it’s like to work in a decommissioned building, how long it takes an ex-Russian Army officer to eat to a large steak, and why it’s time to break up with the third-party cookie. Don’t miss it.
https://www.ringlead.com/events/talkdatatome-ondemand/#agenda
Fun conversation about CDPs, DMPs, data quality, orchestration and the future of data with Russ Arztz, the executive chairman of Ringlead and co-founder of Computer Associates.
The Devil's Work 