Data Management is the Backbone of Enterprise Agility in CX

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?


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.


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.


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]


The Data Gravity Effect

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.

  • Authentication: As discussed in my last post, no modern CDP strategy can begin without accounting for customer identity and authentication. The first value exchange with customers in the post-cookie world is giving them a secure and trusted way to provide their data. That means making CIAM part and parcel of a first-party data strategy. There is no first-party data strategy without secure customer authentication, period.
  • Consent and Preferences: To get to the next layer of value, you also must have a scaled way of capturing detailed consent and managing preferences. This is today’s version of “permission-based marketing.” Thanks to GDPR and continuing privacy legislation, this is no longer a marketing framework, but a real requirement. Enterprise Consent and Preference Management (ECPM) enables brands to start a meaningful value exchange. As a brand, if you are giving me an offer I never asked for, you have failed at the start.
  • Data Governance and Stewardship: With great amounts of data comes great responsibility. Even brands with strong technological capability to manage authentication and preferences need a strategy that defines exactly what types of data to collect, how they are used, and who gets to access them. This is non-trivial. It goes well beyond buying software and requires the enterprise to carefully define the benefits of creating a value exchange with the customer upfront.

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]


The Five Vs of Data Virtuosity

Rethinking enterprise data management in our new normal

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?”


The New Era of 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]


Connecticut College Magazine

Chris O’Hara ’90 of Salesforce is helping companies ethically navigate the evolving world of digital marketing and consumer data.


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)


Paleo Adtech Podcast

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.


CDP and the “Five Vs”

I jumped into my friend’s amazing blog to write about the “five Vs” that every CDP needs to be successful. Read the full article on Vala’s ZDNet blog!

The rise of the customer data platform has been interesting to watch. CDPs are an exciting new software category, and most progressive organizations are looking at them as a way of solving some fundamental business challenges — how do you get a “single source of truth” for customer data, when customers create so much of them? Data, that is.

Endless advertising and martech software acquisitions, patched together through brittle “data extensions” and manual integrations lead to many differing views of customers, mostly centered on what channel they are engaging on. Companies tend to have a “marketing” customer they can understand through interactions in e-mail, an “advertising” customer they know through pseudonymous online interactions, and “sales” customers they understand by their profile in a CRM system. Connecting those identities into a rich profile can unlock a lot of value.

Imagine if the call center employee, for example, could have access to a rich profile of every customer that included her recent purchases, loyalty status and points, marketing interactions, and lifetime value score? You might be able to have a real, personalized interaction rather than reading from a canned call script. Imagine further, if the system was smart enough to assign an inbound call priority based on those data attributes, such that a “Platinum” loyalty member got routed to the local call center, rather than the overseas location? Better, more personalized, service. Less customer churn. The possibilities are endless!

The good news is that this is happening today. Large enterprises with sophisticated IT departments, in-house developers, and large software budgets are connecting these systems together to create such results. The bad news is that it’s very expensive, requires constant vigilance and development to keep it working, and its dependent upon licensing solutions from dozens of software vendors for data ingestion to data activation, and everything in between.

The other problem is that this innovation seems to be aimed solely at marketing use cases today. Despite the fact that 80% of companies we surveyed in our State of Marketing Report say they have already begun to connect their marketing and service systems, today’s CDPs seem to be narrowly focused on marketing, advertising, and personalization use cases. But why stop data management there?

If you are embarking on a true data management journey and want some guideposts for building a system that can truly connect your entire enterprise at the data platform layer (where it counts), there are five critical things to think about:

Your systems need to manage a high volume of data, coming in at various speeds. Some data, such as CRM and legacy enterprise system data is slower moving, and generally comes in via batch mode, in the form of tables. These are things like customer records, purchase history data, and the like. But there’s a lot of data that needs to some into the system in real time. A online customer looking for a local store where they can apply an offer they received is information happening in real time that can be applied to real world use cases. Unless you can read and react to that signal quickly, they are likely find the nearest competitor. So having a system that can handle data at many different speeds is a requirement, especially as more and more signals are created from real time and real life interactions.

You then need to map first-party data into a single information model. Data silos, as discussed above, are just the tip of the iceberg. It’s obvious that connecting marketing, advertising, and CRM systems can create new use cases that drive business value, but the true underlying issue isn’t the systems themselves, but how they store data. One system labels a first name as “First_Name” and another as “FirstName.” It seems trivial, but every system has a slightly different main identifier or “source of truth,” and the goal is to have one. This starts with being able to provision a universal information model, or schema, which can organize all of the differently labeled data into a

common taxonomy. Companies are starting to organize around a Common Information Model (The “Cloud” Information Model for companies like Google, Amazon, and Salesforce) as a way of creating a Rosetta Stone for data.

Companies must ensure they can provision a single, persistent profile for every customer or account. Social media systems think of your social “handle” as your primary identifier. E-mail systems use the e-mail address. DMPs usually see people as cookies. Every system is somewhat different. How do you get a “single source of truth” for people data? All of these identity types need to roll up to a rich profile or universal ID. This gets resolved in “known” PII data by making sure one person is the same among many different e-mail and postal addresses, as an example. In the digital world, where people tend to have dozens of cookies and device IDs, these identifiers also need to be mapped to the universal ID. It’s a hard problem to solve, but a system that has a strong identity spine is the only way to get there.

Once you manage to resolve and identify data from many different sources and systems, you end up with…a lot of data. It has been theorized that, in 2020, 1.7MB of data was created every second for every person on earth. That’s hard to fathom, but it’s a problem that is not going away in a world that increasingly values every click, call, and video view. If you want to use those interactions to form the basis of your digital engagement strategy, you have to store them somewhere. That necessitates a system that can handle billions of data attributes, millions of rows, and thousands of interconnected tables. Machine learning works best when pointed at petabytes of analytic data. Your system needs to be built for a world in which more data is created every day, and there are more systems that require them to work well.

The real question is, how do you make data actionable in every channel — marketing, sales, service, commerce, and analytics — and get tangible value from them? Once you have a clean, unified set of scaled data there are many ways to derive value from it. Segmentation tools can pull data from any source and stitch it into scaled groups of addressable customers. Analytics tools get more powerful when analyzing a robust and comprehensive dataset whether for BI or for media analytics. The best part? AI systems get more powerful. Success in machine learning is not about the algorithms — it’s about giving them the ability to run across a highly scaled, true, set of data that creates results.

If you are thinking about starting your company’s digital transformation journey with a CDP or an enterprise data management system, the five Vs are a great framework for success.


Data Driven Wins the Axiom Awards!

DATA_DRIVEN_AXIOMProud to announce that my book, Data Driven, has won the 2019 Silver medal for best Business Technology book!

In August of 2007, Jenkins Group launched the Axiom Awards, “recognizing and promoting the year’s best business books.” Now, 12 years later, they have announced the winners of the 2019 Axiom Business Book Awards, honoring this year’s best business books, their authors, and publishers.

The Axiom Business Book Awards are intended to bring increased recognition to exemplary business books and their creators, with the understanding that business people are an information-hungry segment of the population, eager to learn about great new books that will inspire them and help them improve their careers and businesses.

Data Management Platform · DMP · Platforms

The Identity Based Data Platforms of the Future

Today’s disparate traditional databases and connected devices make “people-based” marketing as difficult and awkward as this interaction. 

Currently, marketers don’t have a single source of truth about their consumers. Tomorrow, there must be a single place to build consumer profiles with rich attribute data, and provisioned to the systems of engagement where that consumer spends their time.

At a recent industry event, we heard a lot about the upcoming year in marketing, and how data and identity will play a key role in driving marketing success.

As a means to master identity, some companies have heralded the idea of the customer data platform (CDP), but the category is still largely undefined. For example, many Salesforce customers believe that they already have a CDP. The reason? They have several different ways of segmenting known and unknown audiences between a data management platform (DMP) and CRM platform.

In an article I wrote here last year, I introduced a simple “layer cake” marchitecture, describing the three core competencies for effective modern marketing. In such a fast moving and evolving industry, I have since refined it to the core pillars of identity, orchestration and intelligence:

marchitecture cake

With this new marchitecture, brands have the ability to know consumers, engage with them through each touchpoint and use artificial intelligence to personalize each experience.

Mastering each layer of complexity is difficult, requiring an investment in time, technology and people. Lets focus on perhaps the most important – the data management layer where the new CDP category is trying to take hold.

The next wave of data management

By now, it’s safe to say marketers have mastered managing known data. A few years ago, when I was working for a software company that also managed postal mailing lists, I was astonished at the rich and granular data attached to mailing lists. There is a reason direct mail companies can justify $300 CPMs – it works, because direct marketers truly know their customers.

After joining Salesforce, I was similarly awed by the power to carefully segment CRM data, and provision journeys for known customers spanning email, mobile, Google and Facebook, customer service interactions and even community websites.

How can we get to this level of precision in the world of unknown (anonymous) consumer data?

As marketing technology and advertising technology converge, so must the identity infrastructure that underlies both. Put more simply, tomorrow’s systems need a single, federated ID that is trust-based. Companies must have a single source of truth for each person, the ability to attach various keys and IDs to that unified identity, as well as have a reliable and verifiable way to opt people out of targeting.

Let’s take a look at what that might look like:

federated ID

This oversimplification looks at the various identity keys used for each system and the channels they operate in. Today, the CRM is the system of record for engaging consumers directly in channels like direct mail, email campaigns and service call centers. The DMP, on the other hand, is the system of record for more passive, anonymous engagement in channels like display, video and mobile.

When consumers make themselves known, they “pull” engagement from their favorite brands by requesting more information and opting into messaging. At the top of the funnel, we “push” engagements to them via display ads and social channels.

As a marketer, if you have the right technologies in place, you can seamlessly connect the two worlds of data for more precise consumer engagement. The good news is that, martech and adtech have already converged. Recent research from Salesforce shows that 94% of marketers use CRM data to better engage with consumers through digital advertising, and over 91% either already own or plan to adopt DMP over the next year.

So, if mastering consumer identity is the most important element in building tomorrow’s data platform then what, exactly, are the capabilities that need to be addressed? There are three:

1. A single data segmentation engine

Currently, marketers don’t have a single source of truth about their consumers.

Here’s why: Brands build direct mail lists and email lists in their CRM. Separately, they build digital lists of consumers in a DMP tool. Then, they have lists of social handles for followers in various platforms like Facebook and Twitter. Consumer behaviors like browsing and buying that happen on the ecommerce platforms are often not integrated into a master data record. And distributed marketing presents a challenge because a big mobile company or auto manufacturer may have thousands of franchised locations with their own, individual databases.

Segmentation is all over the place. Tomorrow, there must be a single place to build consumer profiles with rich attribute data, and provisioned to the systems of engagement where that consumer spends their time.

2. Data pipelining and governance capabilities

This identity layer must also have the ability to provision data, based on privacy and usage restrictions, to systems of engagement.

For example, when a consumer buys shoes, they should be suppressed from promotions for that product across all channels. When a consumer logs a complaint on a social channel, a ticket needs to be opened in the call center’s system for better customer service. When a person opts out and chooses to be “forgotten,” the system needs to have the ability to delete not only email addresses, but hundreds of cookies, platform IDs and other addressable IDs in order to meet compliance standards with increasingly restrictive privacy laws and, more importantly, giving consumers control over their own data.

Finally, marketers need the ability to ingest valuable DMP data back into their own data environments to enrich user profiles, perform user scoring, as well as build propensity models and lifetime value scores. This requires granular data storage, fast processing speeds and smart pipelines to provision that data.

3. Leaping from DMPs to holistic data management

Ad technology folks are guilty of thinking of cross-device identity (CDIM) as the definition of identity management. Both deterministic and predictive cross-device approaches are more important than ever, but in a world where martech and adtech are operating on the same budgets and platform, today’s practitioner must think more broadly.

Marketers can no longer depend solely on another party’s match table to bridge the divide between CRM and DMP data. A more durable, and privacy-led connector between known and unknown ID types is required. Moreover, when they can, marketers need the ability to enrich email lists with anonymous DMP attributes to drive more performance in known channels—now only possible when a single party manages the relationship.

These three tenets of identity are the starting point for building the data platform of the future. The interest and excitement around CDPs is well placed, and a positive sign that we are evolving our understanding of identity as the driving force behind the changes in marketing.

[This article originally appeared in Econsultancy’s blog on 2/1/2018]

Data Management Platform · DMP

What is the future of DMPs?

In the 1989 film “Back to the Future II,” Marty McFly traveled to Oct. 21, 2015, a future with flying cars, auto-drying clothes, and shoes that lace automatically. Sadly, none of these things happened. 

What is the future of data management platforms? This is a question I get asked a lot.

The short answer is that DMPs are now part of larger marketing stacks, and brands realize that harnessing their data is a top priority in order to deliver more efficient marketing.

This is a fast-moving trend in which companies are licensing large enterprise stacks and using systems integrators to manage all marketing—not just online advertising.

As detailed in Ad Age (Marketing clouds loom), the days of turning to an agency trade desk or demand side platform (DSP) to manage the “digital” portions of advertising are fading rapidly as marketers are intent on having technology that covers more than just advertising.

Building consumer data platforms

A few years ago, a good “stack” might have been a connected DMP, DSP and ad server. A really good stack would feature a viewability vendor and start a dynamic creative optimization (DCO). The focus then was on optimizing for the world of programmatic buying and getting the most out of digital advertising as consumers’ attention shifted online, to mobile and social, rather than television.

Fast forward a few years, and the conversations we are having with marketers are vastly different. As reported in AdExchanger, more than 40% of enterprise marketers license a DMP, and another 20% will do so within the next 12 months. DMP owners and those in the market for one are increasingly talking about more than just optimizing digital ads. They want to know how to put email marketing, customer service and commerce data inside their systems. They also want data to flow from their systems to their own data lakes.

Many are undertaking the process of building internal consumer data platforms (CDPs), which can house all of their first-party data assets—both known and pseudonymous user data.

We are moving beyond ad tech. Quickly.

Today, when those in the market are considering licensing a “DMP” they are often thinking about “data management” more broadly. Yes, they need a DMP for its identity infrastructure, ability to connect to dozens of different execution systems and its analytical capabilities. But they also need a DMP to align with the systems they use to manage their CRM data, email data, commerce systems, and marketing automation tools.

Data-driven marketing no longer lives in isolation. After I acquire a “luxury sedan intender” online, I want to retarget her—but I also want to show her a red sedan on my website, e-mail her an offer to come to the dealership, serve her an SMS message when she gets within range of the dealership to give her a test drive incentive, and capture her e-mail address when she signs up to talk to a salesperson. All of that needs to work together.

Personalization demands adtech and martech come together

We live in a world that demands Netflix and Amazon-like instant gratification at all times. It’s nearly inconceivable to a Millennial or Generation Z if a brand somehow forgets that they are a loyal customer because they have so many choices and different brands that they can switch to when they have a bad experience.

This is a world that requires adtech and martech to come together to provide personalized experiences—not simply to create more advertising lift, but as the price of admission for customer loyalty.

So, when I am asked, what is the future of DMPs, I say that the idea of licensing something called a “DMP” will not exist in a few years.

DMPs will be completely integrated into larger stacks that offer a layer of data management (for both known and unknown data) for the “right person;” an orchestration layer of connected execution systems that seek to answer the “right message, right time” quandary; and an artificial intelligence layer, which is the brains of the operation trying to figure out how to stitch billions of individual data points together to put it all together in real time.

DMPs will never be the same, but only in the sense that they are so important that tomorrow’s enterprise marketing stacks cannot survive without integrating them completely, and deeply.

[This post was originally published 11 May, 2017 by Chris O’Hara in Econsultancy blog]