A Customer Data Platform – or a CDP – is a software category first defined in 2013 by Raab Associates (headed by longtime marketing technology analyst, David Rabb). Shortly thereafter, Rabb Associates created the CDP Institute as a CDP promotional and education vehicle (and today actively manages it).
If you’re confused by CDPs and whether you need one, you’re not alone. The category includes a mixed bag of companies in a wide variety of shapes, sizes, and abilities. As of mid-November 2019, the CDP Institute listed 94 in its directory[i]. In the rest of this article, you’ll get the condensed history shedding light on the origins of CDPs, useful forensics on the category, and 3 tips to help you decide which (if any) to consider in your Martech or CX stack.
Wait, what’s a CDP?
Let’s start with the definition of a CDP from the CDP Institute:
“A Customer Data Platform is packaged software that creates a persistent, unified customer database that is accessible to other systems.” [ii]
And the CDP Institute follows this with a first-level unpacking of the definition:
“Packaged software”: the CDP is a prebuilt system that is configured to meet the needs of each client.
“Creates a persistent, unified customer database”: the CDP creates a comprehensive view of each customer by capturing data from multiple systems, linking information related to the same customer, and storing the information to track behavior over time.
“Accessible to other systems”: data stored in the CDP can be used by other systems for analysis and to manage customer interactions.
On the Origin of CDPs
In my first Martech job with UPS in 1993 we signed a contract with Harte-Hanks for their “Marketing Customer Information File,” or MCIF. Interestingly it was:
- Packaged software: Harte-Hanks sold it first to banks, and then to companies like UPS.
- A persistent and unified customer database: It included household and customer ids.
- Accessible to other systems: It had export and import capabilities.
Apparently, we bought a customer data platform at the time and didn’t even know it! And here’s the thing: Harte-Hanks’ customer database wasn’t even relational (for the techno nostalgists out there, it used a hierarchical file system). And they shipped us updates once per month, on tapes! The point is this 1993 software (according to the CDP Institute’s definition) technically qualified as a CDP, which isn’t very reassuring if you are looking for criteria to judge a vendor’s worthiness to serve your current-day customer data needs.
By the late ’90s, relational databases (with SQL interfaces) had taken over and using this technology, campaign management vendors and marketing consultancies spawned the 2nd generation of mostly hand-crafted customer data platforms known as marketing data marts (many of the original on-premise campaign management systems tapped into these). Practitioners unsatisfied with what they got from their IT partners, built these using database solutions like Oracle, IBM DB2, Microsoft SQLServer, and Teradata. Meanwhile, IT continued building out data warehouses.
Then, IT moved into the era of big data (or NO SQL) solutions and building data lakes. From warehouses to lakes, to oceans, the mentality was “store it and they will come.” But (for the most part) come they did not because IT hadn’t designed for specific business outcomes.
Nonetheless, IT had accumulated vast data reserves, and as a result, reporting firms such as Cognos, MicroStrategy, and Business Objects tapped in with general-purpose reporting and decision-support software. These evolved into the wide-class of business intelligence tools available today, including tools like Tableau.
With the advent of websites and web banner ads during the .com boom, programmatic bidding platforms needed a database to store audiences (from all those cookies and device ids) and overlayed 3rd party data. These became known as cookie pools or DMPs (Data Management Platforms). Further, on owned websites, brands started tagging and tracking visitor behavior to understand traffic patterns and in the hope of eventually providing site personalization. This spawned tag management and website personalization firms. And there you have it: the genealogy of CDPs (Figure 1).
Figure 1: CDP Family Tree
The CDP Convergence Era
By 2013, a host of factors were affecting these technologies and the data landscape:
- The cloud movement caused Campaign Management and Email Service Providers to form Marketing Clouds.
- Website personalization expanded to all digital channels.
- Tag management/web analytics became commoditized (thanks in part to Google Analytics being free).
- The DMP market slowed because cookie pools were never people-based (making it impossible to do 1:1 personalization).
- The CX revolution forced marketing, sales, and service practitioners to think more broadly about customer data and customer journeys.
- Big data management suppliers were looking for valuable use cases.
- Marketing data marts, for the most part, had been absorbed by IT’s 360 data initiatives.
- Marketers faced growing issues with the variety, volume, and increasing complexity of data.
With these dynamics at play, and to their credit, the CDP Institute attracted packaged-software businesses from various origins that qualified for inclusion in its directory. Many found this new label attractive as their existing markets softened, got saturated, or commoditized. In the ensuing five years, a handful of CDPs grew to nearly a hundred.
Late last year, Forrester’s Joe Stanhope and Stephanie Liu wrote an article entitled, “For B2C Marketers, Customer Data Platforms Overpromise and Underdeliver.” The blog promoting it summed up their view: “Marketers, these aren’t the
droids data platforms you’re looking for.” [iii] This is especially true for enterprise B2C marketers.
To further understand the complexion of the organizations that collected under the CDP umbrella, I separated them into sub-categories in terms of lineage. I analyzed the 94 companies in the CDP Institute’s directory as of November 2019 and found this distribution:
|Origins||Companies||Avg # Employees||# Bought|
|Marketing Automation (e.g., Email Provider, Campaign Management)||15||112||1|
|Data Management (e.g., Data Quality, ETL, Data Management)||12||249 *||4|
|Customer Analytics (e.g, Business Intelligence, Customer 360)||9||47||3|
|Recommendation Engine (Product, Content, Real-time Recommendations)||8||145||0|
|Tag Management / Web Analytics||5||152||1|
|Madtech / Attribution / Journey Orchestration||5||96||0|
*Removed Informatica (4700 employees) from calc to avoid over skewing
First, the 36 native CDPs:
- Most were born recently and didn’t spring from an earlier category.
- They average 71 employees; many are startups; consistent with a nascent category.
- Although some claim to have cross-industry experience most have a weight of experience in one sector.
- Three notable examples are Lytics (CPG), mParticle (Media & Entertainment), and SessionM (Restaurants)
- For many, their customer base is mid-market and/or B2B slanted and not enterprise.
- In the last 5 years, the native CDPs have become acquisition targets (bigger companies bought 7 of the 36 between 2015 and 2019).
As we saw, to qualify for inclusion in the CDP Institute’s directory, the solution must prove it persists data, yet that doesn’t mean the vendor has useful business experience with that data. And interestingly, 25 of the 94 didn’t originate as Martech vendors, but instead as general-purpose (or even sales force management) data firms. In other words: buyer beware in terms of experience with enterprise-scale, marketing use cases, and B2C data.
Some of the critical capabilities beyond the basic CDP Institute criteria to keep an eye on are:
- Data scrubbing – hygiene on bad or missing data
- Data appending – attaching net-new data attributes to an existing profile
- Data aggregation – summarizations, calculations, and pattern detection to create predictive fields for high-value use cases such as propensity to buy or churn
- Data streaming – continuous feeding of data as it’s created
- Identity resolution – device matching, stitching, and rationalization to pinpoint the person
- Data visibility and privacy – compliance, security, and preference management features
- Ecosystem connectors – Pre-built interfaces to streamline interchange with other platforms
As you wade through all the Institute’s vendors, as well as the brand-new (and untested) CDP offerings by the mega Martech vendors (Adobe’s CDP, Salesforce’s Customer 360 Truth, SAP CX Suite, Oracle’s CX Unify, and Teradata’s Vantage CX), and any others happily slapping on the CDP label, carefully inspect the above critical capabilities. And when doing so, consider these tips as you decide whether to license a CDP.
Tip #1: Feeds and Speeds Matter
As you ponder data accessibility, think about the speed of access required for real-time customer engagement. In my June article “The Final 4: Martech Platforms and Ecosystems,” I opined that one of the four linchpin platforms for effective real-time engagement should be a Customer Insights Platform (CIP), going on to compare and contrast it with a CDP. The spoiler alert is that a CIP is NOT the same, and very few CDPs qualify as a CIP.
A CIP’s primary job is to feed the right individual-level data at the right time (often in real-time) to the Acquisition and Relationship Execution platforms (details in the above article). Some of the best data to predict current intent comes from recent digital interactions. A CIP, which is a transactional platform, can’t also be a business intelligence platform. CIPs, designed to transact in real-time, access a customer profile (and the sub-strata of that data for an individual) in milliseconds not batching across them in minutes or hours. Consequently, ask yourself, “Do I need a tool for business intelligence or real-time 1:1 execution action?” If you care about real-time feeds and speeds, and the outcomes you’ll get with a well-architected execution platform, you want a CIP to feed it, and many of the CDPs won’t work.
Another crucial consideration is the latency and scalability when streaming digital channel behavior data in real-time. Notice in Figure 2 that data must flow in real-time (not batch) into the customer profile managed by the Relationship Execution Platform. Other slowly changing data, such as core customer records and product holdings, can enter periodically, and you might use a CDP as that data source.
Figure 2: Customer Insights Platform – Example data processing
Here are the CDPs from the directory with origins in Tag Management (and examples of their enterprise-grade experience)
- Celebrus – Achmea, BOA, HSBC
- Commanders Act – Credit Mutuel, Engie, Nestle
- Ensighten – OI, TUI, United
- Tealium – Cox, HSBC, Vodafone
Of the CDPs, the Tag Management vendors are best suited to capture and stream real-time digital data (handling volumes such as 5,000 transactions per second), but keep in mind some require more involved multi-page tagging to get the right behavior indicators.
Tip #2: Inventory moments of truth – focus on the data needed to detect them
Data, like oil, is useless when trapped in the ground or in crude form. Value comes from tapping into it, refining it, distilling it into a refined energy product, and dealing responsibly with its combustion and aftermath. Your job is to find detailed insights that fuel a productive understanding of customers’ behaviors, demands, and intent. Relevance happens when you react swiftly and with grace, delivering personalized offers, services, and recommendations. So, the question is, can CDPs help you with this challenge?
That depends. In “Deconstructing Customer Data Platforms – Myth vs. Reality,” [iv] the Winterberry Group concurs and cautions that “Different CDPs have different levels of expertise at managing different levels of data capture.”
When customers use websites, mobile apps, and other digital devices, they emit signals showing interest in products, completing tasks, subscribing to things, getting alerted, and interacting with their environment. If brands effectively tap into these signals and react with extraordinary timing and class, they can achieve a competitive advantage. But these moments are fleeting.
For instance, a customer searching on a site with the term “early termination fee” could be a clear sign churn is coming in minutes. A customer dwelling on a mortgage page for the second time in a day might be making a final decision right then on who gets their home loan. Subscribing to a 401k newsletter may be the first in many retirement interactions. Customers’ proximity to your store (or a competitor’s) might hint shopping is imminent.
So, make a list of these events, tap into them, store patterns of data and flags about them, and devise a way to act on them.
Tip #3: Don’t confuse CDPs with more conversation and more action
Better customer engagement and conversations don’t necessarily require more master data management. But don’t get me wrong. If you don’t have well-organized customer data, then a CDP’s data collection, identity resolution, and unification capabilities could prove useful to drive the right engagement. Yet if you are a large enterprise, chances are you have scores of ongoing data unification efforts, and what you probably need is rationalization and coordination, not another data repository.
In terms of orchestrating personalized customer conversations, several CDPs originated in real-time interaction management, or in the website, product, or content recommendation space:
|Blueshift||Content Recommendations||eLearning & Media|
|Boxever||Real-time Interaction Management||Travel & Leisure|
|Evergage||Website Personalization||Retail & Tech|
|Manthan||Product Recommendations (BI vendor that bought Rich Relevance)||Retail & CPG|
|SmarterHQ||Real-time Interaction Management||Retail|
|SymphonyRM||Real-time Interaction Management||Healthcare|
Some are good recommendation engines, and have specific areas of experience, but remember it’s not a recommendation engine you are after in the CDP area. It’s outcome-oriented customer data management. So, don’t get distracted by recommendation capabilities when what you seek is the ability to handle data feeds and speeds, find insights, and activate an execution platform to deliver at moments of truth.
If you don’t have an adequate relationship execution platform, evaluate those separately. In that process, look at the strongest real-time interaction management (RTIM) platforms that major in serving recommendations on paid and owned properties.
Like the first minute of a roller coaster ride, the CDP train is dragging us up to the hype precipice and what’s in store when it plummets down to the trough of disillusionment is unknown. No doubt, it will be fast, furious, and frightening, especially for those heavily invested in this technology. Because the CDP category is a mixed bag, very different firms will shop in the bin, some making head-scratcher acquisitions. Some CDPs will go out of business. Quite possibly, the plunge has begun, with Mastercard’s recent purchase of SessionM, D&B’s buy of Lattice, and ARM’s purchase of Treasure Data.
Focus on the use cases (and data needed) that improves your ability to serve timely, relevant, and personalized offers and services. Codify the important data and the speed it must move into your decision-making solution. If a CDP has components that help you, and you get those at a fair price, consider plugging them in. But remember, you’ll inherit redundant features, so be wary of the premium you’ll pay for those and ensure you can either use them or work around them. Further, assess how difficult it will be to pull them out should your plans change.
And if after all this you’re still confused, consider sitting on the sidelines until the dust settles, using your existing data-management technology, and watch others take the wild ride.
[iii] Forrester, https://go.forrester.com/blogs/b2c-marketers-and-cdps/,2019