Category: CDP

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To CDP or NOT – 3 tips – then you decide

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).

CDP

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).

CDP Family Tree

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
Native CDP 36 71 7
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
Lead Management 4 68 1

*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.

Customer Insights Platform

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:

Vendor Origins Major experience
Blueshift Content Recommendations eLearning & Media
Boxever Real-time Interaction Management Travel & Leisure
Evergage Website Personalization Retail & Tech
Jahia Content Recommendations  
Manthan Product Recommendations (BI vendor that bought Rich Relevance) Retail & CPG
NectarOm Content Recommendations  
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.

Conclusion

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 freighting, 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. 

[i] CDP Institute, https://www.cdpinstitute.org/directory, 2019

[ii] CDP Institute, https://www.cdpinstitute.org/cdp-basics, 2019

[iii] Forrester, https://go.forrester.com/blogs/b2c-marketers-and-cdps/,2019

[iv] Winterberry Group, https://www.winterberrygroup.com/our-insights/deconstructing-customer-data-platforms-myths-vs-realities, 2019

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CRM Magic or Smoke and Mirrors?

Old stuff is commonly stamped as long in the tooth, antediluvian – to be face-lifted, remade, or simply discarded after years of service.

Amazingly in the CRM world, some things that never even get full adoption or wide-spread use, still get per annum marketing make overs – no doubt aimed at luring buyers with brand new fairytale names and future promises.   Take for instance CDPs (Customer Data Platforms), modules offered by most of the CRM vendors.

Is it truly CRM magic or just hocus pocus?

Genie, the latest announcement by Salesforce, is a recent example of this trend and hard to size up.  Is it just a new data model or worse, just a fancy new name for an existing CDP product?  Or is it really a new & shiny customer data platform?  Or is it something different?  Perhaps a bundle of existing offerings with some minor enhancements.  As always, time will tell.  When the smoke clears, will we realize there’s nothing new and exciting available today – but instead just new promises. 

Salesforce isn’t the only vendor guilty of polishing old code, announcing ahead of the curve, or re-packaging existing product with new marketing wrappers and new names.  Many other web analytics, content, and data management vendors are constantly renaming products to jump on messaging bandwagons to announce the next magic potion.

Reading the headlines, here’s the takeaways so far on Salesforce Genie:

  • Salesforce suggests it’s closer to assembling and updating customer profiles in real-time now, but it’s not real-time.   Commonly accepted definitions of real-time are that processing happens in under 1 second.  But at Dreamforce we heard, “real-time is 5 min ago not 5 days ago.”  
  • The Marketing Cloud Genie seems to be a bundle of the Salesforce CDP, Personalization (Evergage), Engagement (Journey Builder and Email Studio), and Intelligence (Datorama).
  • There is a direct integration with Snowflake which sounds interesting but unfortunately not much detail was provided.
  • Amazon Sagemaker can directly access Genie data.  This could benefit data scientists working in this tooling, to get data prepped easier and faster for model building purposes.
  • Einstein powered AI-content selection was discussed.  Is Einstein considered part of Genie?  Not clear.  This allows personalizing the content selected based on a consumer’s location & associated weather data.    
  • It’s not clear how Salesforce will price Genie.
  • Einstein Engagement Frequency Reporting with “What If” analysis – this is depth of file analytics (how many targets to include in campaigns) and fatigue reporting – the announcement of a “what if” capability allows for some basic scenarios to be run.
  • Salesforce users can now bulk import customers (called contacts in Salesforce lingo) into Salesforce Engagement.  
  • Various enhancements to Salesforce Intelligence (Datorama) were announced, including a control center for data governance.

All of this, including the last two points beg a major question.  How many CDPs does Salesforce have now?  By one count there may be as many as four:

  1. Salesforce CDP (formerly called Salesforce 360)
  2. Marketing Cloud Engagement Datastore
  3. Marketing Intelligence Datastore
  4. Genie (which by some accounts, may include an upgrade of some of the Evergage CDP capabilities)

Some tips:

Instead of banking on promises and new names, focus on outcomes and what can be achieved with proven solutions.  Chasing wet behind the ears data management technology, or worse vaporware, can be expensive, frustrating, and fraught with tremendous opportunity costs.  We should have learned by now that data management technologies in and of themselves won’t return value.  Build it and they will come doesn’t work.  You need good data, but it alone has no value until you activate it.  And you’ll need the right decision engine tightly integrated with it to get value.

Instead of the marketing headlines and superficial news stories, look for product documentation and actual training materials that describe the actual GA product, how it’s configured, and what features it contains.

Look for real customer accounts of using the software and the value they got in return.

Read crowd review sites, such as Trust Radius, G2, and Gartner Peer Insights to get real user feedback.

In summary, buy real working products not promises.

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CDPs Then & Now – The Customer ID (Identification & Data) Problem

In November 2019 perhaps you caught this article: “To CDP or NOT – 3 tips – then you decide.”  The main takeaway – the CDP space is a quasi-market with a mixed bag of firms coming from different lineages and different levels of capability, maturity, and focus.  The conclusion: buyer beware and standby.

That was BC – Before COVID-19.  Since then, what hasn’t changed about the world?  And like everything in 2020, the CDP market was not immune to upheaval.  And although the basic premise for adding a CDP into the Martech stack is still the same:

  1. Help resolve customer identity
  2. Rationalize and manage customer data
  3. Make that data accessible to other systems

…what’s changed are the vendors involved, and their core and extended capabilities, which are substantially different nearly three years later.

Most markets appear as nebulous categories, and the CDP market was no exception.  But as buyers and vendors evolve, dust settles, and the picture becomes clearer.  Still, two important aspects of what a CDP should supply loom large and are worthy of close inspection.  Namely, providing customer recognition/identity management and distilling the right (and righteous) customer data into meaningful insights.

Considering those key features, let’s explore a few of the big changes since November 2019:

  • The huge marketing cloud players entered the market:  Adobe, Oracle, and Salesforce
  • More consolidation took place, with small CDPs swallowed up by an interesting mix of companies
  • Perilous new milestones reached for third-party cookies and stealth consumer tracking

Stick with me.   You’ll get insight into these three changes, three tips, and some final thoughts.

Marketing Cloud Titans Enter CDP Fray

Adobe Real-Time CDP

In early 2020, Adobe entered the CDP ring with Adobe Experience Platform’s Real-Time CDP, promising to “Combine all individual and company data — internal and external, known and unknown — into a standard taxonomy that can be activated in real-time.”[i]  A tall order indeed.

Although certainly set up to collect digital data by way of Adobe Launch & Analytics, Adobe’s aggressive mission to combine “all data” for B2C and B2B across known and unknown, lacks focus and gives reason for pause.

Adobe has fared well in providing digital marketing data & support for early-stage customer journey activity, with its first-generation web analytics and tag management (by way of its Omniture acquisition over 10 years ago), followed by its subsequent purchases of Demdex (third-party cookie data-management platform), and marketing automation firms like Neolane (B2C) and Marketo (B2B).  Yet with the third-party cookie tracking foundation crumbling as the final browsers outlaw it, they’ve had to look for another way.  So far, that appears to be using CNAME record cloaking, which in effect is just a clever DNS hack to circumvent gaining explicit permission to track. 

The ultimate jury and judge (the consumer) may not approve of this tactic (once they discover it).  Further, with a shortage of direct access to first-party behavior data, customer analytics depth, and channel breadth, Adobe still struggles to develop deep customer understanding and natively/performantly enrich its customer profile.  And other than collecting raw digital data in real-time, not much else about Adobe’s CDP is real-time and insightful.  Adobe nonetheless plows forward with bold statements of real-time and unity that potential CDP buyers should take with a grain of salt.

As a major marketing cloud player, Adobe will eventually amass more digital data, improve its signal detection, and get more apt at activating those signals and audiences in acceptable ways.   But for now, buyers should beware of completeness claims, tracking practices, data feeds and speeds, and external integration features.

Oracle’s CDP

Interestingly, googling with the term “Oracle CDP” yields a top result pointing to an Oracle whitepaper-like webpage espousing that a “customer data platform (CDP) is software that collects and unifies first-party customer data.” [ii]  So far so good.  

Reading on, the article mentions “first-party data” 11 times, never mentioning third-party data until the final punchline at the end, where the author claims that a Customer Intelligence Platform (CIP) is different from a CDP because it “incorporates anonymous, third-party data as well as first-party data.”    It’s here that Oracle tries to differentiate its CDP, Oracle Unity, from all others.  That differentiation attempt falls flat, and is oddly fascinating on three fronts:

  • Oracle has almost no choice but to take this approach, since it spent $400m on BlueKai in 2014, one of the world’s leading third-party data trackers.  As such, Oracle wants the buyer to believe they get a premium from contracting with a CDP that can merge third-party data.
  • Oracle claims it’s not really a CDP, but instead differentiates as a Customer Intelligence Platform (CIP), and not just for marketing.   Amusingly, in my June 2019 article I advocate for a CIP – The Final 4: MarTech Platforms and Ecosystems –  yet with the middle letter short for insights about individuals attainted from first-party data, not general intelligence.  Very different CIPs indeed.
  • The reason for the demise of the cookie-based cottage industry and third-party data is that it was built on a house of cookie cards, gathering and brokering consumers’ data without explicit permission, and inherently unreliable as a good proxy for consumer intent & behavior – one of the major tenants for a CDP.

Given this, be careful with Oracle’s CDP (or CIP) solution, with its bias toward third-party data, paid media channels, and early-stage acquisition use cases.  Purchasing one means buying into the value of third-party data and acquisition use cases, while not solving for data-driven, real-time 1-1 customer engagement use cases, deeper into the relationship, on owned channels.

Salesforce’s CDP

Late in 2019, as the virus was unknowingly spreading, Salesforce began spreading the news about its new Customer 360 Truth, claiming it had a product with “a new set of data and identity services that enable companies to build a single source of truth across all of their customer relationships.“ [iii]  And although at the time they didn’t call it a CDP, they were quacking as if it were one, and funny enough in April 2021 relaunched it as a CDP. [iv]

In 2019, in classic Salesforce fashion, they announced a not-ready-for-prime-time CDP-like product, C360, with pages of fine print.  Like a theater stage with a kitchen viewed from afar, it might have appeared fully equipped.  However, on closer inspection, some of the supposed appliances were but props with no cords to plug in, no motor to run them.

And even on re-launch in May 2021, they simply slapped existing separate products such as Tableau and Mulesoft onto the wrapping paper of the Salesforce CDP.  Further, like most CDPs (except ones that come from the web analytics space, such as Tealium and Celebrus) everything is based on creating customer segments and sharing those in less than real-time for activation instead of taking an individual personalization approach and sharing in real-time.

Thus, rip off the cartoon marketing wrappers, and look inside the box and inspect all the parts for function and fit before buying.

CDP Market Consolidation

In addition to the entrance of the above big three, Microsoft and SAP also announced CDP solutions.  Before November 2019, 18 acquisitions took place. Since November 2019, 8 more further transformed the CDP landscape:

  • IgnitionOne bought by Zeta Global – December 2019
  • AgilOne bought by Acquia – December 2019
  • Evergage bought by Salesforce – February 2020
  • Segment bought by Twilio – October 2020
  • Exponea bought by Bloomreach – January 2021
  • BlueVenn bought by Upland Software – March 2021
  • Boxever bought by Sitecore – March 2021
  • Zaius bought by Optimizely/Episerver – March 2021

What’s the takeaway?  Dust is still flying in this market.  And if you are betting on one of the 100+ vendors calling themselves a CDP to plug key gaps, especially in foundational areas such as identity & data management, consider whether their future is secure, and they’ll continue to go in the same direction, as it could impact yours.

The Calamitous Cookie Crisis – Customer Identification and Tracking

In January 2020, Google announced plans to end support for third-party cookies in Chrome in two years.  Late-breaking news is that in June 2021, Google said they will delay until the middle of 2023.  But cookiepocalypse is still coming.  With less than two years until that deadline, ad-tech companies, and ad agencies alike are scrambling to find workarounds for web behavior identification and tracking. 

Case in point – The Trade Desk and ad agency Publicis (who bought the database marketing firm Epsilon in 2019) are teaming on a digital advertising solution built around the new open-source identification scheme called Unified ID 2.0.  Initially developed by The Trade Desk, Unified ID 2.0 obfuscates a consumer’s email address, using a technical hashing technique to protect consumer privacy.[v]  

As of May 2021, The Trade Desk says it already has over 170 million profiles obtained with consent.  But long-term success depends on an even bigger pool of email addresses (e.g., more consumers opting in than opting out), and that means enough publishers adopting the standard, and obtaining consumer consent.  Since history has shown consumers will opt-in without reading terms and conditions, it may have hope, especially in places like the US and Asia, so stay tuned.  My advice – read before you click, as it’s essentially agreeing to be a target of every participating company. 

In addition, SAP and Akamai bought traditional sign-on companies Gigya and Janrain respectively, going the route of obtaining social sign-on solutions to gain access to customer identification and tracking capabilities.  And although Okta, who acquired rival Auth0 in May 2021, hasn’t called itself a CDP (yet), they are a force in the customer authentication and identity space.  

What does this have to do with CDPs?   Well many ad-tech companies, formerly calling themselves data-management platforms (DMPs) during the third-party cookie era, now claim to be CDPs.  Keep in mind, however, they built their solutions to manage third-party data and cookies and to target based on these spurious methods, and not on first-party data and known identities.  Ultimately, without a strong first-party data foundation, those DMP CDPs have a limited shelf-life and are poor investments.

CDP Selection Tips

Tip #1 – Study their specialty

Keep in mind that all vendors started with a core offering.  That tells a lot about what they’re probably good at.  When interviewing a job applicant, there’s a reason why we inspect someone’s background (work history, school they attended), as it gives insight into how they’ve honed their craft. 

No vendor (not even the big ones) will be able to supply best-of-breed capabilities to handle all stages of a journey, from the anonymous browsing steps to phases deeper in an authenticated relationship.  Nor will they be able to major in more than a handful of the dozen or so capabilities the collective CDP market covers:

  1. Data collection
  2. ETL – Extract, transform, load (including cleansing and householding)
  3. Identity stitching and management
  4. Real-time data insights
  5. Predictive analytics
  6. Recommendations and decisioning engines
  7. Journey (cross-channel) orchestration
  8. Owned channel marketing automation and e-message services
  9. Digital advertising
  10. General (business intelligence) customer data activation
  11. Internal query, reporting, dashboards, and attribution analysis

Most native CDPs came up focusing on one or more of the first 3.  And with no official CDP magic quadrants or waves by major analyst firms, many others have conveniently slapped the CDP label on themselves. So, decide where you have the biggest capability gaps and needs along the customer journey, where a data-driven solution will drive better outcomes and more value, find matches, and select accordingly.  Also, if gaps exist mainly in areas 4 – 10, look beyond the CDP market, as there are a multitude of vendors not calling themselves CDPs that major in these areas.

Tip #2 – Demand real-time response times

When considering the claim of “real-time,” (which is a critical capability to take CX to another level) look beyond single components, such as the speed of data collection, or placing data onto a customer profile record.  Instead, inspect the entire data/event -> insights -> decision journey and ask:

  • “Can that entire trip be accomplished in an SLA (Service Level Agreement) under 200 milliseconds?”
  • “Can the vendor do that at scale, for millions of customers and 1000’s of interactions per second?”

Why 200 milliseconds you ask?  Because as a consumer, do you want websites you use to be slower?   As a person responsible for the website, will you allow anything new to slow down page loads?   I bet the answer to both is no.   So if your new CDP is going to play a role in providing better real-time digital experiences, it better not take up much of the two-tenths of a second response time budget.

Tip #3 – Demand real-time insights

Look for a CDP that can supply real-time data insights, with a library of these for your industry.  This looms so largely in reaping unfair benefits from a CDP investment because not many CDPs do this, and it’s how you’ll move the needle on customer experience.  Can you do this today?  Can you find customer behavior diamonds in the deep mines of digital data, surface it, polish it, and immediately pass it to a customer decision hub?  Not many can.

For instance, detecting consumers’ heightened but fleeting interest in specific products, refining that raw data into curated signals, passing them to a decision engine in real-time, so it can trigger special and immediate actions.  Very few CDPs can do these things – in that order – fast enough.  An example: a consumer on a banking website, researching mortgages [again] in the final stage of selecting a mortgage provider. 

So, look for a CDP that can solve this problem. There aren’t many.  You’ll add something special and unique that few can do.  Celebrus is one solving this exact problem:  collecting the right behavior data, making sense of it in the form of a signal library, passing those signals to a decision authority in real-time, so it can act in the moment.

Conclusion

Big is not always better, but it’s always bigger.  And although selecting a large outfit as a CDP provider gives some assurance that the solution will be around in a few years, that doesn’t necessarily equate to the best CDP solution.  Doing business with a mega-CDP vendor rarely means faster, more seamless interfaces and deep expertise.  On the contrary, expect bigger integration costs, longer wait times, custom work, and more patience required.  And if selecting a big CDP is for “one throat to choke,” try finding that elusive throat inside a tech behemoth with 50,000 employees who have swallowed up 20 companies on the way to building their marketing stack and CDP.

Conversely, using a smaller player has its tradeoffs.  Besides the risk of being bought, or folding up, inevitably their capability focus will be esoteric.  So, carefully inspect core competencies. Look for a CDP that compliantly tracks customers, collects data in real-time, has a signal library fit for purpose, and can interoperate with a decision hub. That way, you’ll get differentiation leading to better customer experiences from your CDP investment.

[i] Adobe.com, https://business.adobe.com/products/real-time-customer-data-platform/RTCDP.html, June 2021

[ii] Oracle.com, https://www.oracle.com/cx/customer-data-platform/what-is-cdp/, June 2021

[iii] Salesforce.com, https://www.salesforce.com/news/press-releases/2019/11/19/salesforce-announces-customer-360-truth-a-single-source-of-truth-for-every-customer-across-the-worlds-1-crm-2/, November 2019

[iv] Salesforce.com, https://www.salesforce.com/news/stories/salesforce-cdp-innovations-make-customer-interactions-smarter*/, May 2021

[v] The Wall Street Journal, https://www.wsj.com/articles/publicis-groupe-signs-on-to-use-trade-desks-alternative-to-cookies-11617883217, April 2021

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Don’t fall into the “we need a CDP first” trap

Introduction

Over the last three decades, marketers and customer experience experts learned the importance of employing data in data-driven customer decision making.  With the right data, they realized, machines could assist them in running better programs.  The result was more customers receiving relevant offers, and in turn leading to improved response rates and increases in customer satisfaction and retention.

This journey, however, wasn’t short on painful and costly lessons.  Stories were common of virtually endless data warehouse projects seriously behind schedule and overbudget.  In some of the most infamous cases, $10’s of millions were spent over years, with little to show for it.  Why?  Because from the onset the goals were misguided, and in many cases the wrong people drove the project. 

What went wrong?  Simply put, project sponsors set out with the wrong sequencing of goals – trying to solve for the ultimate data repository first and putting the most important aspect, who would use it and how, on the back burner.  In other words, they set the priority on sourcing data, cleaning it, and structuring it, and put off concerns on which applications would leverage it.  Build it, they posited, and they will come.

Challenges:

Sadly today, many embarking on CDP projects are falling into this same trap: 

Select the best Customer Data Platform (CDP) first, build it to solve for nagging problems of fragmented data and cross-device identity.  Later, help customer decisioning applications get connected to it.

The problems with this approach are:

  1. Without considering first which specific outcomes are crucial to success and working back to the data needed to support those, chances are extremely high the CDP won’t have the right data.
  2. History shows it could take years to agree on the right data, amass, cleanse, stitch, and organize it into a brand-new platform.
  3. Nearly every vendor calling themselves a CDP is now also claiming to solve for enterprise customer decisioning requirements.  Yet selecting the same vendor for both means a direct dependency on this repository, where the CDP must be up and running before the business can run its first new customer engagement programs.

Twenty years ago, at Unica, we saw this exact same problem.  The business was waiting for IT to complete the never-ending data warehouse project.  Or worse, they took matters into their own hands and selected a tool like Epiphany that required all the data structured and uploaded into its marketing data model (essentially a CDP – just not called it at the time).   Sound familiar? 

Again at Unica, to tackle this problem, we designed a different solution and approach.  We called the solution UDI (Universal Data Interconnect) which allowed marketers to map to existing data sources and run campaigns leveraging that data in place. 

We advised frustrated clients to set goals such as improving promotional response rates and urged them not to wait for data warehouse projects to complete.  The advice we gave them –  focus on redesigning campaigns, use advanced analytics to improve lift, and connect only to data sources required for those redesigned campaigns.  Essentially, let the new campaign rules drive the data source requirements.  References reported running successful campaigns shortly after project inception.  In just months they touted tangible economic benefits, bolstering their case to expand rollout.

CDPs are all the rage – what should I do?

First, the fact that CDPs are “all the rage” is part of the problem.  Upon closer inspection it’s the CDP vendors generating the hype, and not the paying clients.  Oddly missing are stories of resounding project success and massive ROI, and instead infamous stories of CDP projects failing to meet goals are piling up.  In Gartner’s 2021 Cross-Functional Customer Data survey, just 14% of respondents that reported having a CDP also reported achieving a 360-degree view. [i]  What we’re witnessing is the classic Gartner technology hype cycle, with CDPs now passing peak hype, and falling into the trough of disillusionment. [ii] 

In my 2019 article, To CDP or NOT – 3 tips – then you decide, the advice was beware of the hype in a poorly defined market.  Now, in 2022, vendors are trying to differentiate in a still nebulous market.  Here are some of the CDP subcategories that have emerged since 2019 [iii]:

CDPs selected primarily by Marketing and Business buyers:

  • Smart Hubs / Hub & Spoke CDP
  • Real-Time CDP
  • Marketing Cloud CDP (e.g., Adobe AEP, Salesforce CDP)
  • Campaign & Delivery CDP

CDPs selected primarily by IT, Data, and Analytics buyers:

  • Data Integration and Management CDP (focused on data collection and identity management)
  • CDP toolkits (used by IT to build a CDP)
  • Customer Analytics & Insights CDP

Certainly, the right answer isn’t to buy multiple CDPs.  Yet that is exactly what’s happening.  And for larger enterprises, some are buying as many as three, simply proving poor alignment between the business and IT. [iv]  Having lived through those days, be assured, the result is not alignment on outcomes, rapid access to the right data, and improved customer experience.  

At the same time, the right answer isn’t to let the business (or IT) solely determine the selection.   Although the business must have primary responsibility and control, it also must tightly collaborate with IT where both parties understand their roles and stick to them.  Though unfortunately not common, brands that get this right, and take inventory of what data & systems they have and what roles each party should play, report better success and ROI.  As such, follow these rules:

Do –

  1. Establish a strong partnership between the business & IT, align on use cases, outcomes, and how to measure success. Take inventory of existing capabilities and chart a roadmap together.
  2. Work back from the highest value use cases and desired outcomes and map out the data needed to support them. 
  3. Make it a requirement to be able to iteratively add to the data repository, as new programs might demand new data sources.  It won’t be instantaneous (think in terms of quarterly releases for production data source changes).
  4. Insist that the decisioning and execution capabilities and the CDP solution be evaluated on their own merits, and if in the end different vendors provide what’s best and can be integrated without herculean effort, select accordingly. Demand references that attest to their enterprise decisioning operational use, scale, and effectiveness.
  5. If evaluating (or already embarking on) a CDP project, simultaneously consider a re-vamped RTIM project. [v]  If a CDP project is ongoing, let the RTIM’s data requirements feed into the CDP’s, not the reverse.  And don’t wait for the CDP project to complete.  Select an RTIM vendor that can map quickly to existing data and can provide tangible proof of fast time to value and ROI.

Don’t –

  1. Accept at face value that the CDP’s RTIM engine will be “good enough.”  Rather, insist the vendor demonstrates unified inbound and outbound decisioning, real-time re-decisioning at scale, advanced analytics features, and capabilities to incorporate contextual streaming data.    
  2. Don’t accept that having a single vendor will outweigh the benefits of having a best-of-breed real-time interaction management (RTIM) engine.
  3. Wait until teams agree on all the right data.  That day won’t come.  Instead, if a CDP has been selected, demand an agile approach for how to enhance the CDP over time.  Ask the vendor of choice for RTIM to provide plans for running before and after the CDP project is done.
  4. Make the mistake thinking that a CDP Smart Hub can deliver scalable and maintainable RTIM decisioning.  None can.  Most rely on traditional segmentation and scripted / deterministic rule-based journey orchestration – all fraught with old problems of static segment definitions, deterministic offer assignment, and hard to maintain eligibility and engagement rules.  A modern RTIM engine with a 1-1 personalization approach solves for all these traditional limitations.

Conclusion

A CDP project, aimed at rationalizing customer data, improving identification, providing segmentation, and streamlining access seems a worthy cause.   Yet history teaches us that chasing a complete view of every single customer across all their devices and interaction points is an elusive goal.  What’s more likely is a CDP project turns into a giant hole, sucking time and resources.  And its expected benefits, like the light bent back on itself by a black hole’s vortex, may never emerge.

Instead, if a CDP project is ongoing, set it on careful rails, and manage scope.  Meanwhile, evaluate RTIM capabilities and embark in parallel to address those shortcomings and gaps.   Research ROI evidence from CDP and RTIM projects and compare.  If resources to fund both projects compete, pit them against each other based on business cases and prioritize investments accordingly.  And remember the lesson of sunk costs, and don’t be afraid to adjust project plans and budgets already in flight.  Many who have placed bets on RTIM cite quick successes that propel massive long-term returns — some with 10x ROI and more than $500 million in incremental revenue. [vi]  Don’t make the mistake of waiting and suffering huge opportunity costs.

[i] Market Guide for Customer Data Platforms, Gartner, March 2022

[ii] Hype Cycle for Digital Marketing, Gartner, July 2021

[iii] Customer Data Platform Industry Update, CDP Institute, January 2022

[iv] Ometria, https://ometria.com/blog/5-reasons-standalone-cdp-might-not-right-solution-retailers, January 2022

[v] What is RTIM, https://www.teradata.com/Glossary/What-is-RTIM, 2022

[vi] Forrester RTIM Wave, https://www.pega.com/forrester-rtim-2022, Q2 2022