The Seven Virtues of a Customer Data Platform

January 07, 2014

Last updated: Wednesday January 8, 9 am PT.

I want to express my thanks and appreciation to Scott Brinker for publishing a new version of The Marketing Technology Landscape (and to Kobie Fuller from Accel Partners,  Brian Andersen from LUMA Partners and David Raab for comments that sparked updates after it was published).

Customer Data Platform ... or whatever you call it.

I am excited that Scott is highlighting a new "Marketing Middleware" category in the form of a "Customer Data Platform" (a term first coined by David Raab). Check out his simplified view of the marketer's world:

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Marketers today need a "brain to power the marketing brawn", a central customer system of record that will help marketers better analyze and understand their customers (Customer/Marketing Analytics) and recommend and trigger personalized treatments for each and every customer, across channels, that will improve the customer experience optimize customer lifetime value (Retention/Lifecycle Marketing).

Customer Data Platform and Customer Data Applications

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Kobie Fuller and David Raab helped me see the difference between the backbone/middleware (the Customer Data Platform) and Customer Data Applications (I get confused since AgilOne provides both in an integrated, but modular, solution). Of course, having a Customer Data Platform or middleware alone is not enough. The Customer Data Platform's purpose is to power most, if not all, omni-channel customer experiences and marketing applications. The most prominent Customer Data Applications today are Customer/Marketing Analytics (which Scotts categorizes as part of Marketing Operations) and Retention/Lifecycle Marketing (the Marketing Experiences in Scott's high-level chart - which could be general/omni-channel or channel-specific).

Marketing Analytics dashboards, reports and alerts tell you about important trends with your customers, products and marketing campaigns - such as which campaigns are producing the customers with the highest lifetime value, how many recurring (versus one-time only) customers you have and which products are most popular with your high-value customers. Lifecycle Marketing campaigns are designed to increase customer lifetime value and range from basic campaigns, like customer welcome programs, abandoned cart campaigns and next-sell recommendations, to more advanced strategies like VIP programs and customer win-back programs.

Customers and Prospects are Different.

Some of the vendors listed by Scott in the Customer Data Platform category aren't dealing with known customer data (as of yet), but rather collect unknown or cookie based data (on prospects and customers) and these platforms are referred to by others as Data Management Platforms (DMPs). Another huge difference amongst the vendors listed by both Scott and David as Customer Data Platforms is their intended business purpose: some vendors are focused on Lead Generation (like Mintigo and Lattice Engines), others focus on Display Audience Targeting (like [X+1] and BlueKai) and at AgilOne we focus on (and bundle in) Marketing Analytics and Lifecycle Marketing applications.

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The above picture comes from a presentation by Brian Andersen from LUMA Partners and illustrates the difference between the two different types of "Data Platforms". I also agree with Brian that a different way to look at Customer Data Platforms is as highly transactional, highly scalable and super-automated CRM systems (and that this is different from the cookie pools or DMP platforms).

Questions to ask about a Customer Data Platform

The first question to ask is whether the Customer Data Platform comes with one or more Customer Data Applications and, if so, which ones? Here are some questions to ask about the Marketing Analytics and Lifecycle Marketing applications.

1. Marketing Analytics

Does your product provide a customer analytics dashboard, ad-hoc/pivot reporting and alerting? What kind of out of the box reports and dashboards does the Customer Data Platform provide? Can you create custom reports? Can you schedule reports or dashboards to be emailed on a regular basis? Are you automatically alerted to interesting trends or changes in your business? Does the Marketing Analytics platform include highly flexible pivot reporting?

2. Customer Lifecycle Marketing

What Customer Lifecycle Marketing campaigns are included with your Customer Data Platform? Does it include customer welcome campaigns, abandoned cart campaigns, customer win-back campaigns, next/up/cross-sell campaigns, replenishment campaigns, VIP campaigns, geo-targeted campaigns, mobile campaigns and so on? Whereas you may already be running some of these campaigns, there are strong benefits to power them with you Customer Data Platform. For example, a stand-alone abandoned cart application campaign may lack the complete view of the customer and send abandoned cart discounts to people who already completed their transaction.

After you have covered these basics, it's time to delve deeper into the underlying technology (the actual Customer Data Platform):

3. Customer data management

Do you collect data from all customer touch points - online and offline - and are you able to de-duplicate and enhance these records? A customer data platform vendor should collect customer data from all customer touch points, from web to email to phone to in-store interactions. The records should be accurate and complete. If you already have existing customer data stores, the technology should unify - not replace - these. Customer data should be normalized and de-duplicated and enhanced with geography, household, physical address (verification), automated checks for misspelled names, and gender. Then, to fully understand customers, you need to fuse each customer record with product data, purchase transactions and marketing campaign performance. You might ask how many attributes a vendor collects about each customer and whether this is (infinitely) extensible.

4. Predictive analytics

What predictive models come built in with your platform? You want to make sure that the platform you choose can analyze your customers completely.  There are many different algorithms that can be applied to customer data, which can roughly be categorized in three groups: 1. automated customer segmentation (clustering), 2. recommendations (collaborative filtering) and 3. predictions (propensity models). Each additional model can create additional lift and help find incremental revenue opportunities. Often models work together: for example you might want to find your "customer with high-predicted lifetime value who are at risk of switching to a competitor". Ask which specific models are included with your Customer Data Platform and how frequently the models are maintained and updated (to continue to make good predictions a predictive algorithm requires constant tuning). If you have an existing model you would like to continue to use, ask if there is an opportunity to access the customer data through an open API (see below).

5. Out of the box connectors

What marketing applications do you have out of the box connectors for? Can you continue to use your existing email service provider or do you need to switch to a built-in system provided by the Customer Data Platform? Does the Customer Data Platform also connect to Call Center Software, Point of Sales Systems, Direct Mail Houses and can it power personalized Web experiences and recommendations? The availability of out of the box connectors and deep integration is going to significantly accelerate your time to value.

6. Open API's

What API's do you have available? It's important that your customer data platform is extensible: can you add connectors to custom data sources and can you access the customer data and analysis results via an API as well?

7. Scalability

How many customer records can the Customer Data Platform handle (in real-time)? One of the main differences between the new Customer Data Platform and traditional (B2B) CRM is the scale and level of automation. A Customer Data Platform routinely handles real-time customer actions of millions of customers at the same time and can automatically (not manually through rules) extract customer personas and recommend highly individualized treatments for every one of these.