The Right Filtering Process for CDP Selection

Exploring the lenses with which to filter Customer Data Platforms (CDPs) and eliminate vendors that don’t meet business needs.

Customer Data Platforms (CDPs) are rapidly becoming a foundational technology piece in omnichannel martech stacks. They can also serve as a key component in broader digital transformation and customer experience (CX) initiatives.

Unfortunately for you the technology buyer, scores of products call themselves a “CDP,” and perhaps as many platforms in other categories have CDP-like capabilities. Naturally, almost every vendor claims to be a leader, challenger, largest, first, leading, global or some combination of these.

With this much noise in the marketplace, several issues arise, including:

  • Many prospective customers have the impression that most of these products are similar and there is not much to differentiate between them.
  • When evaluating CDPs for your requirements, it becomes difficult to create an initial shortlist of tools relevant for your requirements.
Filtering out irrelevant products early in a selection process helps you create a more suitable shortlist.

Some Common Ways of Filtering

The good news is that you can employ several lenses to filter out unsuitable products for your requirements, including:

  1. Support for business use cases or scenarios.
  2. Based on tiers.
  3. Based on data life cycle stages.
  4. Support for specific industry vertical or domain.
  5. Deployment approaches.

Of course, this is not an exhaustive list. We do sometimes run into very specific requirements, but these five are a great place to start, especially if you employ them in combination.

Related Article: How a CDP Can Optimize Customer Lifecycle Management

Support for Business Use Cases or Scenarios

We’ve learned to favor business use-cases or scenarios over feature grids as a key mechanism to filter vendor choices.

As usual, vendors will claim broad applicability for their platforms. But since software development is all about trade-offs, inevitably each platform (and vendor) will bring certain strengths in terms of what they can do. Some CDPs support ecommerce needs better than others. Or some platforms can manage loyalty type use cases better than others. There’s some overlap with functionality here but support for broader use case can prove to be a good categorization axis.

The following are 10 potential scenarios. However, we’ve found that most vendors in reality only specialize in three or four of the 10.

Most CDP vendors support only a handful of these scenarios
Most CDP vendors support only a handful of these scenarios.

To be fair, these scenarios are abstractions, and some will be more or less germane to you. So use these as a starting point to create your own use cases. Our experience indicates that your scenarios will almost always be some hybrid combination of one or more of these.

Based on Market Tiers

Market tiers can come in many dimensions, ranging from target customer size (small, medium, enterprise) to complexity to solution bundling, and so on. One popular dimension is to categorize CDPs based on whether they are a part of broader marketing cloud (or suite) or a stand-alone, best-of-breed product.

Do you want a specialist CDP provider or do you want to use CDP capabilities from a marketing cloud or suite vendor that provides you several other capabilities?

There are pros and cons of both approaches. As a martech stack owner, you need to decide whether your customer data should be a part of a larger platform (like Adobe, SAP or Salesforce) or should you be more conscious of “separation of concerns” and invest in an independent CDP. Pro tip: we see more success with the latter.

Related Article: Get the Foundation Use Case Ready for Your Customer Data Platform

Based on Data Lifecycle stages

A CDP can theoretically provide a wide range of services across all stages of the customer data lifecycle. Activities across these stages can be best understood under the following four pillars:

  1. Core Data Services: that include data ingestion, processing and quality management.
  2. Customer Data Hub: where you carry out profile unification, ID resolution, data governance and consent management.
  3. Customer Data Activation: where this underlying data is available for segmentation, real-time triggers and events.
  4. Customer Engagement: or the so-called last mile, which includes using customer data for personalization, recommendations or other engagements.

The figure below shows these stages with key activities in each stage.

What does a CDP do for you?
What does a CDP do for you?

Now, you will often find CDP vendors boasting they can perform all these stages equally well. However that is not true, and CDPs have different sweet spots. In addition, you may not want your CDP to perform all these services because you already have existing technologies  for these tasks.

Therefore, it is important to figure out what the CDP needs to do for you and then filter out CDPs based on that. Broadly, these four pillars can be clubbed into two stages:

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