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.
Some Common Ways of Filtering
The good news is that you can employ several lenses to filter out unsuitable products for your requirements, including:
- Support for business use cases or scenarios.
- Based on tiers.
- Based on data life cycle stages.
- Support for specific industry vertical or domain.
- 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.
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:
- Core Data Services: that include data ingestion, processing and quality management.
- Customer Data Hub: where you carry out profile unification, ID resolution, data governance and consent management.
- Customer Data Activation: where this underlying data is available for segmentation, real-time triggers and events.
- 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.
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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- Data processing encompasses first two stages of core data services and customer data hub.
- Engagement encompasses latter two stages customer data activation and customer engagement.
Based on these stages, you can categorize CDPs as:
- Processing-oriented CDPs are CDPs that are stronger in data processing (and first two pillars).
- Engagement-oriented CDPs are CDPs that are stronger in activation and engagement (latter two pillars).
Based on Support for specific Industry Vertical or Domain
CDPs find usage across a wide range of industries. Even though the broad functionality that a CDP provides is “horizontal” in nature, several industry-specific aspects become important and require domain flavor.
It’s also worth noting that while functionality could be similar across industry use cases, the mechanics of it could be different. As an example, recommending a healthcare plan to potential patients and recommending which burger to order are both examples of recommendations, but they’re of very different nature and likely need domain-specific algorithms and data models to be able to come up with right recommendations.
Some CDPs provide industry solutions using industry-specific data models, terminology, integrations with industry-specific products and appropriate labels in the UI. In addition to having an industry-specific solution, this also shows the vendor actually understands specific challenges of that industry.
It can be a good idea to see if the vendor has specific industry solutions for your domain or not. Having an industry-specific solution, even if it’s a basic one, can kickstart your implementation, with the caveat that integration ease is likely more useful than pre-populated data models.
Related Article: How Customer Data Platforms Can Benefit the Call Center
Deployment Approaches
You can categorize based on different deployment approaches. There are several deployment approaches possible — on-premises, public cloud, private cloud, hybrid and so on. Even within these, there are many possibilities. We’ve seen enterprises asking for a specific cloud (e.g., Azure vs GCP vs AWS). Sometimes this is because they want to host the CDP themselves in a private cloud environment, though to be sure, very few CDPs support this.
Why does this choice of cloud matter if it’s a SaaS-based service, you might ask? Well, a lot of customers already have existing components of their martech stack in a particular cloud, and it makes sense to have CDP on the same cloud to improve latency and save on data transfer costs.
Conclusion: How to Use These Filtering Criteria
Here’s how you can use all these different ways of filtering.
When there are hundreds of CDPs in the marketplace, the first step is usually to filter out unnecessary products, so you can deep dive into a more manageable list of vendors. So instead of 100 products, you only have to look at a list of 10-12 vendors.
As an example of using deployment approaches criteria, if your key requirement is to have a solution that deploys only on Google Cloud Platform (GCP), do you even need to look at so many other products that don’t support GCP at all? Or if your key requirement is to be able to deploy in a private cloud, there’s really no point in comparing products that are SaaS-based only.
In this article, we looked at these five lenses to filter CDPs, but these lenses can change based on specific requirements. We usually use this approach as a starting point. You'll likely see modifications for your specific requirements and add additional criteria while dropping some.
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