Machine Learning Models to Consider for Your CDP

A self-driving car that adapts to road conditions in real time. The Facebook prompt of your mom’s name when you hover over her photo. The auto-fill that correctly guesses what you would have typed into Google’s search bar.

All examples of machine learning (ML) in our everyday lives. All examples of ML’s power. 

By processing large data sets more quickly than any human ever could, machine learning, a subset of artificial intelligence, can accurately predict our behaviors and tendencies. 

Imagine that power embedded in a customer data platform (CDP), software that collects and unifies data across channels and systems to create a single source of truth for customer data. With a CDP, organizations can more finely tune marketing campaigns, derive business insights that guide operations, and craft initiatives that lower churn.

No wonder more and more organizations are looking to add a CDP to their technology stack. Indeed, according to the CDP Institute, industry revenue was expected to reach $2 billion last year, a 25% rise over 2021.

That jump points to the increasingly crowded market for CDPs, but not all vendors’ machine learning capabilities are the same. Many will claim to incorporate machine learning in their product; many will fall short. To help organizations separate the signal from the noise, we partnered with the CDP Institute to provide advice for marketing decision makers. The resulting white paper reviews organizational use cases that favor the technology, different kinds of ML models, and what to look for when evaluating the claims of would-be CDP partners.

Use cases: How a machine learning-enabled CDP helps organizations

Earlier, we cited a few of the use cases that help organizations take advantage of a ML-powered CDP. Let’s dive into another handful to get a better sense of the many ways that this subset of AI is changing how organizations approach customer data.

Acquiring new customers

Growing the number of new customers is a must for any business, and a CDP with ML capabilities is a critical tool for reaching that goal. For instance, it can help with media optimization and choosing the best offers that attract certain segments. Importantly, it can also estimate the long-term value of particular customers, facilitating improved targeting.

Increasing customer lifetime value 

ML-powered CDPs not only help organizations expand their customer base, they can also grow their lifetime value after acquisition. Such CDPs can, for example, determine the best path forward when a customer is active in multiple campaigns. Or, if there are product categories that a customer hasn’t yet bought into, a ML-enabled CDP can evaluate those that have the highest cross-category purchase likelihood. And, using channel-optimization models that take into account a customer’s channel preferences and potential response rates, ML-backed CDPs can offer the most effective messaging channel for each customer.

Improving customer experiences

Providing excellent customer service on- and offline remains a top priority for competitive organizations, and a CDP can help in both arenas. For example, a CDP can use ML models to predict the level of support individual customers may need, as well as suggest the best course of action to customer service reps and chatbots. Optimizing offline service operations may include recommending what inventory would be best added to field service vehicles or the best routes for field engineers and delivery drivers.

Maximizing internal operations

CDPs can also assist organizations with activities that don’t involve customer interactions. For instance, they can lean on ML models that help businesses forecast demand, thus improving how inventory is managed, as well as analyze the value of product features or why some products fail. So, beyond customer management, ML-powered CDPs can support supply chain and product design teams.

Types of machine learning models

The small handful of use cases mentioned above hopefully offers a glimpse into the wide range of applications to which CDPs may be put. Just as far ranging are the kinds of ML models needed to produce the results organizations crave. Let’s take a look at some examples.

Complying with data privacy regulations

Perhaps one of the biggest benefits of ML-supported CDPs is their ability to ensure compliance with data privacy standards, which are on the rise. CDPs achieve compliance via ML models that use master training data sets organized by permissibility. For example, a training data set can exclude privacy-sensitive data altogether, but if some customers have consented to the use of their data, then the data set may include that information. Some ML systems can also detect sensitive items in the data and ask users whether or not they should be included in the model.

Unifying data through identity resolution

Unified data may be the key output of CDPs. ML applications produce these so-called golden customer records via identity resolution that combines data from various sources. Even when there’s no shared, persistent customer ID, some CDPs can merge the data and include exact, deterministic, or probabilistic matches. (Be sure you know which match types you need so that you choose a CDP that can deliver them.)

Building customer personas through clustering

ML algorithms excel at identifying similarities in customer data and discovering patterns that would take humans, quickly overwhelmed by the volume of data produced today, much longer to discern. Through clustering models, however, a CDP can help marketers develop targeted campaigns based on data, not on intuition or hunches, such as you might find with segmentation.

Requirements to consider

Reviewing which ML-powered CDP is best for your organization involves weighing not just the use cases it supports or the kinds of models it includes. Other considerations include but are not limited to:

• Explainability. Much as we’d like to think that we control AI, some of its outputs exceed human comprehension. To control for this, ML systems offer a range of reporting. Some will identify the most important data elements and explain why particular records were scored a certain way, while other systems will report on a model’s performance over time. The types of reports organizations require will depend on the expected user base. A non-technical user will benefit from reports that a data scientist would find less useful, for example.
   
• Scale. The size of an organization’s customer base, the amount of data it generates, and the sources from which the data is collected are variables that influence a CDP’s performance requirements. Does your organization enjoy a customer base of millions (and the billions of data points they represent)? Or does your organization have a more modest pool of data sources? Keep scale in mind as you evaluate what will both go into your CDP and what you expect out of it.
   
• Automation. Do you need a CDP that automates the creation of ML models, or do you need it to automate other ML-related tasks, such as exploring training sets or data preparation? Parse which automation tasks require machine learning and which may be handled by other components of the CDP or other tools included in your martech stack.

Where to go from here

The ways in which machine learning can bolster CDPs are manifold, and we’ve only scratched the surface here. Plus, the field of machine learning continues to evolve, so the outcomes and models described above only showcase what’s possible today. An in-house data specialist or an enterprising vendor may have requests or planned features that push the discipline’s boundaries, so don’t be shy about asking for the seemingly pie-in-the-sky. At the very least, it will give you an idea of how well-versed a vendor is about the current R&D environment for ML.

In the meantime, we partnered with the CDP Institute to create a comprehensive guide to machine learning and CDPs. Download the free white paper now to enhance your understanding of how ML is shaping the possibilities for CDPs and organizations today.