Dynamic Customer Segmentation: The Next Evolution in Marketing

Having a common definition of what dynamic customer segmentation, or Dynamic Predictive Segmentation (DPS) as it’s referred to in the report, would be a good starting point.

The Forrester report describes DPS as follows:

There is a logical roadmap to migrate from current to future state in the world of customer segmentation. Adopting an explainable machine learning customer segmentation model is part of that roadmap from beginning to end. AI offers powerful alternatives to traditional statistical techniques, so as companies become aware of this technology, I am going to provide a simple high level two step process to this evolution.

One of the first ways to get started down the path of using better explainable AI (XAI) driven segmentation is to focus on keeping fewer segments in place –to smooth out the operational impact of using a different analytic software to create segments. By doing this you automatically can become more relevant with customers as outlined in the table below:

Track these movements by simply capturing segment assignment history by customer. As you rotate different offers to them, you can think about how that affects messaging over time. But simply updating more frequently will assign customers to the most relevant segment at the moment given updated data.

Training an XAI algorithm on the same underlying data as your original clusters were created from with a target number of K segments will produce the same number of segments – except they will be machine driven and predictive – showing the predictive factors associated with each segment. There are a few immediate things you should do once you have replicated the same number of segments with XAI:

1. Examine how the Segments are Different

The segments may be similar in that the XAI technology is still grouping through K-means, similar objects. However the technology can divide clusters by predicted class – “will buy” or “won’t buy” for example. What are the customer counts by segment?

2. What Are the Predictive Drivers of Each Segment

These new segments will show predictive drivers of behavior. This is where a great deal of insight can be gleaned with regard to what is important to those customers predicted to buy or not buy. How does this affect your communication to them?

3. Make Simple Tweaks to Offers and Messages to Start

Using the above insights, you can make simple updates to existing copy if necessary, where predictive factors are compelling and clear. These can be very subtle but meaningful in simply acknowledging in some what is most important to a group of customers.

4. Ask Yourself What Data Is Missing

Based on these predictive factors, ask yourself if you have fed into the analytic software all the data you should have available.

For example, primary research data can be modeled out across customers and then included in segmentation as factors that could in fact be predictive. Or you can report all the descriptive characteristics out associated with new predictive segments, and examine the intersection of predictive and descriptive characteristics to think through how your customers are really behaving and what defines them.

5. Update More Frequently

Since XAI can update predictive clusters at a faster rate than traditional segmentation models, you can see how customers move from one segment to another across updates.

Expanding K is easy with XAI. You can re-run segmentation and just change the settings to set K at whatever you want it to be. However, what do you do with new segments?

Obviously, absorbing new segments into your existing campaign process is not a trivial task. New creative and media planning needs to take place based on these new segments. So just sticking with the customer side of the equation for now there are specific steps you should consider in expanding K. These are as follows:

1. Examine New Segments Compared to Existing Segments

These new segments will now reflect existing customers that have moved from the old segments into these new machine-driven segments. This is simply a tighter clustering of objects that cluster together more discreetly than before.

The new predictive features and descriptive factors that define them will be distinct. A comparison of the factors between new segments vs. old can be done to assess what makes these segments different.

2. Generate New Treatments for New Segments One at a Time

You can develop new treatments and media strategies on your timeline while executing against the original segments. Work with your agency to inform new creative briefs for a handful of new segments that seem discrete and meaningful in size – warranting the investment.

3. Test and Learn Against New Segments

These new segments can be activated one at a time as well into the campaign stream through testing. Running A/B tests against these new segments will help examine performance over time.

Since these are predictive segments, targeting predicted buyers for example with relevant offers to their weighted factors should outperform standard response and conversion metrics. A/B testing will help refine both messaging and offers based on different treatments over time to optimize yield.

4. Evaluate What is the Optimal K in Non-Production Over Time

Running multiple segmentation scenarios in a development server off to the side for analysis and planning is always a good idea. As customers evolve, data changes and you get more sophisticated at matching treatments to more segments and understanding where best to reach these customers in terms of channels, you will be well informed about how to evolve your segmentation by increasing K further. Ultimately you will level off to the right number for optimal gain.

5. Consider Multiple Segmentation Strategies for Different Parts of the Life Cycle

Since you can, you may want to run segmentations related to increased product usage, cross sell, upsell, churn and reactivation independently of one another. As a customer moves across the lifecycle horizon – you can be even more relevant through this approach. However, this assumes your organization has kept up with being customer aligned in a way that you can actually operationalize such a sophisticated approach.