Segmentation for fun and profit
With the arrival of the new year - and new marketing budgets - companies are exploring how to develop and implement new customer segmentation. Here's how marketers can plan for a segmentation that is more useful and profitable.
Choose your destination clearly.
Segmentation is one of the fundamental tools of marketing; we simply could not function effectively without being able to classify markets, prospects, and customers into useful categories. Too often, though, the drive to build customer insights is expressed as a vague concept, which might be summarized as: “Our new segmentation should tell me everything about our customers, so that the best marketing programs can be designed and rolled out.”
There are several potential pitfalls here. There is no objective definition of what is meant by “best” marketing programs. Imagine developing a creative brief that had only this level of detail. Yet it is surprising how many segmentation initiatives are launched around such vague notions.
Companies also face theoretical and practical problems in developing segmentation that is supposed to tell us “everything.” For example, as more dimensions are folded into the segmentation, it becomes more complex. Often, the complexity becomes significant enough that the decision is made to "collapse" the many resulting micro-segments into a more manageable and smaller set of segments. For this reason, many companies have arrived at enterprise segmentation schemes with six to eight segments.
This approach to segmentation is unguided. There is a desire to identify natural segments from which we are supposed to infer insights and strategies. In other words, the analysts are supposed to find the true meaning locked within the data and reveal it to the organization. However, the best insights come when we are looking for particular items or when we have a specific outcome in mind.
Stay on course.
The solution? Focus on the desired strategic outcome and you will achieve better customer insights. For example, let's assume that an important goal for the company is to reduce customer attrition from 20% annually to 18% annually; if successful, the company would see a significant increase in its profits. At a high level, we could ask that the segmentation answer a few fundamental questions:
Which customers are most likely to leave in the future? Too many segmentation schemes are static and fail to yield any ability to forecast the future. In this example, what if attrition rose to 22%?
Why are a fifth of all customers leaving each year, and are there any discernible segments that cluster around specific reasons (e.g., some customers believe the products are too expensive, while others believe that the company's technology is outdated, and still others have been angered by poor customer service)?
What will induce customers to remain with the brand? Here we may also separate different segments, with some customers potentially representing little opportunity for retention, while others need to have the right response from the company in order to induce them to stay.
We could certainly look at other dimensions, but by focusing on the strategic outcome of improving attrition rates, we can hone in on the most relevant questions and formulate a response.
Other routes to take:
Instead of looking at strategic segmentation built around attrition, we could just as easily use something even more general, such as customer value. We would begin by placing customers into different value segments, then use other data to expand the segmentation. In particular, we would want to understand how to increase value across various segments. This means we need to understand what will enable us to persuade customers to spend more, and then devise marketing programs that use these insights. We also may have segments in which maintaining value is the most critical aspect. For example, highly valuable customers may not have a lot of potential for more growth, but we want to make sure to retain the value they represent.
So-called tactical segmentation is also worth exploring. For example, let's say we want to build a basic response model, something that would probably employ logistic regression. In nearly every case, that model can be improved by using latent class regression, which will produce a segmentation local to the dependent variable we are trying to predict. Plus, we now can exploit the differences between segments to develop more effective campaigns. Like the other scenarios, it shows how guided, purposeful segmentation can bring you to a more profitable outcome.