Directing Segmentation
With the arrival of the new year (and new marketing budgets), more than a few companies may be contemplating developing and implementing a new customer segmentation. Having seen my share of such projects over the years, I'd like to offer a specific suggestion for how marketers can plan for a more useful segmentation.
The "Tell Me Everything" Approach
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 in the following statement: "our new segmentation should tell me everything about customers, so that the best marketing programs can be designed and rolled out."
There are several potential pitfalls here. For example, 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.
Similarly, we face problems, both theoretical and practical, in developing a segmentation that is supposed to tell us "everything." For example, one issue we face is that as we try to incorporate more dimensions into the segmentation, the more complex it becomes. 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.
I characterize this approach to segmentation as "unguided," in the sense that 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. Yet, the best insights come when we are looking for particular things or when we have a specific outcome.
Setting A Direction
So now to my bit of advice to anyone embarking on segmentation: focus on the desired strategic outcome and you will achieve better customer insights.
Here is a quick example. Let us say 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. With this goal in mind, it is easier to formulate a segmentation that supports the company's strategy. At a high level, we could ask that the segmentation be able to 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 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.
I used the example of a strategic segmentation built around attrition, but 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.
We are also great advocates of what I would call "tactical segmentation". 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. Here is an even more clear example of "directing" segmentation around a more specific problem.
Posted at 01:00PM Jan 13, 2010
by David King in Data |
