The idea behind segmentation is pretty simple. We’re basically trying to find groups of customers or events or items with similar characteristics and grouping them together. Generally, we want elements in our groups to be as similar as possible to each other. But at the same time, we want the groups themselves to be different enough that it makes sense to treat them differently. When elements within a group are similar, we call that homogeneity within groups. And when the groups themselves are different from each other, we call that heterogeneity between groups. A good segmentation has both properties.
There are many uses of segmentation. A business may develop different products to serve the needs of different customer segments. It may use different communication methods or channels to sell the same products to different customers. Some businesses even model their organizational structures after the segments they intend to serve. In most cases, the business is trying to optimize the effectiveness and efficiency of their activities by customizing them in some way to the market. Segmentation helps to inform how that should be done. Segmentation can also be used as a first step in a larger analysis. For example, I might use customer segments as inputs to a statistical model, like a regression, to predict some type of behavior, whether or not I intend to act on that prediction by segment.
There are quite a few analytic methods that can be applied to execute a segmentation analysis, including factor analysis and principal components analysis, clustering, and decision trees or other propensity modeling techniques. For example, we might use a clustering technique that yields an output like this.