There are two main types of consumer segmentation.

The first is unsupervised (post hoc) in which segments are "discovered" with some form of cluster analysis. K-means and hierarchical clustering historically have been most popular, though there are many newer methods such as latent class that are also used.

The second type is supervised (a priori) in which a target (dependent) variable is statistically profiled. The target variable is usually (though not always) a nominal or ordinal variable. Logistic regression, CHAID and random forests are three of many analytics tools used.

Often the two are used in combination, e.g., post hoc segmentation to discover the segments and a priori segmentation to predict them. In this case, the post hoc clusters are the target variable.

A third kind, which I call driver segmentation, essentially combines the two approaches simultaneously. This is less known in marketing research but can be very useful.

Brand segmentation is somewhat different. In brand segmentation, brand "maps" are created using correspondence analysis, biplots, principal components, hierarchical clustering or other methods to identify clusters of brands (not people).

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