K-means clustering programs are frequently used to group buyers into market segments. Segment members exhibit similar background profiles, based on such characteristics as psychographics, benefits seeking, conjoint-based partworths, and so on. In addition, researchers also may use data on one or more exogenous variables for the same respondents. The authors describe and apply an algorithm for systematically modifying the original K-means segmentation to enhance prediction of an exogenous variable (either continuous or categorical). The modification is designed to respect a user-specified constraint on the permissible percentage decrease in the initial partition's ratio of among-groups sums of squares to total-groups sums of squares. The authors compare this approach with models drawn from the highly popular clusterwise regression literature.
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