This presentation shows how to improve market segmentation studies with the judicious use of machine learning methods. We start by outlining the four steps of a typical market segmentation process: • Selecting basis variables • Creating segments • Profiling segments • Predicting segment membership Traditionally, the industry used taxonomic and statistical methods for these steps. In a few specific instances, those methods are the correct ones to use. But they don’t generalize well to all or even many cases of market segmentation. Frequently over-used in lazy (segment creation via cluster analysis) or simply incorrect (variable selection via factor analysis) ways, they almost always be improved upon. Machine learning methods can improve the segmentation research process at various of its four steps: • Sometimes as additional tools to enhance what we were able to do before with traditional statistics (e.g., decision trees for segment creation) • Sometimes as improvements over what traditional statistics can do (e.g., random forests for segment prediction) • Sometimes to do things properly that misunderstood statistical methods are inappropriately tasked to do (e.g., factor analysis is frequently, and incorrectly, thought to be a data pre-treatment method for segmentation analysis, but machine learning techniques are the vastly better way to select basis variables).
