The last phase of many segmentation projects is the construction of a segment typing tool, reducing the broad basis of the segmentation down to a minimum number of “golden questions” used to confidently classify future respondent into existing segments. While a variety of statistical and machine learning tools can create typing tools, when we use our handy MaxDiff experiment results to build the segments, our choices become more limited. Two commonly applied methods for building MaxDiff-based typing tools are a Naïve Bayes classifier (currently used in Sawtooth’s MaxDiff Typing Tool software) and Stepwise Discriminant Analysis. Using a series of real-world MaxDiff segmentations, we will test these two methods head-to-head to see if we can determine which is more successful at sorting holdout respondents to known segment memberships.
