Presentation: A New Modeling Tool for Identifying Meaningful Segments and their Willingness to Pay: Improving Validity by Reducing the Confound between Scale and Preference Heterogeneity

2015 Advanced Research Techniques (ART) Forum

June 15, 2015

Jay Magidson, Statistical Innovations

San Diego, CA

With discrete choice data, it is common practice to use HB or latent class modeling to capture heterogeneity across decision-makers. However, a significant part of the heterogeneity retrieved relates to differences in the amount of error variance, a phenomenon referred to as scale heterogeneity. As a result of the preference vs. scale heterogeneity confound, traditional approaches to segmentation may yield spurious segments that differ only in their scale heterogeneity, and may not differ at all in their willingness to pay. In this presentation we introduce a new scale adjusted latent class (SALC) choice model that accounts for both observed and unobserved scale heterogeneity, resulting in homogeneous segments that differ only in their preferences and willingness to pay, increasing the usefulness of segmentation analyses to marketers. We compare results from the SALC approach with other standard and nonstandard approaches.

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