Latent-Class Choice Segmentation

Latent-class choice segmentation estimates consumer market segments and their preferences at the same time, by fitting a finite mixture of discrete-choice models to individual purchase or choice data. Wagner Kamakura and Gary Russell introduced the approach in their 1989 Journal of Marketing Research paper, which fit a probabilistic choice model whose latent segments differ in both brand preference and price sensitivity, yielding a unified picture of market structure and elasticities. Rather than clustering consumers first and modeling choice afterward, the method treats segment membership as an unobserved (latent) variable and recovers it jointly with the segment-level choice parameters by maximum likelihood. Each segment is a multinomial logit model with its own coefficient vector, and the mixing proportions describe how large each segment is. Michel Wedel and Wagner Kamakura's authoritative monograph later codified the finite-mixture framework as the methodological backbone of model-based market segmentation. The result links the pattern of brand switching to the magnitudes of own- and cross-price elasticities, giving managers a behaviorally grounded segmentation tied directly to demand response.