Market segmentation by maximum likelihood clustering using choice elasticities

Abstract

We determine market segments by clustering households on the basis of their average choice elasticities across purchases and brands w.r.t. price, sales promotion and brand loyalty. The cluster analysis technique used is a maximum likelihood method which allows varying size and orientation and assumes constant volume. Elasticities originate from choice models with alternatively linear and nonlinear utility functions. Choice models are estimated on the basis of household scanner data. Segments are interpreted by means of multiple discriminant analysis and multinomial logit models whose predictors are elasticities of predictors and external variables (i.e. number of purchases, number of brands bought, income and household size), respectively.