Retention of latent segments in regression-based marketing models

Abstract

Product design and marketing mix decisions for segmented markets depend crucially on the correct specification of marketing models used as input to these decisions. With real-world data, the true number of segments in a market is unknown. Current evidence from simulation studies suggests that the accuracy of commonly used criteria for determining the number of segments in a market depends on the usage context, including the type of distribution being used to describe the data, the model specification, and the characteristics of the market. This study investigates via simulation the performance of seven segment retention criteria used with finite mixture regression models for normal data. This is one of the most important analysis contexts in marketing research since regression models are used, for example, in conjoint analysis and market response analysis, yet no previous study in either the marketing or statistics literatures explores the segment retention problem for mixture regression models. The study shows that one criterion, Akaike's Information Criterion (AIC) with a per-parameter penalty factor of 3 (AIC3), is clearly the best criterion to use across a wide variety of model specifications and data configurations, having the highest success rate and producing very low parameter bias. Currently, this criterion is rarely, if ever, used in the marketing literature.