The representation of consumer heterogeneity has a long history in marketing, beginning with the use of finite mixture models (Kamakura and Russell, 1989) that approximate the distribution of heterogeneity as a fixed set of mass points, to hierarchical Bayes models (Rossi, Allenby and McCulloch, 2005) that employ continuous distributions with observable covariates (Allenby and Ginter, 1995) to shift the mean of the heterogeneity distribution. A limitation of these approaches to modeling heterogeneity is that they assume a common pooling mechanism for all respondents. This is particularly problematic because of the shallow nature of marketing data, making it difficult to identify alternative mechanisms. In this presentation we develop a hierarchical Dirichlet process (HDP) model for heterogeneity in the context of a conjoint model for charitable giving. The heterogeneity distribution employs a Grade of Membership model for scaled response questions that shifts the location of the heterogeneity distribution. We show that the HDP model avoids imposing unnecessary restrictions in the heterogeneity distribution, resulting in a cleaner classification of respondents useful for customer segmentation and understanding alternative pooling mechanism. We apply our model to a conjoint study of charitable giving.
