Latent Segments Models (LSM) are commonly used as an approach for market segmentation. When using LSM, several criteria are available to determine the number of segments. However, it is not established which criteria are more adequate when dealing with a specific application. Since most market segmentation problems involve the simultaneous use of categorical and continuous base variables, it is particularly useful to select the best criteria when dealing with LSM with mixed type base variables. We first present an empirical test, which provides the ranking of several information criteria for model selection based on ten mixed data sets. As a result, the ICL-BIC, BIC, CAIC and L criteria are selected as the best performing criteria in the estimation of mixed mixture models. We then present an application concerning a retail chain clients' segmentation. The best information criteria yield two segments: Preferential Clients and Occasional Clients.

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Clustering,Finite mixture models,Information criteria,Marketing research

• Mathematics - Natural Sciences
• Computer and Information Sciences - Natural Sciences