Market segmentation with mixture regression models: Understanding measures that guide model selection

Abstract

Owing to their considerable potential for market segmentation studies, mixture regression models have recently received increasing attention from both academics and practitioners. One fundamental difficulty with their application is related to the problem of model selection, that is, the choice regarding the number of segments. Retaining the correct number of segments is, however, crucial as many managerial decisions depend on this decision. Since the proper number of segments is unknown in real-world applications, a thorough understanding of measures that guide the model selection decision is of fundamental importance. Based on a simulation study, this paper addresses the issue by evaluating how the interaction of the most important influencing factors for the measures’ success — sample and segment size — affects the performance of four of the most widely used criteria for assessing the correct number of segments in mixture regression models. For the first time, the quality of these criteria is evaluated with regard to a wide spectrum of possible constellations. Furthermore, relative and absolute performances are analysed in respect of outside criteria. Recommendations on criterion selection are thereafter deduced from the results when a certain sample size is given. These recommendations also help to establish the sample size that is needed in order to guarantee an accurate decision based on a specific criterion. An application based on customer satisfaction data illustrates the relevance of the findings. In conclusion, theoretical and managerial implications are provided.