A Comparison of Segment Retention Criteria for Finite Mixture Logit Models

Abstract

Despite the widespread application of finite mixture models in marketing research, the decision of how many segments to retain in the models is an important unresolved issue. Almost all applications of the models in marketing rely on segment retention criteria such as Akaike's information criterion, Bayesian information criterion, consistent Akaike's information criterion, and information complexity to determine the number of latent segments to retain. Because these applications employ real-world data in which the true number of segments is unknown, it is not clear whether these criteria are effective. Retaining the true number of segments is crucial because many product design and marketing decisions depend on it. The purpose of this extensive simulation study is to determine how well commonly used segment retention criteria perform in the context of simulated multinomial choice data, as obtained from supermarket scanner panels, in which the true number of segments is known. The authors find that an Akaike's information criterion with a penalty factor of three rather than the traditional value of two has the highest segment retention success rate across nearly all experimental conditions. Currently, this criterion is rarely, if ever, applied in the marketing literature. Experimental factors of particular interest in marketing contexts, such as the number of choices per household, the number of choice alternatives, the error variance of the choices, and the minimum segment size, have not been considered in the statistics literature. The authors show that they, among other factors, affect the performance of segment retention criteria.