Finite Mixture Models in Market Segmentation: A Review and Suggestions for Best Practices

1. IntroductionMarketers usually address consumer heterogeneity by grouping consumers into segments consisting of those consumers having relatively similar product or service needs. Cluster analysis (CA) is one of the most widely used methods in segmenting markets (Wedel and Kamakura, 2000). Most clustering done in MS practice is based largely on heuristic procedures like Ward's method and k-means (Tuma, Decker, and Scholz, 2011). However, the often insufficient statistical basis of such methods appears to be a major drawback for their use and crucial issues in segmentation, such as determining the optimal number of segments, can hardly be answered by heuristic procedures. Lubke and Muthen (2005:23), for instance, note in this respect that "clustering using k-means is achieved based on an arbitrarily chosen criterion which aims at minimizing the within-cluster variability while maximizing between-cluster variability". FMMs or MB clustering are a principal alternative to heuristic-based algorithms. They are "viewed as elegant procedures that incorporate mixtures of parametric distributions to define the true cluster structure' (Steinley and Brusco, 2011:63). They are the main statistical approach to clustering and segmentation and some academic literature tout and advocate their usage "as a preferred approach because of the provision of a formal statistical model' (Andrews, Brusco and Currim 2010:609; McLachlan and Peel, 2000). In practice, a business analyst embarking on the use of FMMs for segmentation has to deal with several crucial issues like selecting the type of FMM, variable selection, determination of the number of variables and sample size, data pre-processing requirements, determination of the number of segments, validity and stability tests of the obtained results as well as the interpretation and substantial description of segments. All these issues highly influence the outcome and quality of the derived market segments regarding further market-centric activities. The seminal work of Wedel and Kamakura (2000) provides a comprehensive review of FMM applications in MS until the turn of the last century. Since then, however, there have been many new developments in FMMs applications. In the domain of marketing, new models have been designed, implemented and published in toptier journals (e.g. Hahn et al., 2002). Andrews, Brusco and Currim (2010) suggest that some of the hesitancy on the part of practitioners to apply MB approaches to MS may stem from an insufficient knowledge of their performance relative to their NMB counterparts. Whereas the performance of NMB techniques has been evaluated using Monte Carlo simulations for more than three decades (Blashfield, 1976; Steinley and Brusco, 2008a/2008b), extensive simulation studies comparing MB and NMB methods are still slowly emerging in the literature and should ultimately provide business researchers with information as to precise conditions under which MB approaches are apt to be preferred. Moreover, as user friendly software packages like Latent GOLD have made their debut and FMMs have grown in their use since 2000 and, to the best of our knowledge, as there is no recent survey-based empirical literature examining their managerial application, an up-to-date review of their usage in segmentation research seems to be overdue.Against this backdrop, the aim of this article is two-fold: First, it reviews the crucial issues in MS using FMMs. In order to inform business managers and marketing researchers about the performance of competing methods, it builds on the suggestion of Andrews, Brusco and Currim (2010) and, drawing on prior findings in marketing and other disciplines, provides a closer look at comparison simulation studies, with set-ups and key results regarding MB and NMB segmentation methods. The paper contributes to the current literature by integrating previous research results in order to present a holistic overview and comprehensive insights (e. …