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Journal Article

Finite Mixture Models in Market Segmentation: A Review and Suggestions for Best Practices  pp2-15

Michael Tuma, Reinhold Decker

© Jun 2013 Volume 11 Issue 1, Editor: Ann Brown, pp1 - 50

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Abstract

Recently, Andrews, Brusco and Currim (2010) noted that some of the hesitancy on the part of practitioners to adopt model‑based (MB) methods in market segmentation (MS) may stem from an insufficient awareness of their performance relative to their non‑model‑based (NMB) counterparts. Comparisons of MB and NMB methods should provide business researchers with information as to precise conditions in which the former should be preferred. Moreover, finite mixture models (FMMs) have grown in their use since 2000 and, as there is no recent survey‑based empirical literature examining their application, a comprehensive review of their usage in segmentation research seems to be of use. This article discusses some of the critical issues involved when using FMMs to segment markets, takes a closer look at comparison simulation studies in order to highlight conditions under which a business analyst might consider the application of an FMM approach, discusses model selection as well as validation issues and provides suggestions for best practices and potential improvements. Furthermore, it presents an empirical survey that seeks to provide an up‑to‑date assessment of FMM application in MS.

 

Keywords: market segmentation, model-based clustering, finite mixture models, latent class models

 

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