A generalized ordinal finite mixture regression model for market segmentation

Yifan Zhang, Duncan K.H. Fong, Wayne S. DeSarbo

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Model-based market segmentation analyses often involve an ordinal dependent variable as ordinal responses are frequently collected in marketing research. In the Bayesian segmentation literature, there are models for an interval- or ratio-scaled dependent variable but there is not any general model for an ordinal dependent variable. In this manuscript, the authors propose a new Bayesian procedure to simultaneously perform segmentation and ordinal regression with variable selection within each derived segment. The procedure is robust to outliers and it also provides an option to include concomitant variables that allows the simultaneous profiling of the derived segments. The authors demonstrate that the practice of treating ordinal responses as interval- or ratio-scales to apply existing Bayesian segmentation procedures can lead to very misleading results and conclusions. Through simulation studies, the authors show that the proposed procedure outperforms several benchmark Bayesian segmentation models in parameter recovery, segment retention, and segment membership prediction for such data. Finally, they provide a commercial business customer satisfaction empirical application to illustrate the usefulness of the proposed model.

Original languageEnglish (US)
Pages (from-to)1055-1072
Number of pages18
JournalInternational Journal of Research in Marketing
Issue number4
StatePublished - Dec 2021

All Science Journal Classification (ASJC) codes

  • Marketing


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