A Clusterwise Regression Method for Simultaneous Fuzzy Market Structuring and Benefit Segmentation

Michel Wedel · Jan‐Benedict E.M. Steenkamp

143 citations0/0 files storedJournal of Marketing Research

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Economic and Environmental Valuation Consumer Market Behavior and Pricing Bayesian Methods and Mixture Models

Abstract

A generalized algorithm for fuzzy clusterwise regression (GFCR) is proposed that incorporates both benefit segmentation and market structuring within the framework of preference analysis. The method simultaneously estimates the models relating preferences to product dimensions within each of a number of clusters, and the degree of membership of brands and of subjects in those clusters. The performance of GFCR is assessed in a Monté Carlo study. An application to data on preferences for brands of margarine and butter is reported, the cross-validity of GFCR is assessed, and it is compared empirically with Kamakura's method. Managerial and research implications are discussed.

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References

1989A Probabilistic Choice Model for Market Segmentation and Elasticity Structure 1978Issues and Advances in Segmentation Research 1968Benefit Segmentation: A Decision-Oriented Research Tool 1979Additive clustering: Representation of similarities as combinations of discrete overlapping properties.1979Customer-Oriented Approaches to Identifying Product-Markets 1987A Simultaneous Approach to Market Segmentation and Market Structuring 1984A Customer-Oriented Approach for Determining Market Structures 1969A Monte Carlo Investigation of the Statistical Significance of Kruskal's Nonmetric Scaling Procedure

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