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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