Abstract
An algorithm for fuzzy clusterwise regression (FCR) is proposed which can be used for benefit segmentation within the framework of preference analysis. The method simultaneously estimates the models relating preference to product dimensions within each cluster, as well as the parameters indicating the degree of membership of subjects in these clusters. Information on the degree of competition within segments, opportunities for new products and indications for marketing strategies are provided, all from a consumers' perspective. Using synthetic data, the performance of the method is evaluated according to computational requirements, parameter recovery, and data reproduction. Comparisons with the clusterwise regression approach of DeSarbo, Oliver and Rangaswamy (1989) and Hagerty's (1985) optimal weighting method for conjoint analysis are given, as well as an application to the analysis of preferences for meat products. Special attention is paid to significance testing using Monte Carlo test procedures, and convergence to local optima.
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