Fuzzy clusterwise regression in benefit segmentation: Application and investigation into its validity

Abstract This article describes a new technique for benefit segmentation, fuzzy clusterwise regression analysis (FCR). It combines cluistering with prediction and is based on multiattribute models of consumer behavior. FCR is especially useful when the number of observations per subject is small, when the relevant set of brands varies among subjects, when the researcher wants to take predictive fir into account in the segmentation procedure, or when subjects can belong to more than one segment. A cross-validation procedure is proposed to assess the validity of the results obtained with FCR. FCR is empirically illustrated for conjoint data on actual product involving price-quality tradeoffsl. The application supports the usefulness and validity of FCR as a benefit segmentation technique.

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