Segmenting retail markets on store image using a consumer-based methodology

Abstract

Various approaches to segmenting retail markets based on store image are reviewed, including methods that have not yet been applied to retailing problems. It is argued that a recently developed segmentation technique, fuzzy clusterwise regression analysis (FCR), holds high potential for store-image segmentation research. FCR clusters consumers into segments and simultaneously provides statistically derived store image attribute importances for each segment. Due to the simultaneous segmentation and estimation procedures, FCR can be used even in situations where the number of store image attributes exceeds the number of stores that are evaluated by a subject. FCR accommodates nonoverlapping, overlapping, and fuzzy clustering in an integrated consumer-based framework. The usefulness of FCR is empirically investigated in the context of store image for retailers selling meat in the Netherlands. FCR is also compared to a more traditional segmentation approach. The incremental insights into consumer behavior over the unsegmented solution and over segments provided by the more traditional segmentation approach are discussed.

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