Using Extremes to Design Products and Segment Markets

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Abstract
Current marketing methodologies used to study consumers are inadequate for identifying and understanding respondents whose preferences for a product offering are most extreme. These “extreme respondents” have important implications for product design and market segmentation decisions. The authors develop a hierarchical Bayes random-effects model and apply it to a conjoint study of credit card attributes. Their proposed model facilitates an in-depth study of respondent heterogeneity, especially of extreme respondents. The authors demonstrate the importance of characterizing extremes in identifying product attributes and predicting the success of potential products.

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