A Clusterwise Bilinear Multidimensional Scaling Methodology for Simultaneous Segmentation and Positioning Analyses

The authors propose a general clusterwise bilinear spatial model that simultaneously estimates market segments, their composition, a brand space, and preference/utility vectors per market segment; that is, the model performs segmentation and positioning simultaneously.

Abstract

The segmentation–targeting–positioning conceptual framework has been the traditional foundation and genesis of marketing strategy formulation. The authors propose a general clusterwise bilinear spatial model that simultaneously estimates market segments, their composition, a brand space, and preference/utility vectors per market segment; that is, the model performs segmentation and positioning simultaneously. After a review of related methodological research in the marketing, psychometrics, and classification literature streams, the authors present the technical details of the proposed two-way clusterwise bilinear spatial model. They develop an efficient alternating least squares procedure that estimates conditional globally optimum estimates of the model parameters within each iteration through analytic closed-form expressions. The authors present various model options. They provide a conceptual and empirical comparison with latent-class multidimensional scaling. They use an illustration of the new bilinear multidimensional scaling methodology with an actual commercial study sponsored by a large U.S. automotive manufacturer to examine buying/consideration intentions for small sport-utility vehicles. The authors conclude by summarizing the contributions of this research, discussing the marketing implications for managers, and providing several directions for further research.