Wayne S. Desarbo, Rajdeep Grewal, and Crystal J. Scott
Executive Summary
Competitive market structures are an essential ingredient in the strategic market-planning process. The primary task of competitive market structure analyses is deriving a spatial configuration of products/brands/services in a designated product/service class on the basis of some competitive relationships between these products/brands/services. To provide managers with more meaningful decision-oriented information needed for the evaluation of actual brands’ positions with respect to competitors, competitive market structure analyses have been enhanced with positioning models that incorporate actual consumers’ purchase behavior/intentions, background characteristics, marketing mix, and so forth. Thus, contemporary approaches for empirical modeling of competitive market structure take into account both consumer heterogeneity (e.g., market segmentation) and the competitive positioning of products/brands/services. Such analyses are integrated into more encompassing frameworks, such as the segmentation–targeting–positioning approach. In such a framework, in which a firm targets one or more groups or market segments with its offerings, positioning becomes a segment-specific concept.
For this purpose, 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 (i.e., performing segmentation and positioning simultaneously). The goal is to derive simultaneously a single joint space in which “segments” are represented by vectors and brands by coordinate points and their interrelationship in the space denotes some aspect of the structure in the data. A model option allows for the direct mapping of brand attributes onto the space and for the investigation of how changes in these attributes affect brand positions. The proposed approach does not require distributional assumptions, such as latent-class multidimensional scaling procedures, and provides a concise spatial representation for the analysis of preference or dominance data, as the authors illustrate in an application to buying considerations for small sport utility vehicles. No finite-mixture distribution identification is required, unlike its latent-class multidimensional scaling counterparts. The estimation procedure developed is fast and efficient and converges in a matter of minutes on a personal computer. Globally conditional optimum estimates of parameters are obtained at each iteration of the estimation (though potential locally optimum solutions are also possible here). Finally, the proposed procedure accommodates both overlapping segments and hard partitions.
The authors present the technical details of the clusterwise bilinear spatial regression model along with the estimation procedure developed for the model. Subsequently, they present the application of the model to the simultaneous estimation of market segments and a joint multidimensional space for a commercial application investigating purchase intentions/considerations for small sport utility vehicles in a segmentation–targeting–positioning application. They compare the proposed procedure with the MULTICLUS latent-class multidimensional scaling vector model in terms of calibration fit and predictive validity. They conclude by providing some remarks on the utility of the proposed clusterwise model for various marketing research problems, as well as extensions of the methodology and directions for further research.
Biography
Wayne S. DeSarbo is the Mary Jean and Frank P. Smeal Distinguished Professor of Marketing in the Smeal College of Business at the Pennsylvania State University at University Park. He has published more 130 academic articles in journals such as Journal of Marketing Research, Psychometrika, Journal of Consumer Research, Journal of Mathematical Psychology, Marketing Science, Journal of Classification, Journal of Marketing, Management Science, and Decision Sciences. His methodological interests lie in multidimensional scaling, classification, and multivariate statistics, especially as they pertain to substantive marketing problems in positioning, market structure, consumer choice, market segmentation, and competitive strategy. He serves on the review boards of Marketing Science (associate editor), Journal of Consumer Research, Journal of Marketing, Marketing Letters, and Journal of Marketing Research (associate editor). He has ten years of work experience in marketing research at AT&T and Bell Laboratories. Wayne has been a consultant for diverse firms, such as Ameritech, AT&T, Ad Audit, Pfizer, Senmed, Pacific Bell, General Motors, Hughes Aircraft, GTE, Motorola, Marketing Metrics, Blue Cross, Eli Lilly Co., and Merck Pharmaceuticals. He is founder and chief executive officer of ANALYTIKA Marketing Sciences, Inc.
Rajdeep Grewal (PhD 1998, University of Cincinnati) is Professor of Marketing and Dean’s Faculty Fellow in the Smeal College of Business at the Pennsylvania State University. He is also the Associate Research Director of the Institute for the Study of Business Markets in the Smeal College of Business at the Pennsylvania State University. His research focuses on empirically modeling strategic marketing issues and has appeared in prestigious journals such as Journal of Marketing, Journal of Marketing Research, Marketing Science, Management Science, Journal of Consumer Psychology, MIS Quarterly, and Strategic Management Journal, among others. Currently, he serves on the editorial boards of Journal of Marketing, International Journal of Research in Marketing, and Decision Sciences. He has received several awards for his research, including a doctoral dissertation award from the Procter & Gamble Market Innovation Research Fund. His research also received the Honorable Mention Award in the prestigious Marketing Science Institute/Journal of Marketing competition on “Linking Marketing to Financial Performance and Firm Value” and the 2003 Young Contributor Award from the Society of Consumer Psychology for his article in Journal of Consumer Psychology. His article on incentive-aligned conjoint analysis was the finalist for the 2006 Paul E. Green Award for best article published in the Journal of Marketing Research in 2005. In 2003, he was included in the Marketing Science Institute’s Young Scholars List (scholars with a PhD after 1995 selected on the basis of research productivity in top-tier marketing journals).
Crystal J. Scott is Assistant Professor Of marketing in the School of Management at the University of Michigan, Dearborn. She received her PhD in Marketing (2007) from Pennsylvania State University. Her dissertation research examines perceptions of leadership in the marketing organization compared with other functional business areas. Her other research interests include managing consumer insights and experiences across channels, measurement, and the impact of relationship marketing on various measures of firm performance. She is a member of the American Marketing Association and Academy of Marketing Science.
Journal of Marketing Research, Vol. XLV, No. 3, June 2008
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