Multiattribute perceptual mapping with idiosyncratic brand and attribute sets

This article proposes an extremely flexible procedure for perceptual mapping based on multiattribute ratings, such that the respondent freely generates sets of both brands and attributes. Therefore, the brands and attributes are known and relevant to each participant. Collecting and analyzing such idiosyncratic datasets can be challenging. Therefore, this study proposes a modification of generalized canonical correlation analysis to support the analysis of the complex data structure. The model results in a common perceptual map with subject-specific and overall fit measures. An experimental study compares the proposed procedure with alternative approaches using predetermined sets of brands and/or attributes. In the proposed procedure, brands are better known, attributes appear more relevant, and the respondent's burden is lower. The positions of brands in the new perceptual map differ from those obtained when using fixed brand sets. Moreover, the new procedure typically yields positioning information on more brands. An empirical study on positioning of shoe stores illustrates our procedure and resulting insights. Finally, the authors discuss limitations, potential application areas, and directions for research

Inferring Market Structure from Customer Response to Competing and Complementary Products

We consider customer influences on market structure, arguing that market structure should explain the extent to which any given set of market offerings are substitutes or complements. We describe recent additions to the market structure analysis literature and identify promising directions for new research in market structure analysis. Impressive advances in data collection, statistical methodology and information technology provide unique opportunities for researchers to build market structure tools that can assist “real-time” marketing decision-making.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46981/1/11002_2004_Article_5088105.pd

Multiple Category Decision Making: Review and Synthesis

In many purchase environments, consumers use information from a number of product categories prior to making a decision. These purchase situations create dependencies in choice outcomes across categories. As such, these decision problems cannot be easily modeled using the single-category, single-choice paradigm commonly used by researchers in marketing. We outline a conceptual framework for categorization, and then discuss three types of cross-category dependence: cross-category consideration cross-category learning, and product bundling. We argue that the key to modeling choice dependence across categories is knowledge of the goals driving consumer behavior