No. 2002-82 ADAPTIVE MULTIDIMENSIONAL SCALING: THE SPATIAL REPRESENTATION OF BRAND CONSIDERATION AND DISSIMILARITY JUDGMENTS

We propose Adaptive Multidimensional Scaling (AMDS) for simultaneously deriving a brand map and market segments using consumer data on cognitive decision sets and brand dissimilarities. In AMDS, the judgment task is adapted to the individual respondent: dissimilarity judgments are collected only for those brands within a consumers’ awareness set. Thus, respondent fatigue and subjects' unfamiliarity with any subset of the brands are circumvented; thereby improving the validity of the dissimilarity data obtained, as well as the multidimensional spatial structure derived. Estimation of the AMDS model results in a spatial map in which the brands and derived segments of consumers are jointly represented as points. The closer a brand is positioned to a segment’s ideal brand, the higher the probability that the brand is considered and chosen. An assumption underlying this model representation is that brands within a consumers’ consideration set are relatively similar. In an experiment with 200 subjects and 4 product categories, this assumption is validated. We illustrate adaptive multidimensional scaling on commercial data for 20 midsize car brands evaluated by 212 members of a consumer panel. Potential applications of the method and future research opportunities are discussed.