Extentions of probabilistic perceptual maps with implications for competitive positioning and choice

Abstract

Perceptual product positioning maps which are derived from probabilistic scaling models possess some distinct advantages over their deterministic counterparts. However, many probabilistic models still labor under a number of restrictive mathematical conditions. This paper describes an anisotropic space extension that alleviates some of these limitations by explicitly modeling the dimensional variances and covariances of each brand in a product positioning map. To clarify the decisions necessary when using probabilistic scaling models and to illustrate some of their attractive properties, two sets of convenience goods data are analyzed. The applications focus on the model's implications for the understanding of brand positioning and choice probabilities.