Statistical Perceptual Maps: Using Confidence Region Ellipses to Enhance the Interpretations of Brand Positions in Multidimensional Scaling

Abstract

Positioning is among a marketer’s preeminent strategic responsibilities. Positioning helps to clarify brand strengths among competitors and identify potential challenges of similar brands and possible substitutability. Assessments of positioning, from initial marketplace efforts to resources directed at modifications and re-positioning, are frequently assisted by the graphical representations of brands in multidimensional space. Such perceptual maps are constructed to reflect the closeness of brands and therefore the extent to which they are seen as interchangeable, versus distances between brands representing their relative positioning distinctiveness. To create perceptual maps, data are frequently obtained that comprise a sample of respondents rating a series of brands with respect to their perceived similarities and differences, as well as the status of each brand along multiple attributes. This research uses the variability inherent in such three dimensional data to construct confidence regions around point estimates in perceptual maps. Current maps tend to be simply descriptive, with positions reflected by point estimates, but multivariate models including multidimensional scaling and multi-mode factor analysis can be modified to extract the subject heterogeneity and derive inferential perceptual maps. Confidence regions that overlap will indicate more clearly an inference of brand similarity, whereas non-overlapping regions imply statistically differentiated brand perceptions.

Keywords: Multidimensional scaling, Perceptual maps, Confidence regions