Multidimensional Scaling of Preference Data

Multidimensional scaling represents a family of various geometric models for the multidimensional representation of the structure in data as well as the corresponding set of methods for fitting such spatial models. Its major uses in marketing include positioning, market segmentation, new-product design, consumer preference analysis, and so on. We present two popular multidimensional scaling models for the analysis of consumer preference data. The first model presented is called the vector or scalar products model that represents brands by points and consumers by vectors in a T-dimensional derived joint space. The second model is called the multidimensional unfolding or ideal point model where both brands and consumers are jointly represented by points in a T-dimensional derived joint space. We contrast the underlying utility assumptions implied by each of these two models with illustrative figures of typical joint spaces derived from each approach. An actual commercial application of consideration to buy large sports utility vehicles is provided with the empirical results from each model discussed. Implications for positioning are also revealed