An inter model distance for clustering utility function

Conjoint Analysis is one of the most widely used techniques in the assessment of the consumer’s behaviors. This method allows to estimate the partial utility coefficients according to a statistical model linking the overall note of preference with the attribute levels describing the stimuli. Conjoint analysis results are useful in new-product positioning and market segmentation. In this paper a cluster-based segmentation strategy based on a new metric has been proposed. The introduced distance is based on a convex linear combination of two Euclidean distances em bedding information both on the estimated parameters and on the model fitting. Market segments can be then defined according to the proximity of the part-worth coecients and to the explicative power of the estimated models