Optimal product positioning based on paired comparison data

A new approach for analyzing paired comparison data is proposed which combines a probabilistic ideal point model with product positioning issues. Unlike traditional approaches based on paired comparison data the same formulation is used for estimating a joint space representation of consumer segments and products as well as for determining optimal (new) product positioning options in a relevant product-market. A Monte Carlo experiment is presented and real-world coffee market data are used to show advantages of the new approach.