Presented at the 2010 Sensometrics Conference in Rotterdam, The Netherlands
Portfolio optimization based on first choice is an application in which a need for discrete optimization can arise. In particular, when liking data have been collected in a category appraisal the data can be unfolded to create a map containing product and ideal point locations based on product-ideal similarity. On such a map, subjects tend to prefer products whose locations are close to their ideal points. Using such a map it is possible to determine optimal locations for new products so as to best compete strategically with competitor products. This is the setting for what is known as competitive optimization and in this setting we optimize the number of first choice counts obtained by products in our portfolio instead of by the competitor products. In other words we maximize the number of consumers that would be predicted to choose one of our products as their favorite product instead of choosing one of the competitor products. This problem is a discrete optimization problem, making it potentially very complex but also approachable using advanced mathematical tools. Once the optimal arrangement for the portfolio of our products has been determined it is then possible to create target profiles to guide product development.
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