Monetizing Ratings Data for Product Research

Features involving the taste, smell, touch and sight of products, as well as attributes such as safety and confidence, are not easily measured in product research without respondents actually experiencing them for themselves. Moreover, product researchers often evaluate a large number of these of attributes in applied studies, making standard valuation techniques such as conjoint analysis di cult to implement. Product researchers instead rely on ratings data to assess experiential features of a product, asking respondents to evaluate a large number of features over a small number of test products. In this paper we develop a method of monetizing rating data to standardize product evaluations among respondents. The adjusted data are shown to increase the accuracy of purchase predictions by about 20% relative to existing methods of scale adjustment, leading to better inference in models using ratings data. We demonstrate our method using data from a large-scale brand positioning study of a packaged goods manufacturer