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. Moreover, product researchers often evaluate a large number of these attributes (e.g., >50) in applied studies, making standard valuation techniques such as conjoint analysis difficult to implement. Product researchers instead rely on ratings data to assess features for which the respondent has had actual experience. 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 product use study by a packaged goods manufacturer.

Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2016.0980.