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Monetizing Ratings Data for Product Research

Author

Listed:
  • Nino Hardt

    (Fisher College of Business, Ohio State University, Columbus, Ohio 43210)

  • Alex Varbanov

    (The Procter & Gamble Company, Cincinnati, Ohio 45202)

  • Greg M. Allenby

    (Fisher College of Business, Ohio State University, Columbus, Ohio 43210)

Abstract

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 .

Suggested Citation

  • Nino Hardt & Alex Varbanov & Greg M. Allenby, 2016. "Monetizing Ratings Data for Product Research," Marketing Science, INFORMS, vol. 35(5), pages 713-726, September.
    • Handle: RePEc:inm:ormksc:v:35:y:2016:i:5:p:713-726
    DOI: 10.1287/mksc.2016.0980
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    References listed on IDEAS

    as
    1. Greg Allenby & Jeff Brazell & John Howell & Peter Rossi, 2014. "Economic valuation of product features," Quantitative Marketing and Economics (QME), Springer, vol. 12(4), pages 421-456, December.
    2. Lynd Bacon & Peter Lenk, 2012. "Augmenting discrete-choice data to identify common preference scales for inter-subject analyses," Quantitative Marketing and Economics (QME), Springer, vol. 10(4), pages 453-474, December.
    3. Rossi P. E & Gilula Z. & Allenby G. M, 2001. "Overcoming Scale Usage Heterogeneity: A Bayesian Hierarchical Approach," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 20-31, March.
    4. Chandukala, Sandeep R. & Kim, Jaehwan & Otter, Thomas & Rossi, Peter E. & Allenby, Greg M., 2008. "Choice Models in Marketing: Economic Assumptions, Challenges and Trends," Foundations and Trends(R) in Marketing, now publishers, vol. 2(2), pages 97-184, September.
    5. Joachim Büschken & Thomas Otter & Greg M. Allenby, 2013. "The Dimensionality of Customer Satisfaction Survey Responses and Implications for Driver Analysis," Marketing Science, INFORMS, vol. 32(4), pages 533-553, July.
    6. Garrett Sonnier & Andrew Ainslie & Thomas Otter, 2007. "Heterogeneity distributions of willingness-to-pay in choice models," Quantitative Marketing and Economics (QME), Springer, vol. 5(3), pages 313-331, September.
    7. Timothy Johnson, 2003. "On the use of heterogeneous thresholds ordinal regression models to account for individual differences in response style," Psychometrika, Springer;The Psychometric Society, vol. 68(4), pages 563-583, December.
    8. Allenby, Greg M. & Rossi, Peter E., 1998. "Marketing models of consumer heterogeneity," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 57-78, November.
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