Optimal Aggregation of Consumer Ratings: An Application to Yelp.com
Consumer review websites leverage the wisdom of the crowd, with each product being reviewed many times (some with more than 1,000 reviews). Because of this, the way in which information is aggregated is a central decision faced by consumer review websites. Given a set of reviews, what is the optimal way to construct an average rating? We offer a structural approach to answering this question, allowing for (1) reviewers to vary in stringency and accuracy, (2) reviewers to be influenced by existing reviews, and (3) product quality to change over time. Applying this approach to restaurant reviews from Yelp.com, we construct optimal ratings for all restaurants and compare them to the arithmetic averages displayed by Yelp. Depending on how we interpret the downward trend of reviews within a restaurant, we find 19.1-41.38% of the simple average ratings are more than 0.15 stars away from optimal ratings, and 5.33-19.1% are more than 0.25 stars away at the end of our sample period. Moreover, the deviation grows significantly as a restaurant accumulates reviews over time. This suggests that large gains could be made by implementing optimal ratings, especially as Yelp grows. Our algorithm can be flexibly applied to many different review settings.
|Date of creation:||Nov 2012|
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- Nolan Miller & Paul Resnick & Richard Zeckhauser, 2005. "Eliciting Informative Feedback: The Peer-Prediction Method," Management Science, INFORMS, vol. 51(9), pages 1359-1373, September.
- Yan Chen & F. Maxwell Harper & Joseph Konstan & Sherry Xin Li, 2010. "Social Comparisons and Contributions to Online Communities: A Field Experiment on MovieLens," American Economic Review, American Economic Association, vol. 100(4), pages 1358-1398, September.
- Ariely, Dan & Bracha, Anat & Meier, Stephan, 2007.
"Doing Good or Doing Well? Image Motivation and Monetary Incentives in Behaving Prosocially,"
IZA Discussion Papers
2968, Institute for the Study of Labor (IZA).
- Dan Ariely & Anat Bracha & Stephan Meier, 2009. "Doing Good or Doing Well? Image Motivation and Monetary Incentives in Behaving Prosocially," American Economic Review, American Economic Association, vol. 99(1), pages 544-555, March.
- Dan Ariely & Anat Bracha & Stephan Meier, 2007. "Doing good or doing well? Image motivation and monetary incentives in behaving prosocially," Working Papers 07-9, Federal Reserve Bank of Boston.
- Michael Luca & Jonathan Smith, 2011.
"Salience in Quality Disclosure: Evidence from the U.S. News College Rankings,"
Harvard Business School Working Papers
12-014, Harvard Business School.
- Michael Luca & Jonathan Smith, 2013. "Salience in Quality Disclosure: Evidence from the U.S. News College Rankings," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 22(1), pages 58-77, 03.
- Jonathan E. Alevy & Michael S. Haigh & John A. List, 2007.
"Information Cascades: Evidence from a Field Experiment with Financial Market Professionals,"
Journal of Finance,
American Finance Association, vol. 62(1), pages 151-180, 02.
- Alevy, Jonathan E. & Haigh, Michael S. & List, John A., 2003. "Information Cascades: Evidence From A Field Experiment With Financial Market Professionals," Working Papers 28608, University of Maryland, Department of Agricultural and Resource Economics.
- Jonathan Alevy & Michael Haigh & John List, 2005. "Information cascades: Evidence from a field experiment with financial market professionals," Framed Field Experiments 00116, The Field Experiments Website.
- Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
- Dina Mayzlin, 2006. "Promotional Chat on the Internet," Marketing Science, INFORMS, vol. 25(2), pages 155-163, 03-04.
- Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
- David Godes & Dina Mayzlin, 2009. "Firm-Created Word-of-Mouth Communication: Evidence from a Field Test," Marketing Science, INFORMS, vol. 28(4), pages 721-739, 07-08.
- Chrysanthos Dellarocas, 2006. "Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms," Management Science, INFORMS, vol. 52(10), pages 1577-1593, October.
- Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2007. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Working Papers 07-36, NET Institute.
- Pope, Devin G., 2009. "Reacting to rankings: Evidence from "America's Best Hospitals"," Journal of Health Economics, Elsevier, vol. 28(6), pages 1154-1165, December.
- Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, Oxford University Press, vol. 107(3), pages 797-817.
- Glazer, Jacob & McGuire, Thomas G. & Cao, Zhun & Zaslavsky, Alan, 2008. "Using global ratings of health plans to improve the quality of health care," Journal of Health Economics, Elsevier, vol. 27(5), pages 1182-1195, September.
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