Alone, Together: Product Discovery through Consumer Ratings

Consumer ratings have become a prevalent driver of choice. I develop a model of social learning in which ratings can inform consumers about both product quality and their idiosyncratic taste for them. Depending on consumers’ prior knowledge, I show that ratings relatively advantage lower quality and more polarizing products. The reason lies in the stronger positive consumer self-selection these products generate: to buy them despite their deficiencies, their buyers must have a strong taste for them. Relatedly, consumer ratings should not be used to infer which products are polarizing: what is polarizing ex-ante needs not be so among its buyers. I test these predictions using Goodreads book ratings data, and find strong evidence for them. Goodreads appears to serve mostly a matching purpose: tracking the behavior of its users over time reveals an increasing degree of specialization as they gather experience on the platform: they rate books with a lower average and number of ratings, while focusing on fewer genres. Thus, they become less similar to their average peer. Taken together, the findings suggest that consumer ratings contribute to both the long tail and, relatedly, consumption segregation. For managers, this illustrates, counterintuitively, the reputational benefits of polarizing products, particularly early in a firm’s lifecycle, but only when paired with the ability to match with the right consumers.

[1]  Yishay Mansour,et al.  Implementing the “Wisdom of the Crowd” , 2013, Journal of Political Economy.

[2]  David Godes,et al.  Using Online Conversations to Study Word-of-Mouth Communication , 2004 .

[3]  K. Shue Consistent Good News and Inconsistent Bad News , 2017 .

[4]  Bart de Langhe,et al.  Navigating by the Stars: Investigating the Actual and Perceived Validity of Online User Ratings , 2016 .

[5]  Liran Einav,et al.  Uniform Prices for Differentiated Goods: The Case of the Movie-Theater Industry , 2007 .

[6]  Georgios Zervas,et al.  Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud , 2015, Manag. Sci..

[7]  Saad A. Alhoqail,et al.  How Online Product Reviews Affect Retail Sales: A Meta-analysis , 2014 .

[8]  Monic Sun,et al.  How Does the Variance of Product Ratings Matter? , 2010, Manag. Sci..

[9]  Asuman Ozdaglar,et al.  Fast and Slow Learning from Reviews , 2017 .

[10]  Luís M. B. Cabral,et al.  Reputation on the Internet , 2012 .

[11]  Chrysanthos Dellarocas,et al.  Tall Heads vs. Long Tails: Do Consumer Reviews Increase the Informational Inequality Between Hit and Niche Products? , 2007 .

[12]  D. Gilchrist,et al.  Something to Talk About: Social Spillovers in Movie Consumption , 2016, Journal of Political Economy.

[13]  Kostas Bimpikis,et al.  Crowdsourcing Exploration , 2018, Manag. Sci..

[14]  Swayed by the Numbers: The Consequences of Displaying Product Review Attributes , 2018 .

[15]  Xavier Vives,et al.  Why market shares matter: an information-based theory , 1996 .

[16]  Ying Liu,et al.  The Value of Multi-Dimensional Rating Systems: Evidence from a Natural Experiment and Randomized Experiments , 2017, Manag. Sci..

[17]  Joseph M. Golden,et al.  Reputation Inflation: Evidence from an Online Labor Market , 2015 .

[18]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[19]  Georgios Zervas,et al.  The groupon effect on yelp ratings: a root cause analysis , 2012, EC '12.

[20]  Heski Bar-Isaac,et al.  Search, Design and Market Structure , 2009 .

[21]  Yeon-Koo Che,et al.  Recommender Systems as Mechanisms for Social Learning , 2018 .

[22]  J. Kemp Global village. , 2016, Midwives.

[23]  E. Clemons,et al.  When Online Reviews Meet Hyperdifferentiation: A Study of the Craft Beer Industry , 2006 .

[24]  Ron Berman,et al.  Curation Algorithms and Filter Bubbles in Social Networks , 2019, Mark. Sci..

[25]  K. Holyoak,et al.  The Love of Large Numbers: A Popularity Bias in Consumer Choice , 2017, Psychological science.

[26]  Dina Mayzlin,et al.  Promotional Reviews: An Empirical Investigation of Online Review Manipulation , 2012 .

[27]  Steven Tadelis,et al.  Reputation and Feedback Systems in Online Platform Markets , 2016 .

[28]  S. Bikhchandani,et al.  You have printed the following article : A Theory of Fads , Fashion , Custom , and Cultural Change as Informational Cascades , 2007 .

[29]  Ryan Stevens,et al.  The Good, The Bad and The Picky: Consumer Heterogeneity and The Reversal of Movie Ratings , 2019 .

[30]  Pradeep Chintagunta,et al.  The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets , 2010, Mark. Sci..

[31]  E. Moretti Social Learning and Peer Effects in Consumption: Evidence from Movie Sales , 2008 .

[32]  Fang Wu,et al.  How Public Opinion Forms , 2008, WINE.

[33]  Grant D. Jacobsen Consumers, experts, and online product evaluations: Evidence from the brewing industry , 2015 .

[34]  Marco Scarsini,et al.  On Information Distortions in Online Ratings , 2016, Oper. Res..

[35]  Box‐Office Demand: The Importance of Being #1 , 2016 .

[36]  Michael Luca,et al.  What Makes a Critic Tick? Connected Authors and the Determinants of Book Reviews , 2013 .

[37]  A. Banerjee,et al.  A Simple Model of Herd Behavior , 1992 .

[38]  Amit M. Joshi,et al.  A Meta-Analysis of Electronic Word-of-Mouth Elasticity , 2015 .

[39]  David P. Myatt,et al.  On the Simple Economics of Advertising, Marketing, and Product Design , 2005 .

[40]  Paul Resnick,et al.  The value of reputation on eBay: A controlled experiment , 2002 .

[41]  Amin Sayedi Pricing in a Duopoly with Observational Learning , 2018 .

[42]  Marco Scarsini,et al.  Social Learning from Online Reviews with Product Choice , 2018, NetEcon@SIGMETRICS.

[43]  Dina Mayzlin,et al.  Promotional Reviews: An Empirical Investigation of Online Review Manipulation , 2012 .