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Wednesday, November 13, 2019 at 11:00am to 12:00pm
Cornell Tech, Bloomberg Center, Room 161
"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 rea- son 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 product design: 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 bene- fits of polarizing products, particularly early in a firm’s lifecycle, but only when paired with the ability to match with the right consumers.
I'm a Ph.D. candidate in Economics at the New York University Stern School of Business. I expect to graduate by May 2020.
My research interests lie in the Economics of Digitization, Quantitative Marketing, and Behavioral and Experimental Economics.
My Job Market Paper studies the effect of consumer ratings on market structure, and uses data from Goodreads, the world's largest book ratings platform.