Crowd-Driven Competitive Intelligence: Understanding the Relationship between Local Market Structure and Online Rating Distributions

Abstract

Crowdsourced information, such as online ratings, are increasingly viewed as a critical source for understanding local market dynamics. A key aspect of utilizing online ratings to derive competitive market intelligence is to delineate the systematic relationship between local market structure and distributional properties of online ratings. As one of the earliest papers in this stream, combining demographic, population, and restaurant review data from Yelp.com for 372 isolated markets in the U.S., our empirical findings suggest that an increase in competition leads to a broader range of ratings and to a decrease in the average mean rating in a market. These effects are particularly pronounced when the analysis is limited to specific restaurant types where there are fewer opportunities for horizontal differentiation. To gain richer insights into the empirical results, we adopt the classical theoretical lens of an oligopoly where firms vertically differentiate their quality offerings in the presence of heterogeneous consumers and marginal costs that increase quadratically in quality. Moreover, we present evidence in support of both the internal and external validity of Yelp’s crowdsourced online ratings, validating the role that online rating distributions can play in helping scholars and managers understand competitive dynamics in local markets.