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- Title
Online Product Reviews-Triggered Dynamic Pricing: Theory and Evidence.
- Authors
Feng, Juan; Li, Xin; Zhang, Xiaoquan (Michael)
- Abstract
Online product reviews are arguably one of the most easily accessible sources of marketing data for online retailers. It is possible to build machine learning tools to learn consumers' opinions from online word of mouth (WOM). Menu costs are practically trivial for online retailers, and it is not difficult to program automatic price changes based on live feeds of online review data. This paper argues that sellers can use online product reviews to develop better pricing strategies. We first build a theoretical model to examine a seller's optimal pricing strategy when online WOM information is taken into consideration. We find that, with consumer reviews, firms may take price-skimming and penetration strategies depending on the combination of consumer characteristics (such as misfit cost) and product characteristics (such as product quality). We examine a book retailing data set collected from online stores to offer empirical support for the analytical predictions. Prior works offer compelling evidence that, on the demand side of the market, user-generated online product reviews play a very important role in informing consumers' purchase decisions. On the supply side, however, the interplay between online product reviews and firm strategies is less understood. We build an analytical model that differentiates products based on consumers' preference for tastes (horizontal differentiation) or quality (vertical differentiation) and show that a firm is able to not only manipulate its pricing to influence online product reviews (thus influencing sales) but also, adjust pricing dynamically in response to online word of mouth. Our model derives rich and testable results on possible price trajectories. To offer empirical support for the analytical predictions, we conduct a panel data study of prices and reviews. We adopt a difference-in-differences framework to address endogeneity challenges.
- Subjects
MICROECONOMICS; TIME-based pricing; PRODUCT reviews; MACHINE learning; THEATER reviews
- Publication
Information Systems Research, 2019, Vol 30, Issue 4, p1107
- ISSN
1047-7047
- Publication type
Article
- DOI
10.1287/isre.2019.0852