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.
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