Online Product Reviews-Triggered Dynamic Pricing: Theory and Evidence
作者研究了在线零售商如何利用产品评论数据动态调整价格。他们先构建理论模型，分析卖家在考虑在线口碑信息时的最优定价策略。模型发现，根据消费者特征（如错配成本）和产品特征（如质量）的不同组合，企业可能采取撇脂定价或渗透定价。随后，作者用从在线书店收集的图书零售数据，为理论预测提供了实证支持。
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.