Behavior-Based Personalized Pricing: Consumer Privacy Concerns and Information Structure

The proliferation of consumer data and advanced data analytics techniques (e.g., machine learning), has empowered firms to implement personalized pricing by learning individual consumer preferences. Behavioral research and anecdotal evidence suggest that consumers exhibit aversion to such a pricing practice for various reasons, such as fear of price fraud, perceived violation of social norms, unfairness, privacy concerns, etc. However, little research has examined how consumers’ aversion, which inflicts a disutility on consumers, influences the impact of personalized pricing on firms and consumers in a competitive setting. Using a game-theoretic model, we investigate the effect of consumers’ aversion when competing firms use personalized pricing in a market where firms have differing extents of information access to different consumer segments (i.e., different consumer addressability). Interestingly, our result shows that personalized pricing does not necessarily hurt consumers even we consider consumers are averse to it. Contrary to conventional wisdom, firms’ profits from conducting personalized pricing may increase with consumers’ aversion level, whereas consumer surplus decreases. The driving force is that a high aversion level enables firms to poach exclusive target consumers of the rival firm at a high uniform price. Finally, when consumers’ aversion is not very high, greater exclusive access to consumer data can work against the firms. Our findings provide implications for managers on using consumer data for personalized pricing and add to policy debates on how personalized pricing could affect consumer interests.