Discreet Personalized Pricing

Emerging tracking data allow precise predictions of individuals' reservation values. However, firms are reluctant to conspicuously implement personalized pricing because of concerns about consumer and regulatory reprisals. This paper proposes and applies a method which disguises personalized pricing as dynamic pricing. Specifically, a firm can sometimes tailor the "posted" price for the arriving consumer but privately commits to change price infrequently. Note such pricing may unintentionally arise through algorithmic pricing. I examine outcomes in four contexts: one empirical and three hypothetical distributions of consumer valuations. I find that this strategy is most intense and raises profits most for medium popularity products. Furthermore, improvements in the precision of individual-level demand estimates raise the range of popularities this strategy can be profitably applied to. I conclude that this is an auspicious strategy for online platforms, if not already secretly in use.