Personalized Pricing Through Strategic User Profiling in Social Networks

Traditional user profiling techniques rely on browsing history or purchase records to identify users’ willingness to pay. This enables sellers to offer personalized prices to profiled users while charging only a uniform price to non-profiled users. However, the emergence of privacy-enhancing technologies has caused users to actively avoid on-site data tracking. Today, major online sellers have turned to public platforms such as online social networks to better track users’ profiles from their product-related discussions. This paper presents the first analytical study on how users should best manage their social activities against potential personalized pricing, and how a seller should strategically adjust her pricing scheme to facilitate user profiling in social networks. We formulate a dynamic Bayesian game played between the seller and users under asymmetric information. The key challenge of analyzing this game comes from the double couplings between the seller and the users as well as among the users. Furthermore, the equilibrium analysis needs to ensure consistency between users’ revealed information and the seller’s belief under random user profiling. We address these challenges by alternately applying backward and forward induction, and successfully characterize the unique perfect Bayesian equilibrium (PBE) in closed form. Our analysis reveals that as the accuracy of profiling technology improves, the seller tends to raise the equilibrium uniform price to motivate users’ increased social activities and facilitate user profiling. However, this results in most users being worse off after the informed consent policy is imposed to ensure users’ awareness of data access and profiling practices by potential sellers. This finding suggests that recent regulatory evolution towards enhancing users’ privacy awareness may have unintended consequences of reducing users’ payoffs. Finally, we examine prevalent pricing practices where the seller breaks a pricing promise to personalize final offerings, and show that it only slightly improves the seller’s average revenue while introducing higher variance.