Information Sharing and Personalized Pricing in Online Platforms

Abstract

With the rise of big data technology, an online platform can easily gather customer information to engage in price discrimination and obtain additional profits. Sharing customer information with a third-party seller increases the platform’s commission and information revenue, but the seller’s personalized pricing using customer information intensifies the price competition, which may damage the profitability of the platform’s own product. Whether to share information remains an unsolved strategy decision for the platform. We employ a game-theoretic model to characterize the interplay of information sharing by the platform and the pricing strategies of two firms. We consequently study four basic scenarios where the two firms adopt either uniform or personalized pricing policies. In equilibrium, the seller does not always have incentives to acquire information, and the platform is not always willing to share information. Intriguingly, with different combinations of the commission rate and the new consumer ratio, the equilibrium of the overall system has four possible results where the information may not be used for price discrimination. With a relatively high commission rate and a low new consumer ratio, the platform no longer pursues a demand for its own product and lets the seller occupy the whole market, which leads to the lowest consumer surplus and social welfare. We finally show that in the event of a relatively high commission rate, prohibiting information sharing increases consumer surplus and social welfare, verifying the necessity of regulations. These results could provide useful guidelines for platform managers and regulators to better design information sharing and price discrimination policies.