Information Sharing and Personalized Pricing in Online Platforms

With the rise of big data technology, an online platform can easily gather customer information to engage in price discrimination and obtain additional profits. 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 find that the seller always has incentives to acquire information, while the platform is not always willing to share information. With different combinations of the commission rate and the new customer ratio, the equilibrium of the overall system has three possible results where the information is always used for price discrimination by either one party or two parties. With a high commission rate and a low new customer 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 most cases, prohibiting information sharing increases consumer surplus and social welfare, verifying the necessity of regulation. These results could provide useful guidelines for platform managers and regulators to better design information sharing and price discrimination policies