"Tailoring Data for Profit"Link opens in a new window

I develop a theoretical framework to analyze the optimal sale of information products in the digital economy. A data buyer initially has access to a private information structure that provides partial information about a payoff-relevant state. The buyer's initial information determines their willingness to pay for additional data, creating information asymmetry that poses challenges for a monopolist data seller aiming to maximize revenue. My main findings show that the seller can extract the first-best surplus and achieve social efficiency by offering customized supplemental information products tailored to different buyer types. Specifically, the seller can implement first-degree price discrimination by strategically leveraging data correlations. Compared to Bergemann, Bonatti, and Smolin (2018), the novelty of my work lies in two key aspects: (i) the type space consists of various information structures, and (ii) correlations are allowed between the data buyer's initial information structure and the additional information offered by the seller.