Title: Tailoring Data for Profit

Abstract: This paper develops a framework to analyze the optimal sale of information. A data buyer, facing a decision problem under uncertainty, initially has access to an information structure that is private to him and determines his willingness to pay for any additional information. A monopolistic data seller, capable of designing tailored information structures, seeks to maximize revenue. Compared to Bergemann, Bonatti, and Smolin (2018), the novelty of this paper lies in two key features: (i) the type space consists of various information structures, and (ii) correlations are allowed between the data buyer’s initial information and the additional information offered by the data seller. The main result demonstrates that, in a large class of cases, the data seller can design and price information within a mechanism to fully extract the first-best surplus. Specifically, full surplus extraction is achievable when each lower-type buyer’s willingness to pay for information that fully supplements their initial information is weakly higher than that of all higher types.

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