Selling Data

I study how a monopolist data broker (seller), who wants to maximize profits, should present and sell consumer data to a firm (buyer). The buyer has an interest in forecasting a particular consumer characteristic, but the seller is uncertain about which characteristic the buyer wants to forecast and how much the buyer values information. I assume that the joint distribution of both the unknown characteristics and the data is elliptical. This information environment reduces to a multidimensional, multi-product mechanism design problem in which the buyer’s payoffs are nonlinear. Hence, I cannot use the common differential approach to solve for the optimal mechanism. I obtain two main results. First, I show that the seller should optimally offer statistics that are linear combinations of the data and independent noise. Second, by using a direct approach, I show that in the optimal mechanism the seller might want to offer a continuum of different statistics, and these statistics, without containing independent noise, are less correlated than they would be if the seller could perfectly price discriminate. Thus this distortion affects the mimicking type more than the mimicked type.

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