How to Sell a Data Set? Pricing Policies for Data Monetization

The wide variety of pricing policies used in practice by data sellers suggests that there are significant challenges in pricing data sets. In this paper, we develop a utility framework that is appropriate for data buyers and the corresponding pricing of the data by the data seller. Buyers interested in purchasing a data set have private valuations in two aspects-their ideal record that they value the most, and the rate at which their valuation for the records in the data set decays as they differ from the buyers' ideal record. The seller allows individual buyers to filter the data set and select the records that are of interest to them. The multidimensional private information of the buyers coupled with the endogenous selection of records makes the seller's problem of optimally pricing the data set a challenging one. We formulate a tractable model and successfully exploit its special structure to obtain optimal and near-optimal data-selling mechanisms. Specifically, we provide insights into the conditions under which a commonly used mechanism-namely, a price quantity schedule-is optimal for the data seller. When the conditions leading to the optimality of a price-quantity schedule do not hold, we show that the optimal price-quantity schedule offers an attractive worst-case guarantee relative to an optimal mechanism. Further, we numerically solve for the optimal mechanism and show that the actual performance of two simple and well-known price-quantity schedules-namely, two-part tariff and two-block tariff-is near optimal. We also quantify the value to the seller from allowing buyers to filter the data set.