How to Sell a Dataset? Pricing Policies for Data Monetization
63 Pages Posted: 19 Feb 2019 Last revised: 23 Jan 2020
Date Written: August 1, 2019
The wide variety of pricing policies used in practice by data-sellers suggests that there are significant challenges in pricing datasets. 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. A buyer interested in purchasing a dataset has private valuations in two aspects -- her ideal record that she values the most, and the rate at which her valuation for the records in the dataset decays as they differ from her ideal record. The seller allows individual buyers to filter the dataset and select the records that are of interest to them. The multi-dimensional private information of the buyers coupled with the endogenous selection of records makes the seller's problem of optimally pricing the dataset 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. More generally, we obtain an approximation scheme that can guarantee a revenue which is arbitrarily close to the optimal revenue. We also demonstrate how the seller can exploit buyers' preferences to generate intuitive and useful rules of thumb for an effective practical implementation of the scheme.
Keywords: Data Monetization, Multi-Dimensional Mechanism Design, Price-Quantity Schedules, Worst-Case Guarantees, Approximation Scheme
JEL Classification: C61, D44, D82, D47
Suggested Citation: Suggested Citation