The promise of consumer data along with advances in information technology has spurred innovation not only in the way firms conduct their business operations but also in the manner in which data are collected. A prominent institutional structure that has recently emerged is a data cooperative —an organization that collects data from its members, and processes and monetizes the pooled data. A characteristic of consumer data is the externality it generates: Data shared by an individual reveal information about other similar individuals; thus, the marginal value of pooled data increases in both the quantity and quality of the data. A key challenge faced by a data cooperative is the design of a revenue‐allocation scheme for sharing revenue with its members. An effective scheme generates a beneficial cycle: It incentivizes members to share high‐quality data, which in turn results in high‐quality pooled data—this increases the attractiveness of the data for buyers and hence the cooperative's revenue, ultimately resulting in improved compensation for the members. While the cooperative naturally wishes to maximize its total surplus, two other important desirable properties of an allocation scheme are individual rationality and coalitional stability. We first examine a natural proportional allocation scheme —which pays members based on their individual contribution—and show that it simultaneously achieves individual rationality, the first‐best outcome, and coalitional stability, when members' privacy costs are homogeneous. Under heterogeneity in privacy costs, we analyze a novel hybrid allocation scheme and show that it achieves both individual rationality and the first‐best outcome, but may not satisfy coalitional stability. Finally, our RobinHood allocation scheme —which uses a fraction of the revenue to ensure coalitional stability and allocates the remaining based on the hybrid scheme—achieves all the desirable properties.

Digital Content Platforms (DCPs), such as Netflix and Spotify, rely on subscriptions and advertising as their primary revenue sources. Beyond pure subscription-only and ad-only revenue models, DCPs increasingly blend these models by offering a two-tier menu: a free, ad-supported tier for price-sensitive users and a paid, ad-free tier for ad-sensitive users. In this paper, we introduce the Generalized Ad-Supported Subscription (GAS) mechanism – a broad class of subscription-fee and ad-intensity combinations that spans the continuum from ad-only to subscription-only – and nests the traditional mechanisms as special cases. Using a mechanism-design framework, we characterize the revenue-maximizing GAS mechanism and compare its performance with the optimal ad-only, subscription-only, and two-tier mechanisms. While GAS is optimal within this broad class, the simpler two-tier mechanism can achieve near-optimal revenue. We then characterize conditions under which the GAS mechanism delivers a material revenue advantage over the two-tier mechanism. Finally, we estimate our model parameters and empirically validate the theoretical results in the context of Video-on-Demand platforms.

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.