DP17544 Data, Competition, and Digital Platforms
We propose a model of intermediated digital markets where data and heterogeneity in tastes and products are defining features. A monopolist platform uses superior data to match consumers and multiproduct advertisers. Consumers have heterogenous preferences for the advertisers' product lines and shop on- or off-platform. The platform monetizes its data by selling targeted advertising space that allows advertisers to tailor their products to each consumer's preferences. We derive the equilibrium product lines and advertising prices. We identify search costs and informational advantages as two sources of the platform's bargaining power. We show that privacy-enhancing data-governance rules, such as those corresponding to federated learning, can lead to welfare gains for the consumers.