Targeting Information in Ad Auction Mechanisms

Abstract: Digital advertising platforms and publishers sell ad inventory that conveys targeting information, such as demographic, contextual, or behavioral audience segments, to advertisers. While revealing this information improves ad relevance, it can reduce competition and lower auction revenues. To resolve this trade-off, this paper develops a general auction mechanism -- the Information-Bundling Position Auction (IBPA) mechanism -- that leverages the targeting information to maximize publisher revenue across both search and display advertising environments. The proposed mechanism treats the ad inventory type as the publisher's private information and allocates impressions by comparing advertisers' marginal revenues. We show that IBPA resolves the trade-off between targeting precision and market thickness: publisher revenue is increasing in information granularity and decreasing in disclosure granularity. Moreover, IBPA dominates the generalized second-price (GSP) auction for any distribution of advertiser valuations and under any information or disclosure regime. We also characterize computationally efficient approximations that preserve these guarantees. Using auction-level data from a large retail media platform, we estimate advertiser valuation distributions and simulate counterfactual outcomes. Relative to GSP, IBPA increases publisher revenue by 68%, allocation rate by 19pp, advertiser welfare by 29%, and total welfare by 54%.