Designing Ad Auctions with Targeting Information
Digital advertising 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 address this trade-off, we develop the Information-Bundling Position Auction (IBPA), a general auction mechanism for search and display advertising that leverages targeting information while preserving competition. The mechanism treats the realized audience segment as the publisher's private information. For a given advertiser, IBPA implements information bundling through mixed-bundle pricing over segments; across advertisers, it applies the marginal-revenue framework to allocate impressions and determine payments. We show that IBPA is Bayesian incentive compatible and individually rational: truthful bidding forms a Bayes--Nash equilibrium and advertisers receive non-negative expected utility from participation. Moreover, IBPA achieves at least 63% of the revenue of the optimal feasible mechanism and weakly dominates all scalar-bid disclosure mechanisms, including generalized second-price (GSP) auctions. The mechanism also resolves the trade-off between targeting precision and market thickness: publisher revenue is increasing in information granularity and decreasing in disclosure granularity. 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 91%, allocation rate by 26pp, advertiser welfare by 62%, and total welfare by 79%.