Targeting Information in Ad Auction Mechanisms
Srinivas Tunuguntla, Carl F. Mela, Jason Pratt

TL;DR
This paper introduces the Information-Bundling Position Auction (IBPA), a novel auction mechanism that optimally balances ad targeting information disclosure and revenue maximization in digital advertising.
Contribution
The paper develops IBPA, a new auction mechanism that improves revenue and welfare by effectively leveraging targeting information while dominating existing auctions like GSP.
Findings
IBPA increases publisher revenue by 68% compared to GSP.
IBPA improves allocation rate by 19 percentage points.
IBPA enhances advertiser and total welfare significantly.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsConsumer Market Behavior and Pricing · Auction Theory and Applications · Digital Platforms and Economics
