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Targeting Information in Ad Auction Mechanisms

Srinivas Tunuguntla, Carl F. Mela, Jason Pratt

TL;DR

The paper tackles how publishers should monetize impression opportunities when targeting information is privately observed, introducing IBPA, a mechanism that combines information bundling with marginal-revenue allocation across multidimensional advertiser valuations. By treating inventory type as private and soliciting multidimensional bids, IBPA implements second-degree price discrimination within each advertiser and a Myerson-style marginal-revenue allocation across advertisers and slots, ensuring incentive compatibility and non-negative advertiser payoffs. The authors prove that IBPA revenue is monotone in information granularity and decreasing in disclosure, and that IBPA dominates GSP under all information regimes. They also provide computationally efficient approximations and validate the approach with auction data from a large retailer, reporting substantial gains in publisher revenue and overall welfare. The work has practical implications for ad auctions in both display and search, offering a scalable, principled alternative to current practices and inviting further exploration of transparency and equilibrium effects in multi-publisher ecosystems.

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%.

Targeting Information in Ad Auction Mechanisms

TL;DR

The paper tackles how publishers should monetize impression opportunities when targeting information is privately observed, introducing IBPA, a mechanism that combines information bundling with marginal-revenue allocation across multidimensional advertiser valuations. By treating inventory type as private and soliciting multidimensional bids, IBPA implements second-degree price discrimination within each advertiser and a Myerson-style marginal-revenue allocation across advertisers and slots, ensuring incentive compatibility and non-negative advertiser payoffs. The authors prove that IBPA revenue is monotone in information granularity and decreasing in disclosure, and that IBPA dominates GSP under all information regimes. They also provide computationally efficient approximations and validate the approach with auction data from a large retailer, reporting substantial gains in publisher revenue and overall welfare. The work has practical implications for ad auctions in both display and search, offering a scalable, principled alternative to current practices and inviting further exploration of transparency and equilibrium effects in multi-publisher ecosystems.

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%.
Paper Structure (45 sections, 10 theorems, 85 equations, 5 figures, 1 table)

This paper contains 45 sections, 10 theorems, 85 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background and Related Literature
  3. Digital Advertising
  4. Information Asymmetries in Auctions
  5. Vertical Quality Information Asymmetries
  6. Horizontal Quality Information Asymmetries
  7. Privacy in Advertising Targeting
  8. Mixed-Bundle Pricing
  9. The Marginal Revenue Framework
  10. Problem Setup and Proposed Mechanism
  11. Model
  12. Advertiser Problem
  13. Publisher Problem
  14. Information-Bundling Position Auction (IBPA)
  15. Single Advertiser Mechanisms
  16. ...and 30 more sections

Key Result

Proposition 1

For any mechanism $M$ that includes a lottery pricing $k$ with $\chi_{\tilde{s}\tilde{t}}^{(k)}>0$ for some $\tilde{s}\neq s_{0},\;\tilde{t}$, there exists another mechanism $M'$ in which $\chi_{st}^{(k)}=0\;\forall s\neq s_{0}\;\forall t$ such that the publisher's expected revenue under $M'$ is equ

Figures (5)

  • Figure 1: Distribution of inventory types
  • Figure 2: Distribution of average bid vs. quality score
  • Figure 3: Variation of click-through rate by slot
  • Figure 4: Distribution of valuations for randomly sampled inventory-types
  • Figure 5: Revenue Comparison

Theorems & Definitions (25)

  • Proposition 1
  • proof
  • Proposition 2
  • proof
  • Proposition 3
  • proof
  • Theorem 4
  • proof
  • Theorem 5
  • proof
  • ...and 15 more