Screening with Advertisements

2026-01-29

AI Summary

This study investigates how a seller can optimally bundle advertising with a product to maximize revenue when buyers possess two-dimensional private information—namely, their valuation for the product and their tolerance for advertising. Building on the duality approach of Daskalakis et al., the authors introduce a transformed measure μ that depends on advertising revenue k, enabling the derivation of interpretable and tractable sufficient conditions in the two-dimensional type space. These conditions delineate the optimality boundaries among three pricing mechanisms: Good-Only, Ad-Tiered, and Single-Bundle. The analysis reveals clear comparative statics with respect to k: for low advertising revenue, ads are excluded; for moderate k, the seller segments users by type; and for high k, all buyers are offered a bundled package. This work thus establishes the pivotal role of advertising revenue levels in shaping the optimal selling strategy.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Search and Optimization: Adversarial Search
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Advertising auctions, pricing, markets, and exchanges
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Search and Retrieval-Augmented AI: Ad search and search for Web retail

Abstract

We investigate a seller's revenue-maximizing mechanism in a setting where a desirable good is sold together with an undesirable bad (e.g., advertisements) that generates third-party revenue. The buyer's private information is two-dimensional: valuation for the good and willingness to pay to avoid the bad. Following the duality framework of Daskalakis, Deckelbaum, and Tzamos (2017), whose results extend to our setting, we formulate the seller's problem using a transformed measure $\mu$ that depends on the third-party payment $k$. We provide a near-characterization for optimality of three pricing mechanisms commonly used in practice -- the Good-Only, Ad-Tiered, and Single-Bundle Posted Price -- and introduce a new class of tractable, interpretable two-dimensional orthant conditions on $\mu$ for sufficiency. Economically, $k$ yields a clean comparative static: low $k$ excludes the bad, intermediate $k$ separates ad-tolerant and ad-averse buyers, and high $k$ bundles ads for all types.

Problem

• revenue maximization
• two-dimensional private information
• advertising
• bundling
• mechanism design

Innovation

• mechanism design
• duality framework
• two-dimensional screening
• orthant conditions
• advertising revenue