Platform Competition in the Autobidding World
Gagan Aggarwal, Andres Perlroth, Ariel Schvartzman, Mingfei Zhao

TL;DR
This paper analyzes auction design for advertising platforms with strategic advertisers under ROI constraints, revealing that in multi-platform settings, second-price auctions can outperform first-price auctions due to competition effects.
Contribution
It demonstrates that in multi-platform advertising, second-price auctions can be more revenue- and welfare-optimal than first-price auctions, contrary to single-platform scenarios.
Findings
Second-price auctions can dominate first-price auctions in multi-platform settings.
Competition intensity influences the optimal auction format.
Bid landscape sensitivity affects platform auction choices.
Abstract
We study the problem of auction design for advertising platforms that face strategic advertisers who are bidding across platforms. Each advertiser's goal is to maximize their total value or conversions while satisfying some constraint(s) across all the platforms they participates in. In this paper, we focus on advertisers with return-over-investment (henceforth, ROI) constraints, i.e. each advertiser is trying to maximize value while making sure that their ROI across all platforms is no less than some target value. An advertiser interacts with the platforms through autobidders -- for each platform, the advertiser strategically chooses a target ROI to report to the platform's autobidder, which in turn uses a uniform bid multiplier to bid on the advertiser's behalf on the queries owned by the given platform. Our main result is that for a platform trying to maximize revenue, competition…
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Taxonomy
TopicsDigital Platforms and Economics
