Managed Campaigns and Data-Augmented Auctions for Digital Advertising

Dirk Bergemann, Alessandro Bonatti and Nick Wu

No 18087, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We develop an auction model for digital advertising. A monopoly platform has access to data on the value of the match between advertisers and consumers. The platform support bidding with additional information and increase the feasible surplus for on-platform matches. Advertisers jointly determine their pricing strategy both on and off the platform, as well as their bidding for digital advertising on the platform. We compare a data-augmented second-price auction and a managed campaign mechanism. In the data-augmented auction, the bids by the advertisers are informed by the data of the platform regarding the value of the match. This results in a socially efficient allocation on the platform, but the advertisers increase their product prices off the platform to be more competitive on the platform. In consequence, the allocation off the platform is inefficient due to excessively high product prices. The managed campaign mechanism allows advertisers to submit budgets that are then transformed into matches and prices through an autobidding algorithm. Compared to the data-augmented second-price auction, the optimal managed campaign mechanism increases the revenue of the digital platform. The product prices off the platform increase and the consumer surplus decreases.

Keywords: Matching; Competition; Advertising; Auctions; Data (search for similar items in EconPapers)
JEL-codes: D44 D82 D83 (search for similar items in EconPapers)
Date: 2023-04
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Working Paper: Managed Campaigns and Data-Augmented Auctions for Digital Advertising (2023)
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