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Abstract We present a model of digital advertising with three key features: (1) advertisers can reach consumers on and off a platform, (2) additional data enhances the value of advertiser–consumer matches, and (3) the allocation of advertisements follows an auction-like mechanism. We contrast data-augmented auctions, which leverage the platform’s data advantage to improve match quality, with managed-campaign mechanisms that automate match formation and price-setting. The platform-optimal mechanism is a managed campaign that conditions the on-platform prices for sponsored products on the off-platform prices set by all advertisers. This mechanism yields the efficient on-platform allocation but inefficiently high off-platform product prices. It attains the vertical integration profit for the platform and the advertisers, and it increases off-platform product prices while decreasing consumer surplus, relative to data-augmented auctions.
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Price Dispersion and Legacy Discounts in the National Television Advertising Market
Advertising is an input for many final goods, and broadcast television comprises a significant portion of ad spending in the United States. Yet, advertisers face different costs when purchasing national television ads. We seek to empirically confirm differences in firms’ costs to advertise nationally. Network-advertiser contracts are secret, so we combine data on ad placements and average prices of program airings to analyze price dispersion. We document that “legacy” advertisers with established broadcast relationships receive favorable prices for equivalent ad inventories. This may benefit incumbents and potentially soften price competition from newcomers in product markets. History: Avi Goldfarb served as the senior editor for this article. Funding: Financial support from the National Science Foundation [Grant SES-1919040] is gratefully acknowledged. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mksc.2023.1442 .
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- Award ID(s):
- 1919040
- PAR ID:
- 10430895
- Date Published:
- Journal Name:
- Marketing Science
- ISSN:
- 0732-2399
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1287/mksc.2023.1442
