Data-Driven Reserve Prices for Social Advertising Auctions at LinkedIn
| Autor: | Kun Liu, Deepak Kumar, Deepak Agarwal, Tingting Cui, David Pardoe, Lijun Peng |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
TheoryofComputation_MISCELLANEOUS
Generalized second-price auction Auction theory business.industry 05 social sciences TheoryofComputation_GENERAL Advertising 02 engineering and technology Online advertising Data-driven Reservation price 0502 economics and business 0202 electrical engineering electronic engineering information engineering Common value auction Revenue 050211 marketing 020201 artificial intelligence & image processing business Game theory |
| Zdroj: | ADKDD@KDD |
| Popis: | Online advertising auctions constitute an important source of revenue for search engines such as Google and Bing, as well as social networks such as Facebook, LinkedIn and Twitter. We study the problem of setting the optimal reserve price in a Generalized Second Price auction, guided by auction theory with suitable adaptations to social advertising at LinkedIn. Two types of reserve prices are deployed: one at the user level, which is kept private by the publisher, and the other at the audience segment level, which is made public to advertisers. We demonstrate through field experiments the effectiveness of this reserve price mechanism to promote demand growth, increase ads revenue, and improve advertiser experience. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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