Data-Driven Reserve Prices for Social Advertising Auctions at LinkedIn
Tingting Cui, Lijun Peng, David Pardoe, Kun Liu, Deepak Agarwal, and Deepak Kumar
Online advertising auctions constitute an important source of rev- enue 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 pub- lisher, 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.