AI

Optimizing Budget Constrained Spend in Search Advertising

Abstract

Search engine ad auctions typically have a significant fraction of advertisers who are budget constrained, i.e., if allowed to participate in every auction that they bid on, they would spend more than their budget. This yields an important problem: selecting the ad auctions in which these advertisers participate, in order to optimize different system objectives such as the return on investment for advertisers, and the quality of ads shown to users. We present a system and algorithms for optimizing such budget constrained spend. The system is designed be deployed in a large search engine, with hundreds of thousands of advertisers, millions of searches per hour, and with the query stream being only partially predictable. We have validated the system design by implementing it in the Google ads serving system and running experiments on live traffic. We have also compared our algorithm to previous work that casts this problem as a large linear programming problem limited to popular queries, and show that our algorithms yield substantially better results.