Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
Budget optimization in search-based advertising auctions
Proceedings of the 8th ACM conference on Electronic commerce
Data-driven text features for sponsored search click prediction
Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising
Contract Auctions for Sponsored Search
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Improving ad relevance in sponsored search
Proceedings of the third ACM international conference on Web search and data mining
Learning to target: what works for behavioral targeting
Proceedings of the 20th ACM international conference on Information and knowledge management
Finding the right consumer: optimizing for conversion in display advertising campaigns
Proceedings of the fifth ACM international conference on Web search and data mining
Web-scale user modeling for targeting
Proceedings of the 21st international conference companion on World Wide Web
Mean field equilibria of dynamic auctions with learning
ACM SIGecom Exchanges
Towards a robust modeling of temporal interest change patterns for behavioral targeting
Proceedings of the 22nd international conference on World Wide Web
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Advertisers use Sponsored Search to drive traffic to their site at a conversion rate and cost per conversion that provides value to them. However, very often advertisers bid at a constant price on a bundle of keywords, either for lack of enough data to fully optimize their bids at a keyword level, or indirectly by opting into Advanced Matching (AM) that allows an advertiser to reach a large number of queries while explicitly bidding only on a limited number. Then this single bid price reflects the return the advertiser gets from the full bundle. Under these conditions, the advertiser is competing too aggressively for some keyword auctions and with too low bids for others. In this paper, we propose a solution to improve the fairness of each keyword's bid prices within an AM bundle: adjusting the AM keyword bid by the ratio of its conversion rate to the conversion rate it would have reached had it been an Exact Match (EM). First we describe how we measure advertisers' conversion rates despite the opt-in nature of conversion tracking, and illustrate the need for bid adjustment in the context of AM. Then we present our approach to predict conversion rates in a robust manner. Our model uses a number of features capturing the quality of the match between the ad and the query. Then we describe how we adjust keyword bid prices to reflect their value to the advertiser thereby improving (1) the auction through fewer incorrectly high bids in the auction, (2) advertiser return through more auctions won by high value keywords and less by low value keywords, and (3) user satisfaction through higher conversion rate. Finally, we present experimental results from our live system.