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.
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