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Sponsored search is a form of online advertising where advertisers bid for placement next to search engine results for specific keywords. As search engines compete for the growing share of online ad spend, it becomes important for them to understand what keywords advertisers value most, and what characteristics of keywords drive value. In this paper we propose an approach to keyword value prediction that draws on advertiser bidding behavior across the terms and campaigns in an account. We provide original insights into the structure of sponsored search accounts that motivate the use of a hierarchical modeling strategy. We propose an economically meaningful loss function which allows us to implicitly fit a linear model for values given observables such as bids and click-through rates. The model draws on demographic and textual features of keywords and takes advantage of the hierarchical structure of sponsored search accounts. Its predictive quality is evaluated on several high-revenue and high-exposure advertising accounts on a major search engine. Besides the general evaluation of advertiser welfare, our approach has potential applications to keyword and bid suggestion.
Sodomka et al. (Mon,) studied this question.