Keyword Management Costs and "Broad Match" in Sponsored Search Advertising

In sponsored search advertising, advertisers choose the keywords to bid on, decide how much to bid on them, customize the ads for the chosen keywords by geographic region, time of day and season, and then measure and manage campaign effectiveness. We call the costs that arise from such activities keyword management costs. To reduce these costs and increase advertisers’ participation in keyword auctions, search engines offer a tool called broad match which automates bidding on keywords. These automated bids are based on the search engine’s estimates of the advertisers’ valuations and, therefore, may be less accurate than the bids the advertisers would have turned in themselves. Using a game-theoretic model, we examine the strategic role of keyword management costs, and of broad match, in sponsored search advertising. We show that because these costs inhibit participation by advertisers in keyword auctions, the search engine has to reduce the reserve price, which reduces the search engine’s profits. This motivates the search engine to offer broad match as a tool to reduce keyword management costs. If the accuracy of broad match bids is high enough, advertisers adopt broad match and benefit from the cost reduction, whereas if the accuracy is very low, advertisers do not use it. Interestingly, at moderate levels of bid accuracy, advertisers individually find it attractive to reduce costs by using broad match, but competing advertisers also adopt broad match and the increased competition hurts all advertisers’ profits, and thus a “prisoner’s dilemma” arises. Adoption of broad match by advertisers increases search engine profits, and it therefore seems natural to expect that the search engine will be motivated to improve broad match accuracy. Our analysis shows that the search engine will increase broad match accuracy upto the point where advertisers choose broad match, but increasing the accuracy any further reduces the search engine’s profits. We consider a number of extensions to the basic model to show the robustness of our insights.