Estimating Ad Clickthrough Rate through Query Intent Analysis

Abstract

Clickthrough rate, bid, and cost-per-click are known to be among the factors that impact the rank of an ad shown on a search result page. Search engines can benefit from estimating ad clickthrough in order to determine the quality of ads and maximize their revenue. In this paper, a methodology is developed to estimate ad clickthrough rate by exploring user queries and clickthrough logs. As we demonstrate, the average ad clickthrough rate depends to a substantial extent on the rank position of ads and on the total number of ads displayed on the page. This observation is utilized by a baseline model to calculate the expected clickthrough rate for various ads. We further study the impact of query intent on the clickthrough rate, where query intent is predicted using a combination of query features and the content of search engine result pages. The baseline model and the query intent model are compared for the purpose of calculating the expected ad clickthrough rate. Our findings suggest that such factors as the rank of an ad, the number of ads displayed on the result page, and query intent are effective in estimating ad clickthrough rate.