document: Predicting bounce rates in sponsored search advertisements This paper explores an important and relatively unstudied quality measure of a sponsored search advertisement : bounce rate . The bounce rate of an ad can be informally defined as the fraction of users who click on the ad but almost immediately move on to other tasks . A high bounce rate can lead to poor advertiser return on investment , and suggests search engine users may be having a poor experience following the click . In this paper , we first provide quantitative analysis showing that bounce rate is an effective measure of user satisfaction . We then address the question , can we predict bounce rate by analyzing the features of the advertisement ? An affirmative answer would allow advertisers and search engines to predict the effectiveness and quality of advertisements before they are shown . We propose solutions to this problem involving large-scale learning methods that leverage features drawn from ad creatives in addition to their keywords and landing pages .
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