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What happens after an ad click?: quantifying the impact of landing pages in web advertising

Published:02 November 2009Publication History

ABSTRACT

Unbeknownst to most users, when a query is submitted to a search engine two distinct searches are performed: the organic or algorithmic search that returns relevant Web pages and related data (maps, images, etc.), and the sponsored search that returns paid advertisements. While an enormous amount of work has been invested in understanding the user interaction with organic search, surprisingly little research has been dedicated to what happens after an ad is clicked, a situation we aim to correct.

To this end, we define and study the process of context transfer, that is, the user's transition from Web search to the context of the landing page that follows an ad-click. We conclude that in the vast majority of cases the user is shown one of three types of pages, namely, Homepage (the homepage of the advertiser), Category browse (a browse-able sub-catalog related to the original query), and Search transfer (the search results of the same query re-executed on the target site). We show that these three types of landing pages can be accurately distinguished using automatic text classification. Finally, using such an automatic classifier, we correlate the landing page type with conversion data provided by advertisers, and show that the conversion rate (i.e., users' response rate to ads) varies considerably according to the type. We believe our findings will further the understanding of users' response to search advertising in general, and landing pages in particular, and thus help advertisers improve their Web sites and help search engines select the most suitable ads.

References

  1. H. Becker, A. Broder, E. Gabrilovich, V. Josifovski, and B. Pang. Context transfer in search advertising. In SIGIR'09. Poster. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. A. Broder. A taxonomy of web search. SIGIR Forum, 36, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. A. Broder, P. Ciccolo, M. Fontoura, E. Gabrilovich, V. Josifovski, and L. Riedel. Search advertising using Web relevance feedback. In CIKM'08, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. A. Broder, M. Fontoura, E. Gabrilovich, A. Joshi, V. Josifovski, and T. Zhang. Robust classification of rare queries using web knowledge. In SIGIR'07, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. D. Downey, S. Dumais, D. Liebling, and E. Horvitz. Understanding the relationship between searchers' queries and information goals. In CIKM, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. B. Edelman, M. Ostrovsky, and M. Schwarz. Internet advertising and the generalized second price auction: Selling billions of dollars worth of keywords. American Economic Review, 97(1):242--259, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  7. D. Fain and J. Pedersen. Sponsored search: A brief history. In Second Workshop on Sponsored Search Auctions, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  8. R. Jones and K. Klinkner. Beyond the session timeout: Automatic hierarchical segmentation of search topics in query logs. In CIKM, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. U. Lee, Z. Liu, and J. Cho. Automatic identification of user goals in web search. In WWW, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Y. Li, Z. Zheng, and H. Dai. KDD CUP-2005 report: Facing a great challenge. In SIGKDD Explorations. 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. P. Nelson. Advertising as information. Journal of Political Economy, 82(4):729--54, July/Aug. 1974.Google ScholarGoogle ScholarCross RefCross Ref
  12. F. Radlinski, A. Broder, P. Ciccolo, E. Gabrilovich, V. Josifovski, and L. Riedel. Optimizing relevance and revenue in ad search: A query substitution approach. In SIGIR'08, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. M. Regelson and D. Fain. Predicting click-through rate using keyword clusters. In Second Workshop on Sponsored Search Auctions, 2006.Google ScholarGoogle Scholar
  14. B. Ribeiro-Neto, M. Cristo, P. B. Golgher, and E. S. de Moura. Impedance coupling in content-targeted advertising. In SIGIR'05, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: Estimating the click-through rate for new ads. In WWW'07. ACM Press, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. D. Rose and D. Levinson. Understanding user goals in web search. In WWW, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. C. Wang, P. Zhang, R. Choi, and M. D. Eredita. Understanding consumers attitude toward advertising. In 8th Americas Conference on Information Systems, 2002.Google ScholarGoogle Scholar
  18. I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2 edition, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library

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        cover image ACM Conferences
        CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management
        November 2009
        2162 pages
        ISBN:9781605585123
        DOI:10.1145/1645953

        Copyright © 2009 ACM

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        Publication History

        • Published: 2 November 2009

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