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The Little Engines That Could: Modeling the Performance of World Wide Web Search Engines

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  • Eric T. Bradlow

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6371)

  • David C. Schmittlein

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6371)

Abstract

This research examines the ability of six popular Web search engines, individually and collectively, to locate Web pages containing common marketing/management phrases. We propose and validate a model for search engine performance that is able to represent key patterns of coverage and overlap among the engines. The model enables us to estimate the typical additional benefit of using multiple search engines, depending on the particular set of engines being considered. It also provides an estimate of the number of relevant Web pages found by any of the engines. For a typical marketing/management phrase we estimate that the “best” search engine locates about 50% of the pages, and all six engines together find about 90% of the total. The model is also used to examine how properties of a Web page and characteristics of a phrase affect the probability that a given search engine will find a given page. For example, we find that the number of Web page links increases the prospect that each of the six search engines will find it. Finally, we summarize the relationship between major structural characteristics of a search engine and its performance in locating relevant Web pages.

Suggested Citation

  • Eric T. Bradlow & David C. Schmittlein, 2000. "The Little Engines That Could: Modeling the Performance of World Wide Web Search Engines," Marketing Science, INFORMS, vol. 19(1), pages 43-62, June.
    • Handle: RePEc:inm:ormksc:v:19:y:2000:i:1:p:43-62
    DOI: 10.1287/mksc.19.1.43.15180
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    4. Anindya Ghose & Sha Yang, 2007. "An Empirical Analysis of Search Engine Advertising: Sponsored Search and Cross-Selling in Electronic Markets," Working Papers 07-35, NET Institute, revised Sep 2007.
    5. Steven M. Shugan, 2004. "The Impact of Advancing Technology on Marketing and Academic Research," Marketing Science, INFORMS, vol. 23(4), pages 469-475.
    6. Roland T. Rust & Tuck Siong Chung, 2006. "Marketing Models of Service and Relationships," Marketing Science, INFORMS, vol. 25(6), pages 560-580, 11-12.
    7. Song Yao & Carl F. Mela, 2008. "A Dynamic Model of Sponsored Search Advertising," Working Papers 08-16, NET Institute, revised Sep 2008.
    8. Song Yao & Carl F. Mela, 2011. "A Dynamic Model of Sponsored Search Advertising," Marketing Science, INFORMS, vol. 30(3), pages 447-468, 05-06.
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    15. Park, Chang Hee & Park, Young-Hoon & Schweidel, David A., 2014. "A multi-category customer base analysis," International Journal of Research in Marketing, Elsevier, vol. 31(3), pages 266-279.
    16. Donna L. Hoffman, 2000. "The Revolution Will Not Be Televised: Introduction to the Special Issue on Marketing Science and the Internet," Marketing Science, INFORMS, vol. 19(1), pages 1-3.
    17. Krafft, Manfred & Kumar, V. & Harmeling, Colleen & Singh, Siddharth & Zhu, Ting & Chen, Jialie & Duncan, Tom & Fortin, Whitney & Rosa, Erin, 2021. "Insight is power: Understanding the terms of the consumer-firm data exchange," Journal of Retailing, Elsevier, vol. 97(1), pages 133-149.
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