IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v19y2000i1p43-62.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.19.1.43.15180
    Download Restriction: no

    References listed on IDEAS

    as
    1. Terry Elrod, 1988. "Choice Map: Inferring a Product-Market Map from Panel Data," Marketing Science, INFORMS, vol. 7(1), pages 21-40.
    2. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
    3. Roland T. Rust & David C. Schmittlein, 1985. "A Bayesian Cross-Validated Likelihood Method for Comparing Alternative Specifications of Quantitative Models," Marketing Science, INFORMS, vol. 4(1), pages 20-40.
    4. J. Yannis Bakos, 1997. "Reducing Buyer Search Costs: Implications for Electronic Marketplaces," Management Science, INFORMS, vol. 43(12), pages 1676-1692, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Michael Braun & André Bonfrer, 2011. "Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes," Marketing Science, INFORMS, vol. 30(3), pages 513-531, 05-06.
    2. Song Yao & Carl F. Mela, 2011. "A Dynamic Model of Sponsored Search Advertising," Marketing Science, INFORMS, vol. 30(3), pages 447-468, 05-06.
    3. repec:aes:jetimm:v:1:y:2017:i:1:p:124-134 is not listed on IDEAS
    4. Joonwook Park & Priyali Rajagopal & Wayne DeSarbo, 2012. "A New Heterogeneous Multidimensional Unfolding Procedure," Psychometrika, Springer;The Psychometric Society, vol. 77(2), pages 263-287, April.
    5. 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.
    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. repec:eee:ijrema:v:31:y:2014:i:3:p:266-279 is not listed on IDEAS
    8. Anindya Ghose & Sha Yang, 2009. "An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets," Management Science, INFORMS, vol. 55(10), pages 1605-1622, October.
    9. Steven M. Shugan, 2004. "The Impact of Advancing Technology on Marketing and Academic Research," Marketing Science, INFORMS, vol. 23(4), pages 469-475.
    10. Kenneth C. Wilbur & Yi Zhu, 2009. "Click Fraud," Marketing Science, INFORMS, vol. 28(2), pages 293-308, 03-04.
    11. 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.
    12. Sha Yang & Anindya Ghose, 2010. "Analyzing the Relationship Between Organic and Sponsored Search Advertising: Positive, Negative, or Zero Interdependence?," Marketing Science, INFORMS, vol. 29(4), pages 602-623, 07-08.
    13. Song Yao & Carl F. Mela, 2008. "A Dynamic Model of Sponsored Search Advertising," Working Papers 08-16, NET Institute, revised Sep 2008.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:19:y:2000:i:1:p:43-62. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mirko Janc). General contact details of provider: http://edirc.repec.org/data/inforea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.