IDEAS home Printed from https://ideas.repec.org/p/rug/rugwps/09-618.html
   My bibliography  Save this paper

Predicting web site audience demographics for web advertising targeting using multi-web site clickstream data

Author

Listed:
  • K. W. DE BOCK
  • D. VAN DEN POEL

    ()

  • S. MANIGART

Abstract

Several recent studies have explored the virtues of behavioral targeting and personalization for online advertising. In this paper, we add to this literature by proposing a cost-effective methodology for the prediction of demographic web site visitor profiles that can be used for web advertising targeting purposes. The methodology involves the transformation of web site visitors’ clickstream patterns to a set of features and the training of Random Forest classifiers that generate predictions for gender, age, educational level and occupation category. These demographic predictions can support online advertisement targeting (i) as an additional input in personalized advertising or behavioral targeting, in order to restrict ad targeting to demographically defined target groups, or (ii) as an input for aggregated demographic web site visitor profiles that support marketing managers in selecting web sites and achieving an optimal correspondence between target groups and web site audience composition. The proposed methodology is validated using data from a Belgian web metrics company. The results demonstrate that Random Forests demonstrate superior classification performance over a set of benchmark algorithms. Further, the ability of the model set to generate representative demographic web site audience profiles is assessed. The stability of the models over time is demonstrated using out-of-period data.

Suggested Citation

  • K. W. De Bock & D. Van Den Poel & S. Manigart, 2009. "Predicting web site audience demographics for web advertising targeting using multi-web site clickstream data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/618, Ghent University, Faculty of Economics and Business Administration.
  • Handle: RePEc:rug:rugwps:09/618
    as

    Download full text from publisher

    File URL: http://wps-feb.ugent.be/Papers/wp_09_618.pdf
    Download Restriction: no

    Other versions of this item:

    Citations

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


    Cited by:

    1. DE CNUDDE, Sofie & MARTENS, David & EVGENIOU, Theodoros & PROVOST, Foster, 2017. "A benchmarking study of classification techniques for behavioral data," Working Papers 2017005, University of Antwerp, Faculty of Applied Economics.
    2. D. Thorleuchter & D. Van Den Poel & A. Prinzie, 2011. "Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/733, Ghent University, Faculty of Economics and Business Administration.

    More about this item

    Keywords

    demography prediction; demographic targeting; web advertising; Random Forests; web user profiling; clickstream analysis;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:rug:rugwps:09/618. 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: (Nathalie Verhaeghe). General contact details of provider: http://edirc.repec.org/data/ferugbe.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.

    We have no references for this item. You can help adding them by using 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.