IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this article

Predicting online-purchasing behaviour

Listed author(s):
  • Van den Poel, Dirk
  • Buckinx, Wouter

This empirical study investigates the contribution of different types of predictors to the purchasing behaviour at an online store. We use logit modelling to predict whether or not a purchase is made during the next visit to the website using both forward and backward variable-selection techniques, as well as Furnival and Wilson’s global score search algorithm to find the best subset of predictors. We contribute to the literature by using variables from four different categories in predicting online-purchasing behaviour: (1) general clickstream behaviour at the level of the visit, (2) more detailed clickstream information, (3) customer demographics, and (4) historical purchase behaviour. The results show that predictors from all four categories are retained in the final (best subset) solution indicating that clickstream behaviour is important when determining the tendency to buy. We clearly indicate the contribution in predictive power of variables that were never used before in online purchasing studies. Detailed clickstream variables are the most important ones in classifying customers according to their online purchase behaviour. In doing so, we are able to highlight the advantage of e-commerce retailers of being able to capture an elaborate list of customer information.

(This abstract was borrowed from another version of this item.)

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL:
Download Restriction: Full text for ScienceDirect subscribers only

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Article provided by Elsevier in its journal European Journal of Operational Research.

Volume (Year): 166 (2005)
Issue (Month): 2 (October)
Pages: 557-575

in new window

Handle: RePEc:eee:ejores:v:166:y:2005:i:2:p:557-575
Contact details of provider: Web page:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

in new window

  1. W. Buckinx & D. Van Den Poel, 2003. "Customer Base Analysis: Partial Defection of Behaviorally-Loyal Clients in a Non-Contractual FMCG Retail Setting," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/178, Ghent University, Faculty of Economics and Business Administration.
  2. B. Baesens & G. Verstraeten & D. Van Den Poel & M. Egmont-Petersen & P. Van Kenhove & J. Vanthienen, 2002. "Bayesian Network Classifiers for Identifying the Slope of the Customer - Lifecycle of Long-Life Customers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 02/154, Ghent University, Faculty of Economics and Business Administration.
  3. Wu, Couchen & Chen, Hsiu-Li, 2000. "Counting your customers: Compounding customer's in-store decisions, interpurchase time and repurchasing behavior," European Journal of Operational Research, Elsevier, vol. 127(1), pages 109-119, November.
  4. Füsun Gönül & Meng Ze Shi, 1998. "Optimal Mailing of Catalogs: A New Methodology Using Estimable Structural Dynamic Programming Models," Management Science, INFORMS, vol. 44(9), pages 1249-1262, September.
  5. Van den Poel, Dirk & Leunis, Joseph, 1999. "Consumer Acceptance of the Internet as a Channel of Distribution," Journal of Business Research, Elsevier, vol. 45(3), pages 249-256, July.
  6. D. Van Den Poel & B. Larivière, 2003. "Customer Attrition Analysis For Financial Services Using Proportional Hazard Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/164, Ghent University, Faculty of Economics and Business Administration.
  7. Baesens, Bart & Viaene, Stijn & Van den Poel, Dirk & Vanthienen, Jan & Dedene, Guido, 2002. "Bayesian neural network learning for repeat purchase modelling in direct marketing," European Journal of Operational Research, Elsevier, vol. 138(1), pages 191-211, April.
  8. Gerald Häubl & Valerie Trifts, 2000. "Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids," Marketing Science, INFORMS, vol. 19(1), pages 4-21, May.
  9. David C. Schmittlein & Robert A. Peterson, 1994. "Customer Base Analysis: An Industrial Purchase Process Application," Marketing Science, INFORMS, vol. 13(1), pages 41-67.
Full references (including those not matched with items on IDEAS)

This item is featured on the following reading lists or Wikipedia pages:

  1. Clickstream in Wikipedia Portuguese ne '')

When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:166:y:2005:i:2:p:557-575. 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: (Dana Niculescu)

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 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.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.