IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v23y2004i4p579-595.html
   My bibliography  Save this article

Modeling Online Browsing and Path Analysis Using Clickstream Data

Author

Listed:
  • Alan L. Montgomery

    (Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213)

  • Shibo Li

    (Rutgers University, 228 Janice Levin Building, 94 Rockafeller Road, Piscataway, New Jersey 08854)

  • Kannan Srinivasan

    (Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213)

  • John C. Liechty

    (Pennsylvania State University, 710 M Business Administration Building, University Park, Pennsylvania 16802)

Abstract

Clickstream data provide information about the sequence of pages or the path viewed by users as they navigate a website. We show how path information can be categorized and modeled using a dynamic multinomial probit model of Web browsing. We estimate this model using data from a major online bookseller. Our results show that the memory component of the model is crucial in accurately predicting a path. In comparison, traditional multinomial probit and first-order Markov models predict paths poorly. These results suggest that paths may reflect a user's goals, which could be helpful in predicting future movements at a website. One potential application of our model is to predict purchase conversion. We find that after only six viewings purchasers can be predicted with more than 40% accuracy, which is much better than the benchmark 7% purchase conversion prediction rate made without path information. This technique could be used to personalize Web designs and product offerings based upon a user's path.

Suggested Citation

  • Alan L. Montgomery & Shibo Li & Kannan Srinivasan & John C. Liechty, 2004. "Modeling Online Browsing and Path Analysis Using Clickstream Data," Marketing Science, INFORMS, vol. 23(4), pages 579-595, November.
    • Handle: RePEc:inm:ormksc:v:23:y:2004:i:4:p:579-595
    DOI: 10.1287/mksc.1040.0073
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.1040.0073
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.1040.0073?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Wendy W. Moe & Peter S. Fader, 2004. "Dynamic Conversion Behavior at E-Commerce Sites," Management Science, INFORMS, vol. 50(3), pages 326-335, March.
    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. Bettman, James R & Luce, Mary Frances & Payne, John W, 1998. "Constructive Consumer Choice Processes," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 25(3), pages 187-217, December.
    4. Richard Paap & Philip Hans Franses, 2000. "A dynamic multinomial probit model for brand choice with different long-run and short-run effects of marketing-mix variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 717-744.
    5. Crosby, Lawrence A & Taylor, James R, 1981. "Effects of Consumer Information and Education on Cognition and Choice," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 8(1), pages 43-56, June.
    6. P. B. Seetharaman, 2004. "Modeling Multiple Sources of State Dependence in Random Utility Models: A Distributed Lag Approach," Marketing Science, INFORMS, vol. 23(2), pages 263-271, April.
    7. Young-Hoon Park & Peter S. Fader, 2004. "Modeling Browsing Behavior at Multiple Websites," Marketing Science, INFORMS, vol. 23(3), pages 280-303, May.
    8. Alan L. Montgomery, 2001. "Applying Quantitative Marketing Techniques to the Internet," Interfaces, INFORMS, vol. 31(2), pages 90-108, April.
    9. McCulloch, Robert E. & Polson, Nicholas G. & Rossi, Peter E., 2000. "A Bayesian analysis of the multinomial probit model with fully identified parameters," Journal of Econometrics, Elsevier, vol. 99(1), pages 173-193, November.
    10. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    11. Jie Zhang & Lakshman Krishnamurthi, 2004. "Customizing Promotions in Online Stores," Marketing Science, INFORMS, vol. 23(4), pages 561-578, June.
    12. Janiszewski, Chris, 1998. "The Influence of Display Characteristics on Visual Exploratory Search Behavior," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 25(3), pages 290-301, December.
    13. Svenson, Ola, 1996. "Decision Making and the Search for Fundamental Psychological Regularities: What Can Be Learned from a Process Perspective?," Organizational Behavior and Human Decision Processes, Elsevier, vol. 65(3), pages 252-267, March.
    14. Eric J. Johnson & John W. Payne, 1985. "Effort and Accuracy in Choice," Management Science, INFORMS, vol. 31(4), pages 395-414, April.
    15. Mariano,Roberto & Schuermann,Til & Weeks,Melvyn J. (ed.), 2000. "Simulation-based Inference in Econometrics," Cambridge Books, Cambridge University Press, number 9780521591126, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Goić, Marcel & Jerath, Kinshuk & Kalyanam, Kirthi, 2022. "The roles of multiple channels in predicting website visits and purchases: Engagers versus closers," International Journal of Research in Marketing, Elsevier, vol. 39(3), pages 656-677.
    2. Amy Wenxuan Ding & Shibo Li & Patrali Chatterjee, 2015. "Learning User Real-Time Intent for Optimal Dynamic Web Page Transformation," Information Systems Research, INFORMS, vol. 26(2), pages 339-359, June.
    3. Alan L. Montgomery & Kartik Hosanagar & Ramayya Krishnan & Karen B. Clay, 2004. "Designing a Better Shopbot," Management Science, INFORMS, vol. 50(2), pages 189-206, February.
    4. Meihua Zuo & Spyros Angelopoulos & Zhouyang Liang & Carol X. J. Ou, 2023. "Blazing the Trail: Considering Browsing Path Dependence in Online Service Response Strategy," Information Systems Frontiers, Springer, vol. 25(4), pages 1605-1619, August.
    5. Park, Chang Hee, 2017. "Online Purchase Paths and Conversion Dynamics across Multiple Websites," Journal of Retailing, Elsevier, vol. 93(3), pages 253-265.
    6. Goic, Marcel & Rojas, Andrea & Saavedra, Ignacio, 2021. "The Effectiveness of Triggered Email Marketing in Addressing Browse Abandonments," Journal of Interactive Marketing, Elsevier, vol. 55(C), pages 118-145.
    7. Kurt A. Carlson & Samuel D. Bond, 2006. "Improving Preference Assessment: Limiting the Effect of Context Through Pre-exposure to Attribute Levels," Management Science, INFORMS, vol. 52(3), pages 410-421, March.
    8. Kartik Hosanagar, 2011. "Usercentric Operational Decision Making in Distributed Information Retrieval," Information Systems Research, INFORMS, vol. 22(4), pages 739-755, December.
    9. Sam K. Hui & Peter S. Fader & Eric T. Bradlow, 2009. "Path Data in Marketing: An Integrative Framework and Prospectus for Model Building," Marketing Science, INFORMS, vol. 28(2), pages 320-335, 03-04.
    10. Nishtha Langer & Chris Forman & Sunder Kekre & Baohong Sun, 2012. "Ushering Buyers into Electronic Channels: An Empirical Analysis," Information Systems Research, INFORMS, vol. 23(4), pages 1212-1231, December.
    11. Montgomery, Alan L. & Smith, Michael D., 2009. "Prospects for Personalization on the Internet," Journal of Interactive Marketing, Elsevier, vol. 23(2), pages 130-137.
    12. Adam N. Smith & Stephan Seiler & Ishant Aggarwal, 2023. "Optimal Price Targeting," Marketing Science, INFORMS, vol. 42(3), pages 476-499, May.
    13. McHugh, Sandie & Ranyard, Rob & Lewis, Alan, 2011. "Understanding and knowledge of credit cost and duration: Effects on credit judgements and decisions," Journal of Economic Psychology, Elsevier, vol. 32(4), pages 609-620, August.
    14. Aiste Ruseckaite & Dennis Fok & Peter Goos, 2016. "Flexible Mixture-Amount Models for Business and Industry using Gaussian Processes," Tinbergen Institute Discussion Papers 16-075/III, Tinbergen Institute.
    15. Martinovici, A., 2019. "Revealing attention - how eye movements predict brand choice and moment of choice," Other publications TiSEM 7dca38a5-9f78-4aee-bd81-c, Tilburg University, School of Economics and Management.
    16. Dost, Florian & Wilken, Robert & Eisenbeiss, Maik & Skiera, Bernd, 2014. "On the Edge of Buying: A Targeting Approach for Indecisive Buyers Based on Willingness-to-Pay Ranges," Journal of Retailing, Elsevier, vol. 90(3), pages 393-407.
    17. Dan Horsky & Sanjog Misra & Paul Nelson, 2006. "Observed and Unobserved Preference Heterogeneity in Brand-Choice Models," Marketing Science, INFORMS, vol. 25(4), pages 322-335, 07-08.
    18. Nalan Basturk & Cem Cakmakli & S. Pinar Ceyhan & Herman K. van Dijk, 2014. "On the Rise of Bayesian Econometrics after Cowles Foundation Monographs 10, 14," Tinbergen Institute Discussion Papers 14-085/III, Tinbergen Institute, revised 04 Sep 2014.
    19. Thomas Reutterer & Kurt Hornik & Nicolas March & Kathrin Gruber, 2017. "A data mining framework for targeted category promotions," Journal of Business Economics, Springer, vol. 87(3), pages 337-358, April.
    20. Andrew Ching & Susumu Imai & Masakazu Ishihara & Neelam Jain, 2012. "A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models," Quantitative Marketing and Economics (QME), Springer, vol. 10(2), pages 151-196, June.

    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:23:y:2004:i:4:p:579-595. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.