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Modeling Page Views Across Multiple Websites with an Application to Internet Reach and Frequency Prediction

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  • Peter J. Danaher

    (Melbourne Business School, The University of Melbourne, Carlton, Victoria 3053, Australia)

Abstract

In this study, we develop a multivariate generalization of the negative binomial distribution (NBD). This new model has potential application to situations where separate NBDs are correlated, such as for page views across multiple websites. In turn, our page view model is used to predict the audience for Internet advertising campaigns. For very large Internet advertising schedules, a simple approximation to the multivariate model is also derived. In a test of nearly 3,000 Internet advertising schedules, the two new models are compared with some proprietary and nonproprietary models previously used for Internet advertising and are shown to be significantly more accurate.

Suggested Citation

  • Peter J. Danaher, 2007. "Modeling Page Views Across Multiple Websites with an Application to Internet Reach and Frequency Prediction," Marketing Science, INFORMS, vol. 26(3), pages 422-437, 05-06.
    • Handle: RePEc:inm:ormksc:v:26:y:2007:i:3:p:422-437
    DOI: 10.1287/mksc.1060.0226
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    Cited by:

    1. Bucklin, Randolph E. & Sismeiro, Catarina, 2009. "Click Here for Internet Insight: Advances in Clickstream Data Analysis in Marketing," Journal of Interactive Marketing, Elsevier, vol. 23(1), pages 35-48.
    2. David A. Schweidel & Young-Hoon Park & Zainab Jamal, 2014. "A Multiactivity Latent Attrition Model for Customer Base Analysis," Marketing Science, INFORMS, vol. 33(2), pages 273-286, March.
    3. Peter J. Danaher & Janghyuk Lee & Laoucine Kerbache, 2010. "Optimal Internet Media Selection," Marketing Science, INFORMS, vol. 29(2), pages 336-347, 03-04.
    4. Hana Choi & Carl F. Mela & Santiago R. Balseiro & Adam Leary, 2020. "Online Display Advertising Markets: A Literature Review and Future Directions," Information Systems Research, INFORMS, vol. 31(2), pages 556-575, June.
    5. Brenčič, Vera, 2014. "Search online: Evidence from acquisition of information on online job boards and resume banks," Journal of Economic Psychology, Elsevier, vol. 42(C), pages 112-125.
    6. Michael Trusov & Liye Ma & Zainab Jamal, 2016. "Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting," Marketing Science, INFORMS, vol. 35(3), pages 405-426, May.
    7. Vinit Kumar Mishra & Karthik Natarajan & Dhanesh Padmanabhan & Chung-Piaw Teo & Xiaobo Li, 2014. "On Theoretical and Empirical Aspects of Marginal Distribution Choice Models," Management Science, INFORMS, vol. 60(6), pages 1511-1531, June.
    8. Winer, Russell S., 2009. "New Communications Approaches in Marketing: Issues and Research Directions," Journal of Interactive Marketing, Elsevier, vol. 23(2), pages 108-117.
    9. George, Lisa M. & Hogendorn, Christiaan, 2012. "Aggregators, search and the economics of new media institutions," Information Economics and Policy, Elsevier, vol. 24(1), pages 40-51.
    10. Somayeh Moazeni & Boris Defourny & Monika J. Wilczak, 2020. "Sequential Learning in Designing Marketing Campaigns for Market Entry," Management Science, INFORMS, vol. 66(9), pages 4226-4245, September.
    11. Maarten C. W. Janssen & Marielle C. Non, 2009. "Going Where the Ad Leads You: On High Advertised Prices and Searching Where to Buy," Marketing Science, INFORMS, vol. 28(1), pages 87-98, 01-02.
    12. Peter J. Danaher & Michael S. Smith, 2011. "Modeling Multivariate Distributions Using Copulas: Applications in Marketing," Marketing Science, INFORMS, vol. 30(1), pages 4-21, 01-02.
    13. Sami Najafi-Asadolahi & Kristin Fridgeirsdottir, 2014. "Cost-per-Click Pricing for Display Advertising," Manufacturing & Service Operations Management, INFORMS, vol. 16(4), pages 482-497, October.
    14. Glady, Nicolas & Lemmens, Aurélie & Croux, Christophe, 2015. "Unveiling the relationship between the transaction timing, spending and dropout behavior of customers," International Journal of Research in Marketing, Elsevier, vol. 32(1), pages 78-93.
    15. repec:tiu:tiutis:52e91e47-4a2d-4e7b-bb23-3926b842ae30 is not listed on IDEAS
    16. Goh, Khim-Yong & Chu, Junhong & Wu, Jing, 2015. "Mobile Advertising: An Empirical Study of Temporal and Spatial Differences in Search Behavior and Advertising Response," Journal of Interactive Marketing, Elsevier, vol. 30(C), pages 34-45.
    17. Sahar Karimi, 2021. "Cross-visiting Behaviour of Online Consumers Across Retailers’ and Comparison Sites, a Macro-Study," Information Systems Frontiers, Springer, vol. 23(3), pages 531-542, June.
    18. Schröder, Nadine & Falke, Andreas & Hruschka, Harald & Reutterer, Thomas, 2019. "Analyzing the Browsing Basket: A Latent Interests-Based Segmentation Tool," Journal of Interactive Marketing, Elsevier, vol. 47(C), pages 181-197.
    19. Jura Liaukonyte & Thales Teixeira & Kenneth C. Wilbur, 2015. "Television Advertising and Online Shopping," Marketing Science, INFORMS, vol. 34(3), pages 311-330, May.
    20. Park, Chang Hee, 2017. "Online Purchase Paths and Conversion Dynamics across Multiple Websites," Journal of Retailing, Elsevier, vol. 93(3), pages 253-265.
    21. Giang Trinh & Cam Rungie & Malcolm Wright & Carl Driesener & John Dawes, 2014. "Predicting future purchases with the Poisson log-normal model," Marketing Letters, Springer, vol. 25(2), pages 219-234, June.
    22. Kenneth C. Wilbur & Yi Zhu, 2009. "Click Fraud," Marketing Science, INFORMS, vol. 28(2), pages 293-308, 03-04.
    23. Roland T. Rust & Ming-Hui Huang, 2014. "The Service Revolution and the Transformation of Marketing Science," Marketing Science, INFORMS, vol. 33(2), pages 206-221, March.
    24. David A. Schweidel & Peter S. Fader & Eric T. Bradlow, 2008. "A Bivariate Timing Model of Customer Acquisition and Retention," Marketing Science, INFORMS, vol. 27(5), pages 829-843, 09-10.

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