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Applying Quantitative Marketing Techniques to the Internet

Author

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  • Alan L. Montgomery

    (Graduate School of Industrial Administration, Carnegie Mellon University, Posner Hall 255A, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213-3890)

Abstract
Quantitative models have proved valuable in predicting consumer behavior in the offline world. These same techniques can be adapted to predict online actions. The use of diffusion models provides a firm foundation to implement and forecast viral marketing strategies. Choice models can predict purchases at online stores and shopbots. Hierarchical Bayesian models provide a framework for implementing versioning and price-segmentation strategies. Bayesian updating is a natural tool for profiling users with clickstream data. A key challenge for practitioners of Internet marketing is to extract value from the huge volume of data that can be collected. These techniques illustrate how this information can be leveraged to create better decisions.

Suggested Citation

  • Alan L. Montgomery, 2001. "Applying Quantitative Marketing Techniques to the Internet," Interfaces, INFORMS, vol. 31(2), pages 90-108, April.
    • Handle: RePEc:inm:orinte:v:31:y:2001:i:2:p:90-108
    DOI: 10.1287/inte.31.2.90.10630
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    References listed on IDEAS

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    1. Jerry Wind & Paul E. Green & Douglas Shifflet & Marsha Scarbrough, 1989. "Courtyard by Marriott : Designing a Hotel Facility with Consumer-Based Marketing Models," Interfaces, INFORMS, vol. 19(1), pages 25-47, February.
    2. McFadden, Daniel, 1980. "Econometric Models for Probabilistic Choice among Products," The Journal of Business, University of Chicago Press, vol. 53(3), pages 13-29, July.
    3. Vijay Mahajan & Eitan Muller & Frank M. Bass, 1995. "Diffusion of New Products: Empirical Generalizations and Managerial Uses," Marketing Science, INFORMS, vol. 14(3_supplem), pages 79-88.
    4. Yannis Bakos & Erik Brynjolfsson, 1999. "Bundling Information Goods: Pricing, Profits, and Efficiency," Management Science, INFORMS, vol. 45(12), pages 1613-1630, December.
    5. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
    6. Gabriel R. Bitran & Susana V. Mondschein, 1996. "Mailing Decisions in the Catalog Sales Industry," Management Science, INFORMS, vol. 42(9), pages 1364-1381, September.
    7. 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.
    8. Jan Roelf Bult & Tom Wansbeek, 1995. "Optimal Selection for Direct Mail," Marketing Science, INFORMS, vol. 14(4), pages 378-394.
    9. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    10. Dan Horsky, 1990. "A Diffusion Model Incorporating Product Benefits, Price, Income and Information," Marketing Science, INFORMS, vol. 9(4), pages 342-365.
    11. Christophe Van den Bulte & Gary L. Lilien, 1997. "Bias and Systematic Change in the Parameter Estimates of Macro-Level Diffusion Models," Marketing Science, INFORMS, vol. 16(4), pages 338-353.
    12. 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.
    13. Sunil Gupta & Donald G. Morrison, 1991. "Estimating Heterogeneity in Consumers' Purchase Rates," Marketing Science, INFORMS, vol. 10(3), pages 264-269.
    14. Alan L. Montgomery, 1997. "Creating Micro-Marketing Pricing Strategies Using Supermarket Scanner Data," Marketing Science, INFORMS, vol. 16(4), pages 315-337.
    15. Nagle, Thomas, 1984. "Economic Foundations for Pricing," The Journal of Business, University of Chicago Press, vol. 57(1), pages 3-26, January.
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    Cited by:

    1. P Ma & J Crook & J Ansell, 2010. "Modelling take-up and profitability," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 430-442, March.
    2. Yang, Jianmei & Yao, Canzhong & Ma, Weicheng & Chen, Guanrong, 2010. "A study of the spreading scheme for viral marketing based on a complex network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 859-870.
    3. Arthur M. Geoffrion & Ramayya Krishnan, 2003. "E-Business and Management Science: Mutual Impacts (Part 1 of 2)," Management Science, INFORMS, vol. 49(10), pages 1275-1286, October.
    4. Raphaël Suire & Thierry Pénard & Fabrice Le Guel, 2005. "Adoption et usage marchand de l’Internet : une étude économétrique sur données bretonnes," Économie et Prévision, Programme National Persée, vol. 167(1), pages 67-84.
    5. Pelin Atahan & Sumit Sarkar, 2011. "Accelerated Learning of User Profiles," Management Science, INFORMS, vol. 57(2), pages 215-239, February.
    6. Van den Poel, Dirk & Buckinx, Wouter, 2005. "Predicting online-purchasing behaviour," European Journal of Operational Research, Elsevier, vol. 166(2), pages 557-575, October.
    7. Barry C. Smith & Dirk P. Günther & B. Venkateshwara Rao & Richard M. Ratlife, 2001. "E-Commerce and Operations Research in Airline Planning, Marketing, and Distribution," Interfaces, INFORMS, vol. 31(2), pages 37-55, April.
    8. Montgomery, Alan L. & Smith, Michael D., 2009. "Prospects for Personalization on the Internet," Journal of Interactive Marketing, Elsevier, vol. 23(2), pages 130-137.
    9. H-V Seow, 2010. "Question selection responding to information on customers from heterogeneous populations to select offers that maximize expected profit," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 443-454, March.
    10. 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.
    11. 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.
    12. Monica Johar & Vijay Mookerjee & Sumit Sarkar, 2014. "Selling vs. Profiling: Optimizing the Offer Set in Web-Based Personalization," Information Systems Research, INFORMS, vol. 25(2), pages 285-306, June.
    13. Doukidis, Georgios I. & Pramatari, Katerina & Lekakos, Georgios, 2008. "OR and the management of electronic services," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1296-1309, June.
    14. Krishnamurthy, Sandeep, 2006. "Introducing E-MARKPLAN: A practical methodology to plan e-marketing activities," Business Horizons, Elsevier, vol. 49(1), pages 51-60.
    15. Weinberg, Bruce D. & Davis, Lenita, 2005. "Exploring the WOW in online-auction feedback," Journal of Business Research, Elsevier, vol. 58(11), pages 1609-1621, November.
    16. Seow, Hsin-Vonn & Thomas, Lyn C., 2006. "Using adaptive learning in credit scoring to estimate take-up probability distribution," European Journal of Operational Research, Elsevier, vol. 173(3), pages 880-892, September.

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