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Targeted Advertising on the Web with Inventory Management

Author

Listed:
  • David Maxwell Chickering

    (Microsoft Research, One Microsoft Way, Redmond, Washington 98052-6399)

  • David Heckerman

    (Microsoft Research, One Microsoft Way, Redmond, Washington 98052-6399)

Abstract

Companies that maintain Web sites can make considerable revenue by running advertisements, and they therefore compete to attract advertisers. The ability to deliver high click-through rates on a site can attract advertisers and, under an appropriate pricing model, can also increase revenue directly. Consequently, companies can benefit from delivery systems that display advertisements selectively to those visitors most likely to click though. To satisfy contractual obligations, however, these systems must simultaneously manage inventory. We developed a delivery system that maximizes click-through rate given inventory-management constraints in the form of advertisement quotas. The system uses predictive segments in conjunction with a linear program to perform the constrained optimization. Using a real Web site (msn.com), we demonstrated the efficacy of the system. We can generalize our system to find revenue-optimal advertisement schedules under a wide variety of pricing models.

Suggested Citation

  • David Maxwell Chickering & David Heckerman, 2003. "Targeted Advertising on the Web with Inventory Management," Interfaces, INFORMS, vol. 33(5), pages 71-77, October.
    • Handle: RePEc:inm:orinte:v:33:y:2003:i:5:p:71-77
    DOI: 10.1287/inte.33.5.71.19248
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    References listed on IDEAS

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    1. Brian Kahin & Hal R. Varian (ed.), 2000. "Internet Publishing and Beyond: The Economics of Digital Information and Intellectual Property," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262611597, December.
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    Cited by:

    1. 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.
    2. John Turner & Alan Scheller-Wolf & Sridhar Tayur, 2011. "OR PRACTICE---Scheduling of Dynamic In-Game Advertising," Operations Research, INFORMS, vol. 59(1), pages 1-16, February.
    3. Asllani, Arben & Lari, Alireza, 2007. "Using genetic algorithm for dynamic and multiple criteria web-site optimizations," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1767-1777, February.
    4. Shinjini Pandey & Goutam Dutta & Harit Joshi, 2017. "Survey on Revenue Management in Media and Broadcasting," Interfaces, INFORMS, vol. 47(3), pages 195-213, June.
    5. Manmohan Aseri & Milind Dawande & Ganesh Janakiraman & Vijay Mookerjee, 2018. "Procurement Policies for Mobile-Promotion Platforms," Management Science, INFORMS, vol. 64(10), pages 4590-4607, October.
    6. Shen, Yuelin, 2018. "Pricing contracts and planning stochastic resources in brand display advertising," Omega, Elsevier, vol. 81(C), pages 183-194.
    7. Esther Gal-Or & Mordechai Gal-Or, 2005. "Customized Advertising via a Common Media Distributor," Marketing Science, INFORMS, vol. 24(2), pages 241-253, July.
    8. Lili Hai & Lan Zhao & Anna Nagurney, 2010. "An integrated framework for the design of optimal web banners," Netnomics, Springer, vol. 11(1), pages 69-83, April.
    9. Ali Hojjat & John Turner & Suleyman Cetintas & Jian Yang, 2017. "A Unified Framework for the Scheduling of Guaranteed Targeted Display Advertising Under Reach and Frequency Requirements," Operations Research, INFORMS, vol. 65(2), pages 289-313, April.
    10. 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.

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