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To Show or Not Show: Using User Profiling to Manage Internet Advertisement Campaigns at Chitika

Author

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  • Radha Mookerjee

    (Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080)

  • Subodha Kumar

    (Mays Business School, Texas A&M University, College Station, Texas 77843)

  • Vijay S. Mookerjee

    (Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080)

Abstract

We study the problem of an Internet advertising firm that wishes to maximize advertisement (ad) revenue, subject to click-through rate restrictions imposed by the publisher who controls the website on which the ads are displayed. The problem is directly motivated by Chitika, an Internet advertising firm that operates in the Boston area. Chitika contracts with publishers to place relevant ads over a specified period, usually one month, on publisher websites. We develop a predictive model of a visitor clicking on a given ad. Using this prediction of the probability of a click, we develop a decision model that uses a varying threshold to decide whether or not to show an ad to the visitor. We vary the threshold depending on (1) the cumulative number of times an ad has been shown and (2) the cumulative number of clicks on the ad. The decision model's objective is to maximize the advertising firm's revenue subject to a click-through rate constraint. The implemented models work in real time in Chitika's advertising network. We also discuss the implementation challenges and business impact.

Suggested Citation

  • Radha Mookerjee & Subodha Kumar & Vijay S. Mookerjee, 2012. "To Show or Not Show: Using User Profiling to Manage Internet Advertisement Campaigns at Chitika," Interfaces, INFORMS, vol. 42(5), pages 449-464, October.
    • Handle: RePEc:inm:orinte:v:42:y:2012:i:5:p:449-464
    DOI: 10.1287/inte.1120.0644
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    References listed on IDEAS

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    1. Kumar, Subodha & Jacob, Varghese S. & Sriskandarajah, Chelliah, 2006. "Scheduling advertisements on a web page to maximize revenue," European Journal of Operational Research, Elsevier, vol. 173(3), pages 1067-1089, September.
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    Cited by:

    1. Adrian Alexandrescu & Cristian Nicolae Butincu & Mitică Craus, 2017. "Recommending Products and Services Belonging to Online Businesses Using Intelligent Agents," Service Science, INFORMS, vol. 9(4), pages 338-348, December.
    2. Radha Mookerjee & Subodha Kumar & Vijay S. Mookerjee, 2017. "Optimizing Performance-Based Internet Advertisement Campaigns," Operations Research, INFORMS, vol. 65(1), pages 38-54, February.
    3. Radha Mookerjee & Subodha Kumar & Vijay S. Mookerjee, 2017. "Optimizing Performance-Based Internet Advertisement Campaigns," Operations Research, INFORMS, vol. 65(1), pages 38-54, February.
    4. 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.
    5. Zhen Sun & Milind Dawande & Ganesh Janakiraman & Vijay Mookerjee, 2017. "Not Just a Fad: Optimal Sequencing in Mobile In-App Advertising," Information Systems Research, INFORMS, vol. 28(3), pages 511-528, September.
    6. 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.
    7. Shinjini Pandey & Goutam Dutta & Harit Joshi, 2017. "Survey on Revenue Management in Media and Broadcasting," Interfaces, INFORMS, vol. 47(3), pages 195-213, June.

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