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OR PRACTICE---Scheduling of Dynamic In-Game Advertising

Author

Listed:
  • John Turner

    (The Paul Merage School of Business, University of California, Irvine, Irvine, California 92697)

  • Alan Scheller-Wolf

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

  • Sridhar Tayur

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

Abstract

Dynamic in-game advertising is a new form of advertising in which ads are served to video game consoles in real time over the Internet. We present a model for the in-game ad-scheduling problem faced by Massive Inc., a wholly owned subsidiary of Microsoft, and a leading global network provider of in-game ad space. Our model has two components: (1) a linear program (solved periodically) establishes target service rates, and (2) a real-time packing heuristic (run whenever a player enters a new level) tracks these service rates. We benchmark our model against Massive's legacy algorithm: When tested on historical data, we observe (1) an 80%--87% reduction in make-good costs (depending on forecast accuracy), and (2) a shift in the age distribution of served ad space, leaving more premium inventory open for future sales. As a result of our work, Massive has increased the number of unique individuals that see each campaign by, on average, 26% per week and achieved 33% smoother campaign delivery as measured by standard deviation of hourly impressions served.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:oropre:v:59:y:2011:i:1:p:1-16
    DOI: 10.1287/opre.1100.0852
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    References listed on IDEAS

    as
    1. Victor F. Araman & Ioana Popescu, 2010. "Media Revenue Management with Audience Uncertainty: Balancing Upfront and Spot Market Sales," Manufacturing & Service Operations Management, INFORMS, vol. 12(2), pages 190-212, December.
    2. Srinivas Bollapragada & Marc Garbiras, 2004. "Scheduling Commercials on Broadcast Television," Operations Research, INFORMS, vol. 52(3), pages 337-345, June.
    3. Srinivas Bollapragada & Michael R. Bussieck & Suman Mallik, 2004. "Scheduling Commercial Videotapes in Broadcast Television," Operations Research, INFORMS, vol. 52(5), pages 679-689, October.
    4. David Maxwell Chickering & David Heckerman, 2003. "Targeted Advertising on the Web with Inventory Management," Interfaces, INFORMS, vol. 33(5), pages 71-77, October.
    5. Xinhui Zhang, 2006. "Mathematical models for the television advertising allocation problem," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 1(3), pages 302-322.
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    Citations

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    Cited by:

    1. Radha Mookerjee & Subodha Kumar & Vijay S. Mookerjee, 2017. "Optimizing Performance-Based Internet Advertisement Campaigns," Operations Research, INFORMS, vol. 65(1), pages 38-54, February.
    2. Shen, Yuelin, 2018. "Pricing contracts and planning stochastic resources in brand display advertising," Omega, Elsevier, vol. 81(C), pages 183-194.
    3. John Turner, 2012. "The Planning of Guaranteed Targeted Display Advertising," Operations Research, INFORMS, vol. 60(1), pages 18-33, February.
    4. Sameer Mehta & Milind Dawande & Ganesh Janakiraman & Vijay Mookerjee, 2020. "Sustaining a Good Impression: Mechanisms for Selling Partitioned Impressions at Ad Exchanges," Information Systems Research, INFORMS, vol. 31(1), pages 126-147, March.
    5. 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.
    6. Marchand, André & Hennig-Thurau, Thorsten, 2013. "Value Creation in the Video Game Industry: Industry Economics, Consumer Benefits, and Research Opportunities," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 141-157.

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