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Scheduling Commercials on Broadcast Television

Author

Listed:
  • Srinivas Bollapragada

    (General Electric Global Research Center, Information and Decision Technology Labs, 1 Research Circle, Schenectady, New York 12309)

  • Marc Garbiras

    (General Electric Global Research Center, Information and Decision Technology Labs, 1 Research Circle, Schenectady, New York 12309)

Abstract

Television networks sell advertising slots to clients by the shows on which the commercials air. The networks determine the exact location in the show that a commercial will air at a later stage, usually close to the airdate of the show. There are several criteria the networks must meet in scheduling commercials in a show. The schedule should be such that no two commercials promoting competing products from different clients air in the same break. The audience ratings tend to be higher at the start and end of a commercial break than during the middle of the break. Therefore, advertisers generally prefer the first and last positions in a commercial segment, to those in the middle. TV networks normally promise their clients an equitable rotation of commercials among the positions within a commercial break. The scheduling of commercials on shows is traditionally done manually and is a cumbersome, time-intensive, and error-prone process. We formulate the commercial scheduling problem as an integer program and develop near-optimal heuristics for automatically scheduling the commercials to meet all the requirements. We implemented our algorithm at the National Broadcasting Company (NBC). In addition to reducing sales personnel costs by automating the scheduling of commercials, our work has increased customer satisfaction by minimizing errors in meeting customer requirements.

Suggested Citation

  • Srinivas Bollapragada & Marc Garbiras, 2004. "Scheduling Commercials on Broadcast Television," Operations Research, INFORMS, vol. 52(3), pages 337-345, June.
  • Handle: RePEc:inm:oropre:v:52:y:2004:i:3:p:337-345
    DOI: 10.1287/opre.1030.0083
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    References listed on IDEAS

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    1. Jeffrey H. Horen, 1980. "Scheduling of Network Television Programs," Management Science, INFORMS, vol. 26(4), pages 354-370, April.
    2. Vijay Mahajan & Eitan Muller, 1986. "Advertising Pulsing Policies for Generating Awareness for New Products," Marketing Science, INFORMS, vol. 5(2), pages 89-106.
    3. Srinivas K. Reddy & Jay E. Aronson & Antonie Stam, 1998. "SPOT: Scheduling Programs Optimally for Television," Management Science, INFORMS, vol. 44(1), pages 83-102, January.
    4. Vijay Mahajan & Eitan Muller, 1986. "Reply—Reflections on Advertising Pulsing Policies for Generating Awareness for New Products," Marketing Science, INFORMS, vol. 5(2), pages 110-111.
    5. Srinivas Bollapragada & Michael R. Bussieck & Suman Mallik, 2004. "Scheduling Commercial Videotapes in Broadcast Television," Operations Research, INFORMS, vol. 52(5), pages 679-689, October.
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    Citations

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    Cited by:

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    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. Karsu, Özlem & Morton, Alec, 2014. "Incorporating balance concerns in resource allocation decisions: A bi-criteria modelling approach," Omega, Elsevier, vol. 44(C), pages 70-82.
    4. Shi, Yang & Zhao, Ying, 2019. "Modeling Advertisers' Willingness to Pay in TV Commercial Slot Auctions," Journal of Interactive Marketing, Elsevier, vol. 48(C), pages 120-133.
    5. Sylvia Hristakeva & Julie Holland Mortimer, 2023. "Price Dispersion and Legacy Discounts in the National Television Advertising Market," Marketing Science, INFORMS, vol. 42(6), pages 1162-1183, November.
    6. Karsu, Özlem & Morton, Alec, 2015. "Inequity averse optimization in operational research," European Journal of Operational Research, Elsevier, vol. 245(2), pages 343-359.
    7. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    8. Daya Ram Gaur & Ramesh Krishnamurti & Rajeev Kohli, 2009. "Conflict Resolution in the Scheduling of Television Commercials," Operations Research, INFORMS, vol. 57(5), pages 1098-1105, October.
    9. Guohua Wan & Xiangtong Qi, 2010. "Scheduling with variable time slot costs," Naval Research Logistics (NRL), John Wiley & Sons, vol. 57(2), pages 159-171, March.
    10. Giovanni Giallombardo & Houyuan Jiang & Giovanna Miglionico, 2016. "New Formulations for the Conflict Resolution Problem in the Scheduling of Television Commercials," Operations Research, INFORMS, vol. 64(4), pages 838-848, August.
    11. Tripathi, Arvind K. & Nair, Suresh K., 2007. "Narrowcasting of wireless advertising in malls," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1023-1038, November.
    12. José Antonio Carbajal & Peter Williams & Andreea Popescu & Wes Chaar, 2019. "Turner Blazes a Trail for Audience Targeting on Television with Operations Research and Advanced Analytics," Interfaces, INFORMS, vol. 49(1), pages 64-89, January.
    13. Dana G. Popescu & Pascale Crama, 2016. "Ad Revenue Optimization in Live Broadcasting," Management Science, INFORMS, vol. 62(4), pages 1145-1164, April.
    14. Shinjini Pandey & Goutam Dutta & Harit Joshi, 2017. "Survey on Revenue Management in Media and Broadcasting," Interfaces, INFORMS, vol. 47(3), pages 195-213, June.
    15. José Antonio Carbajal & Wes Chaar, 2017. "Turner Optimizes the Allocation of Audience Deficiency Units," Interfaces, INFORMS, vol. 47(6), pages 518-536, December.

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