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Scheduling of Network Television Programs

Author

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  • Jeffrey H. Horen

    (University of Iowa)

Abstract

Maximizing average audience size is a major objective of national television networks. This paper examines the effect of scheduling programs on audience ratings. We determine the predictability of ratings and the major factors which influence ratings. We measure the extent to which ratings can be changed by rescheduling programs, and then test commonly discussed scheduling strategies and propose new strategies. Finally, we ascertain the extent to which the audience's benefit is served by a system of three major networks, each competing to maximize individual ratings. A forecasting model based on five years of historical data estimates ratings for hypothetical schedules. A heuristic fitting procedure yields a model which has consistent estimated parameters and explains about 70% of the variance. The previous year's ratings of a program and competing programs in its time slot are the best predictors of its rating. Also important are the day, time, and lead-in audience. The resulting estimates are incorporated in a combinatorial model, representing the decision of a program scheduler seeking to maximize ratings. A typical sized problem of scheduling 23 programs of various lengths into a week of 40 half-hour time slots can be formulated as a 65 \times 800 assignment-type integer program. The optimizations show that rescheduling programs can, on the average, substantially increase a network's audience size (11.6%) and advertising revenue ($61 million per year). The optimal program scheduling for one network generally decreases the ratings of competing networks. The forecasting and optimization models are then used to evaluate commonly discussed scheduling strategies: Protecting Newcomers, Starting Fast, Homogeneity, Counterprogramming, and Bridging. Of these, Counterprogramming is the only strategy that is presently and optimally used and justified by the model. An additional strategy, termed "Avoidance," is discovered and theoretically justified by subadditivity in ratings. This strategy increases total audience size and benefits the viewing audience.

Suggested Citation

  • Jeffrey H. Horen, 1980. "Scheduling of Network Television Programs," Management Science, INFORMS, vol. 26(4), pages 354-370, April.
  • Handle: RePEc:inm:ormnsc:v:26:y:1980:i:4:p:354-370
    DOI: 10.1287/mnsc.26.4.354
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    Citations

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    Cited by:

    1. Sha Yang & Vishal Narayan & Henry Assael, 2006. "Estimating the Interdependence of Television Program Viewership Between Spouses: A Bayesian Simultaneous Equation Model," Marketing Science, INFORMS, vol. 25(4), pages 336-349, July.
    2. 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.
    3. Givon, Moshe & Grosfeld-Nir, Abraham, 2008. "Using partially observed Markov processes to select optimal termination time of TV shows," Omega, Elsevier, vol. 36(3), pages 477-485, June.
    4. Syed Mohd Muneeb & Ahmad Yusuf Adhami & Zainab Asim & Syed Aqib Jalil, 2019. "Bi-level decision making models for advertising allocation problem under fuzzy environment," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(2), pages 160-172, April.
    5. Eliashberg, Jehoshua & Hegie, Quintus & Ho, Jason & Huisman, Dennis & Miller, Steven J. & Swami, Sanjeev & Weinberg, Charles B. & Wierenga, Berend, 2009. "Demand-driven scheduling of movies in a multiplex," International Journal of Research in Marketing, Elsevier, vol. 26(2), pages 75-88.
    6. M J Brusco, 2008. "Scheduling advertising slots for television," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(10), pages 1363-1372, October.
    7. Olga M. Khessina & Samira Reis, 2016. "The Limits of Reflected Glory: The Beneficial and Harmful Effects of Product Name Similarity in the U.S. Network TV Program Industry, 1944–2003," Organization Science, INFORMS, vol. 27(2), pages 411-427, April.
    8. Danaher, Peter & Dagger, Tracey, 2012. "Using a nested logit model to forecast television ratings," International Journal of Forecasting, Elsevier, vol. 28(3), pages 607-622.
    9. Srinivas Bollapragada & Marc Garbiras, 2004. "Scheduling Commercials on Broadcast Television," Operations Research, INFORMS, vol. 52(3), pages 337-345, June.
    10. Kelton, Christina M. L. & Schneider Stone, Linda G., 1998. "Optimal television schedules in alternative competitive environments," European Journal of Operational Research, Elsevier, vol. 104(3), pages 451-473, February.
    11. I. Robert Chiang & Jhih‐Hua Jhang‐Li, 2020. "Competition through Exclusivity in Digital Content Distribution," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1270-1286, May.
    12. 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.
    13. Jessica Clark & Jean-François Paiement & Foster Provost, 2023. "Who’s Watching TV?," Information Systems Research, INFORMS, vol. 34(4), pages 1622-1640, December.
    14. Pérez-Gladish, B. & Gonzalez, I. & Bilbao-Terol, A. & Arenas-Parra, M., 2010. "Planning a TV advertising campaign: A crisp multiobjective programming model from fuzzy basic data," Omega, Elsevier, vol. 38(1-2), pages 84-94, February.
    15. Danaher, Peter J. & Dagger, Tracey S. & Smith, Michael S., 2011. "Forecasting television ratings," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1215-1240, October.
    16. Song, Lianlian & Shi, Yang & Tso, Geoffrey Kwok Fai & Lo, Hing Po, 2021. "Forecasting week-to-week television ratings using reduced-form and structural dynamic models," International Journal of Forecasting, Elsevier, vol. 37(1), pages 302-321.
    17. 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.
    18. Shinjini Pandey & Goutam Dutta & Harit Joshi, 2017. "Survey on Revenue Management in Media and Broadcasting," Interfaces, INFORMS, vol. 47(3), pages 195-213, June.
    19. 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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