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Forecasting television ratings


  • Danaher, Peter J.
  • Dagger, Tracey S.
  • Smith, Michael S.


Despite the state of flux in media today, television remains the dominant player globally for advertising spending. Since television advertising time is purchased on the basis of projected future ratings, and ad costs have skyrocketed, there is increasingly pressure to forecast television ratings accurately. The forecasting methods that have been used in the past are not generally very reliable, and many have not been validated; also, even more distressingly, none have been tested in today's multichannel environment. In this study we compare eight different forecasting models, ranging from a naïve empirical method to a state-of-the-art Bayesian model-averaging method. Our data come from a recent time period, namely 2004-2008, in a market with over 70 channels, making the data more typical of today's viewing environment. The simple models that are commonly used in industry do not forecast as well as any econometric models. Furthermore, time series methods are not applicable, as many programs are broadcast only once. However, we find that a relatively straightforward random effects regression model often performs as well as more sophisticated Bayesian models in out-of-sample forecasting. Finally, we demonstrate that making improvements in ratings forecasts could save the television industry between $250 and $586 million per year.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:intfor:v:27:y:2011:i:4:p:1215-1240

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    References listed on IDEAS

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    4. 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.
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    6. Smith, Michael & Kohn, Robert & Mathur, Sharat K., 2000. "Bayesian Semiparametric Regression: An Exposition and Application to Print Advertising Data," Journal of Business Research, Elsevier, vol. 49(3), pages 229-244, September.
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    8. Nikolopoulos, K. & Goodwin, P. & Patelis, A. & Assimakopoulos, V., 2007. "Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches," European Journal of Operational Research, Elsevier, vol. 180(1), pages 354-368, July.
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    Cited by:

    1. Aiste Ruseckaite & Dennis Fok & Peter Goos, 2016. "Flexible Mixture-Amount Models for Business and Industry using Gaussian Processes," Tinbergen Institute Discussion Papers 16-075/III, Tinbergen Institute.
    2. 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.
    3. Keita Kinjo & Shinya Sugawara, 2014. "An Empirical Analysis for a Case-based Decision to Watch Japanese TV dramas," CIRJE F-Series CIRJE-F-940, CIRJE, Faculty of Economics, University of Tokyo.
    4. Alexandra Mello Schmidt & Dani Gamerman & Ajax Moreira, 1999. "An adaptive resampling scheme for cycle estimation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(5), pages 619-641.


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