Forecasting television ratings

Research output: Contribution to journalArticleResearchpeer-review

19 Citations (Scopus)

Abstract

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 naive 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.
Original languageEnglish
Pages (from-to)1215 - 1240
Number of pages26
JournalInternational Journal of Forecasting
Volume27
Issue number4
DOIs
Publication statusPublished - 2011

Cite this

Danaher, Peter ; Dagger, Tracey ; Smith, Michael. / Forecasting television ratings. In: International Journal of Forecasting. 2011 ; Vol. 27, No. 4. pp. 1215 - 1240.
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Forecasting television ratings. / Danaher, Peter; Dagger, Tracey; Smith, Michael.

In: International Journal of Forecasting, Vol. 27, No. 4, 2011, p. 1215 - 1240.

Research output: Contribution to journalArticleResearchpeer-review

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T1 - Forecasting television ratings

AU - Danaher, Peter

AU - Dagger, Tracey

AU - Smith, Michael

PY - 2011

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N2 - 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 naive 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.

AB - 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 naive 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.

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