A generalized Bass model for predicting the sales patterns of motion pictures having seasonality and herd behavior
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Marketing scholars have expended much effort in developing quantitative models that forecast box office results, but performance has been below their expectations because of the failure to notice behavioral factors, such as herd behavior. Prior studies also have relied on weekly box office data, not daily data, and thus have failed to provide marketers with sufficient insights necessary for making key decisions. This paper offers a box office forecasting model that overcomes these weaknesses: a generalized Bass model (GBM) that reflects both daily seasonality and herd behavior. The daily box office data of 40 movies released in Korea are used for the analysis. In all, three models are compared: a GBM that takes into consideration both herd behavior and seasonality, a GBM considering seasonality only, and an exponential model as a benchmark model. The estimation results show that the proposed model performs better than the others in terms of model fitness and predictability.
Abstract
Marketing scholars have expended much effort in developing quantitative models that forecast box office results, but performance has been below their expectations because of the failure to notice behavioral factors, such as herd behavior. Prior studies also have relied on weekly box office data, not daily data, and thus have failed to provide marketers with sufficient insights necessary for making key decisions. This paper offers a box office forecasting model that overcomes these weaknesses: a generalized Bass model (GBM) that reflects both daily seasonality and herd behavior. The daily box office data of 40 movies released in Korea are used for the analysis. In all, three models are compared: a GBM that takes into consideration both herd behavior and seasonality, a GBM considering seasonality only, and an exponential model as a benchmark model. The estimation results show that the proposed model performs better than the others in terms of model fitness and predictability.
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