Predicting Gross Movie Revenue

TL;DR

This paper discusses the importance and challenges of predicting movie box-office revenue, emphasizing how data science and computational models can improve accuracy in this complex task.

Contribution

It highlights the significance of revenue prediction in the film industry and suggests that modern data-driven approaches can effectively handle the complexity of factors involved.

Findings

Predicting movie revenue is crucial for industry planning.

Manual prediction is difficult due to many variables.

Data science can simplify revenue forecasting.

Abstract

'There is no terror in the bang, only is the anticipation of it' - Alfred Hitchcock. Yet there is everything in correctly anticipating the bang a movie would make in the box-office. Movies make a high profile, billion dollar industry and prediction of movie revenue can be very lucrative. Predicted revenues can be used for planning both the production and distribution stages. For example, projected gross revenue can be used to plan the remuneration of the actors and crew members as well as other parts of the budget [1]. Success or failure of a movie can depend on many factors: star-power, release date, budget, MPAA (Motion Picture Association of America) rating, plot and the highly unpredictable human reactions. The enormity of the number of exogenous variables makes manual revenue prediction process extremely difficult. However, in the era of computer and data sciences, volumes of data can be efficiently processed and modelled. Hence the tough job of predicting gross revenue of a movie can be simplified with the help of modern computing power and the historical data available as movie databases [2].