Movie Industry Economics: How Data Analytics Can Help Predict Movies’ Financial Success
Copyright (c) 2020 Christophe Bruneel, Jean-Louis Guy, Dominique Haughton, Nicolas Lemercier, Mark-David McLaughlin, Kevin Mentzer, Quentin Vialle, Changan Zhang, Paul Clemens Murschetz, Barira Bakhtawar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Purpose: Data analytics techniques can help to predict movie success, as measured by box office sales or Oscar awards. Revenue prediction of a movie before its theatrical release is also an important indicator for attracting investors. While measures for predicting the success of a movie in box office sales and awards are widely missing, this study uses data analytics techniques to present a new measure for prediction of movies’ financial success.
Methodology: Data were collected by web-scraping and text mining. Classification and Regression Tree (CART), Random Forests, Conditional Forests, and Gradient Boosting were used and a model for prediction of movies' financial success proposed. Content strategy and generating high profile reviews with complex themes can add to controversy and increase the chance of nomination for major movie awards, including Oscars.
Findings/Contribution: Findings show that data analytics is key to predicting the success of movies. Although predicting sales based on data available before the release remains a difficult endeavor, even with state-of-the-art analytics technologies, it potentially reduces the risk of investors, studios and other stakeholders to select successful film candidates and have them chosen before the production process starts. The contribution of this study is to develop a model for predicting box office sales and the chance of nomination for winning Oscars.
Practical Implications: Cinema managers and investors can use the proposed model as a guide for predicting movies’ financial success.