Early Predictions of Movie Success: The Who, What, and When of Profitability

Abstract We focus on predicting the profitability of a movie to support movie-investment decisions at early stages of film production. By leveraging data from various sources, and using social network analysis and text mining techniques, the proposed system extracts several types of features, including “who” is in the cast, “what” a movie is about, “when” a movie will be released, as well as “hybrid” features. Experiment results showed that the system outperforms benchmark methods by a large margin. Novel features we proposed made weighty contributions to the prediction. In addition to designing a decision support system with practical utility, we also analyzed key factors of movie profitability. Furthermore, we demonstrated the prescriptive value of our system by illustrating how it can be used to recommend a set of profit-maximizing cast members. This research highlights the power of predictive and prescriptive data analytics in information systems to aid business decisions.

[1]  Thomas Ehrmann,et al.  We Don’t Need Another Hero — Implications from Network Structure and Resource Commitment for Movie Performance , 2008 .

[2]  Doina Caragea,et al.  Pre-release Box-Office Success Prediction for Motion Pictures , 2013, MLDM.

[3]  Mark Baimbridge,et al.  Movie admissions and rental income: the case of James Bond , 1997 .

[4]  Akbar Zaheer,et al.  Network Evolution: The Origins of Structural Holes , 2009 .

[5]  Yasemin Bozdogan,et al.  The determinants of box office revenue: a case based study: thirty, low budget, highest ROI films vs. thirty, big budget, highest grossing Hollywood films , 2013 .

[6]  James G. Surles,et al.  Model-Dependent Variance Inflation Factor Cutoff Values , 2002 .

[7]  Chih-Lin Chi,et al.  Individualized Patient-centered Lifestyle Recommendations: an Expert System for Communicating Patient Specific Cardiovascular Risk Information and Prioritizing Lifestyle Options , 2022 .

[8]  M. Holbrook,et al.  The role of actors and actresses in the success of films: how much is a movie star worth? , 1993 .

[9]  Z. John Zhang,et al.  From Story Line to Box Office: A New Approach for Green-Lighting Movie Scripts , 2007, Manag. Sci..

[10]  Taha Yasseri,et al.  Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data , 2012, PloS one.

[11]  Ramesh Sharda,et al.  Predicting box-office success of motion pictures with neural networks , 2006 .

[12]  Z. John Zhang,et al.  Assessing Box Office Performance Using Movie Scripts: A Kernel-Based Approach , 2014, IEEE Transactions on Knowledge and Data Engineering.

[13]  John Yen,et al.  Simulating inter-organizational collaboration network: a multi-relational and event-based approach , 2012, Simul..

[14]  Massimo Magni,et al.  Everybody Needs Somebody: The Influence of Team Network Structure on Information Technology Use , 2012, J. Manag. Inf. Syst..

[15]  Charu C. Aggarwal,et al.  The Inverse Classification Problem , 2010, Journal of Computer Science and Technology.

[16]  Kang Zhao,et al.  Early Prediction of Movie Success - What, Who, and When , 2015, SBP.

[17]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[18]  Roger Guimerà,et al.  Team Assembly Mechanisms Determine Collaboration Network Structure and Team Performance , 2005, Science.

[19]  Chien-Chung Chan,et al.  Prediction of movies box office performance using social media , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[20]  Max Kuhn,et al.  Applied Predictive Modeling , 2013 .

[21]  Bo Gao,et al.  User Recommendations in Reciprocal and Bipartite Social Networks--An Online Dating Case Study , 2013, IEEE Intelligent Systems.

[22]  R. Burt.  Structural Holes and Good Ideas1 , 2004, American Journal of Sociology.

[23]  Jerrold H. May,et al.  Evaluating and Tuning Predictive Data Mining Models Using Receiver Operating Characteristic Curves , 2004, J. Manag. Inf. Syst..

[24]  Bernardo A. Huberman,et al.  Predicting the Future with Social Media , 2010, 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[25]  Steven Skiena,et al.  Improving Movie Gross Prediction through News Analysis , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[26]  Steven B. Andrews,et al.  Structural Holes: The Social Structure of Competition , 1995, The SAGE Encyclopedia of Research Design.

[27]  Sriram Venkataraman,et al.  Blogs, Advertising, and Local-Market Movie Box Office Performance , 2013, Manag. Sci..

[28]  Jay F. Nunamaker,et al.  Enhancing Predictive Analytics for Anti-Phishing by Exploiting Website Genre Information , 2015, J. Manag. Inf. Syst..

[29]  Anita Elberse,et al.  The Power of Stars: Do Star Actors Drive the Success of Movies? , 2007 .

[30]  Federica Brunetta,et al.  What is critical to success in the movie industry? A study on key success factors in the Italian Motion Picture industry , 2008 .

[31]  W. Walls,et al.  Uncertainty in the Movie Industry: Does Star Power Reduce the Terror of the Box Office? , 1999 .

[32]  Xiang Wan,et al.  Cost-Sensitive Learning via Priority Sampling to Improve the Return on Marketing and CRM Investment , 2012, J. Manag. Inf. Syst..

[33]  Brinja Meiseberg,et al.  Diversity in teams and the success of cultural products , 2013 .

[34]  William Nick Street,et al.  A Budget-Constrained Inverse Classification Framework for Smooth Classifiers , 2016, 2017 IEEE International Conference on Data Mining Workshops (ICDMW).

[35]  W. D. Walls,et al.  Modeling Movie Success When ‘Nobody Knows Anything’: Conditional Stable-Distribution Analysis Of Film Returns , 2005 .

[36]  Bernardo A. Huberman,et al.  Predicting the Future with Social Media , 2010, Web Intelligence.

[37]  J. Simonoff,et al.  Predicting Movie Grosses: Winners and Losers, Blockbusters and Sleepers , 2000 .

[38]  B. Uzzi,et al.  Collaboration and Creativity: The Small World Problem1 , 2005, American Journal of Sociology.

[39]  Mark Lutter,et al.  Creative Success and Network Embeddedness: Explaining Critical Recognition of Film Directors in Hollywood, 1900–2010 , 2014 .

[40]  J. Eliashberg,et al.  MOVIEMOD: An Implementable Decision-Support System for Prerelease Market Evaluation of Motion Pictures , 2000 .

[41]  Bo Gao,et al.  Recommendation in Reciprocal and Bipartite Social Networks-A Case Study of Online Dating , 2013, SBP.

[42]  Jacky Akoka,et al.  A Taxonomy of Evaluation Methods for Information Systems Artifacts , 2015, J. Manag. Inf. Syst..

[43]  Alan R. Hevner,et al.  Design Science in Information Systems Research , 2004, MIS Q..