Prediction of Box Office Gross Revenue Ramcharan Kakarla, Graduate Student, Oklahoma State University Dinesh Yadav Gaddam, Graduate Student, Oklahoma State University Institution(s) Abstract Data Exploration Objective To predict the gross of the movie based on the genre, ratings and other influencing factors To identify characteristics in the data that would mitigate the risks in business through data exploration June and May months over the past five years had movies with higher collections Based on MPAA (Motion Picture Association of America Rating) most movies are classified as PG13(Parental Guidance Cautioned) and R (Restricted) Interestingly MPAA G (General Audiences) rating movies have done well at the box office although low in number Action and Fantasy Movies have greater openings than any other genre of movies Movie making has always been a risky business especially with huge financial investments at stake. This project is an attempt to explore what factors impact the box office collections using JMP Pro11. It is well known in the movie industry that 80% of the Hollywood movies fail to turn profits. The unknowns are the ways to minimize the risk and maximize gains. Our model estimates the gross revenue of a movie based on the different inputs such as length of the movie, IMDB rating of the movie, genre, star power, studio, budget of the movie, first week’s revenue information and so on related to the movie industry. Raw data has been collected for the years 2009, 2010, 2011, 2012 and 2013 by culling data from three different web sites and merging it to create the modeling data set. The data for the first 3 years is used for training the model and the data from the last 2 years is used for validation. Models such as Neural Networks and Regression have been built to estimate the gross revenue amount. Data Data for the analysis has been obtained from IMDB website Box office Mojo Wikipedia The data has been transformed to desired format using JMP Pro 11. The final dataset had 30 variables with 543 observations accumulated over the span of 5 years. These include movie runtime, IMDB Rating, MPAA Rating, Opening theatres, Closing theatres, Opening week gross, genre, Number of movies directed by the director of the movie, Average gross of films of director, Production house, Month of release, Number of users who have rated the movie Model Building To find the influencing factors for predicting the gross box office collections models such as linear regressions, partitions and neural networks have been built using JMP Pro 11 Prediction Profiler in JMP is a great interactive tool for adjusting the levels to manually see the changes in the output by changing the input variables interactively

Prediction of Box Office Gross Revenue Ramcharan Kakarla, Graduate Student, Oklahoma State University Dinesh Yadav Gaddam, Graduate Student, Oklahoma State University Institution(s) Future Research Results To study the effect of pre release marketing especially trailer reviews and their effects on the movie gross collections To combine the prerelease and post release parameters to predict the collections and see whether the model has improved Opening weekend gross was found to be the most influencing factor in predicting the box office collections Number of Theatres released was the next most influencing factor The biggest hit movie gross collection of directors prior movies was found to be a good predictor Comedy and Horror movies are more predictable than movies of other genres Although Movie runtime was found to be interesting in the initial analysis with a direct relation in higher rated movies it failed to make it to the final list Interestingly movie rating was not found to be a statistical influencing factor in deciding the box office collections Conclusions Since opening weekend collections are an important indicator of how much the movie could make, it is important for the movie team to maximize the openings in the first week through large scale releases Trailers play a critical role in drawing people towards the theatres especially in the opening week. Trailers officially released through YouTube have to be studied and analyzed through sentiment analysis. If there is a negative sentiment, Attempting another trailer might make a difference One good film in the directors kitty is a value addition to the movie which can boost sales irrespective of the experience and number of movies that a director has done May, June and July have a positive trend in the box office collections Since Action genre movies draw greater weekend collections, it might be a good idea to work it in the June July months to reduce the risk New Year season was not a great influencer in the box office collections References http://www.sciencedirect.com/science/article/pii/S0957417405001399 (Predicting box-office success of motion pictures with neural networks by Dr. Ramesh Sharda, Dr. Drusun Delen) http://support.sas.com/documentation/onlinedoc/jmp/11/UsingJMP.pdf Acknowledgement We would like to thank Dr. Goutam Chakraborty, Professor in Marketing, Oklahoma State University for his continuous support and guidance.

Prediction of Box Office Gross Revenue Ramcharan Kakarla, Graduate Student, Oklahoma State University Dinesh Yadav Gaddam, Graduate Student, Oklahoma State University Institution(s) Data Exploration Results