Various factors such as budget allocated, market positioning, release timing, and the type of the content are important in addressing the research problem.
The results of the model are aimed at predicting the success and failure of movie release in the initial opening week box office. Multi-linear regression and regression tree analysis of the data signified error in data interpretation. The percentage of error ranged from 37 to 43 percent. Therefore, it was important to improve data analysis accuracy in order to provide reliable research result. Data analysis of various variables included release time, type of content played, inclusion of an Oscar actor or producer, and genre. The model used in the research study did not bring out the intended accuracy, resulting into recommendation for a further research. There are myriad ways through which the research model can be improved in order to enhance accuracy. For instance, the inclusion of major variables in the research design such as marketing budget, channels, and running the model on a larger database platform. The research study discusses significant effects of various variables and how they determine success of a movie release.
The movie industry is one of the industries that have gained prominence due to prevailing dynamism. Movie investors put into consideration a wide range of factors in the determination of a successful movie. The opening week box office performance has a significant role in predicting fruitful movies. The BIDM report focuses on business prediction model, by carrying out multi-linear regression and regression tree, in addressing the situation. Variables such as budget, the presence of Oscar actor, type of content, release timing, and MPAA ratings have all been put under scrutiny in exploring the success of a movie in its opening week. The return on capital analysis of