Modeling Influencing Factors in U.S. Film Success (1940-2024)

Abstract

This study explores the economic impact of marketing and casting strategies on the U.S. film industry, valued at almost $93 billion in 2022. With high production costs and shifting consumer preferences, effective marketing and casting choices are crucial for maximizing return on investment (ROI). Using different models: Logistic regression, Random Forest and SVM models in R, this research analyzes factors like release timing, critic ratings, and genres to predict box office success, defined by high revenue and awards. The Random Forest Model achieved the highest accuracy, emphasizing that variables like “Runtime”, “IMDb Rating”, and “Rotten Tomatoes Rating” are critical predictors of success. The findings highlight the importance of strategic marketing that optimizes resources without exceeding diminishing returns. Furthermore, the study acknowledges data limitations caused by missing film details, such as those for Avatar : The Way of Water , which affect the completeness of the dataset. Future research should incorporate streaming data to better assess film success beyond box office metrics. These insights offer industry stakeholders data-driven recommendations for optimizing marketing, casting, and release strategies in an evolving entertainment landscape.