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Predicting Movie Box Office Revenue According to Budget and Runtime

Executive Summary:

Global box office revenue was worth 42.2 billion U.S. dollars in 2019 (Statista, 2021) and was forecast to increase in 2020 had the COVID pandemic not forced the closure of international theatres. The average budget for a movie is $32,000,000, with most of this going toward actors’ fees (Anderton, 2016). Movie budgets have steadily increased over the last two decades, as productions put more and more money into creating bigger movies with well-known actors, possibly hoping that a movie with many big names will result in high ticket sales (The Number, ND).

In this analysis I investigate whether extending a movie’s budget to hire higher profile stars is likely to result in higher revenue. I will also create a model that allows me to predict the expected revenue for a movie based on the movies budget and runtime. To this end, I have chosen 5,176 movies from wide ranging genres. The budgets range from $93 to $380,000,000, and the revenue ranges from $100,000 to $2,787,965,087.

In this analysis I investigate whether extending a movie’s budget to hire higher profile stars is likely to result in higher revenue. I will also create a model that allows me to predict the expected revenue for a movie based on the movies budget and runtime. To this end, I have chosen 5,176 movies from wide ranging genres. The budgets range from $93 to $380,000,000, and the revenue ranges from $100,000 to $2,787,965,087.

Problem/System:

We live in a world in which celebrities hold a huge amount of power and influence. Social media allows movie stars to keep their fans up to date on their lives. This has resulted in an “influencer” trend. An influencer is a celebrity or individual who has the ability to affect the purchasing decisions of others (Influencer marketing hub, ND); they are able to charge a fee to advertise products or services to their followers. Movie stars are “influencers” who have the ability to persuade their fans and followers into watching their movies, enabling them to ask for even more money in order to perform in, and advertise, a production.

Movie producers need to know if paying a high percentage of a movie’s budget to big stars will return a higher revenue, or whether they can hire up and coming actors for a fraction of the cost. Assuming that the movie producer’s main priority is to ensure a high return on investment, they need to know whether hiring Mark Wahlberg who asks for 68$ million per movie (Forbes, 2017), will result in higher returns.

Therefore, I will analyze the budget and revenue of 5,176 movies, aiming to predict whether the size of a budget impacts revenue. I will also analyze whether a movies’ runtime effects the final box office revenue. Runtime is a very important aspect of a movie, with cinema prices reaching almost €13, producers need to know whether customers are willing to pay this money to watch shorter movies, or if they would prefer to get their full fees worth by watching a movie double the length.

Horror movies have an average runtime of 90 minutes, while the movie "IT" had a runtime of two hours and fifteen minutes - almost 50% longer than the average horror movie. “IT” also managed to gross $327,481,748 (IMdb, 2017) which is 50% above the average revenue for a movies’ box office return (Excel Data). Other examples of successful movies that ran over 2 hours are ‘Avatar’ and ‘Interstellar’. These movies grossed more in the box office than the average movie’s income for the same genre.

In order to conduct my analysis, I used excel 2016 and its tool pack Data Analysis. I ran a regression analysis that shows the revenues (dependent variable) according to runtime and budget (independent variables). I wanted to measure the dependent variable, and the most important variable is the output, which of course must be the revenue. The independent variables will affect that output (dependent variable); the variables that I want to study and correlate to the revenue, which in my case will be the runtime and budget as you will see in the excel spreadsheet screenshot below.

Data:

The data was collected from the website Kaggle.com, which included extensive information for each movie, such as the movie’s IMdb number, number of votes, whether the movie was made for an adult or kids, production company, poster information, and so on. I cleaned this data and chose the relevant information needed to carry out my analysis. Following this, I reformatted the data in order to give a more accurate output. The data also needed to be converted into excel .xlsx as it was in a .csv file. After converting and cleaning the data, I sorted it to be ready to use in the look you will find on the final spreadsheet below (1-30 out of 5176 movies).

predicting-movie-box-office-revenue-according-to-budget-and-runtime

Analysis and Insights:

Having analyzed the results of the movie’s runtime, budget and revenue you will find that the ‘multiple R’ is 0.73 which is low, but still close to 1 indicating a good linear relationship between the variables. However, ‘adjusted R square’ has a value of 0.52 which means that only 52% of the results can be explained by the model of the analysis. ‘P values’ must be less than 0.05 in both cases which means I have statistically significant results, while the coefficients have a positive value which means that the independent variables have a positive relationship with the revenue.

After taking a closer look at the data I found that only 379 movies out of 1,915 had lost money after investing more than the average budget of 32$ Million. This means, for 80% of these cases, when the budget or runtime increases the revenue also increases. However, the analysis is showing that this is the case for only 52% of the movies.

This analysis of the data led me to query why the test shows 52%, as opposed to 80%. After further investigation, I found the answer to my question in the following graphs.

predicting-movie-box-office-revenue-according-to-budget-and-runtime
predicting-movie-box-office-revenue-according-to-budget-and-runtime

The red circles represent what I regard to be excellent movies: movies that were created for the love of art and to tell an outstanding story, not driven primarily by commercial success and therefore not dependent on high-profile actors to achieve relative success. This, I assume, is why the data indicates an inconsistency within the idea that high budget equaling high revenue. I can see that there are many movies that made huge revenue without spending more than the average 32$ Million, and without increasing the runtime above the average of 110 minutes. However, the results show that ultimately movies with a longer runtime and higher budget gross more in the box office.

Conclusion:

Considering the clear results of the test, I can categorically confirm that the larger the budget, the higher the revenue will be (assuming similar quality of movie). I can also confirm that the longer the runtime, again the higher the revenue will be. Furthermore, assuming that the movies with larger budgets have bigger stars attached I can also conclude that investing in high profile actors to star in a movie will result in higher returns in the box office.

However, as you can see in the graphs above; significant profits can also be made without spending more than the average budget and without casting the big stars who demand over 50$ million per movie.

predicting-movie-box-office-revenue-according-to-budget-and-runtime
predicting-movie-box-office-revenue-according-to-budget-and-runtime

BIBLIOGRAPHY

Anderton, Learn How a $200 Million Movie Budget Is Spent, 2018, http://www.slashfilmmovie.com/movie-budget-spending/ [Accessed 14 March 2018]

Forbs, Forbes names highest-paid actors of 2017, 2017, https://www.cbsnews.com/pictures/forbes-highest-paid-actors-of-2017/ [Accessed 14 March 2018]

IMdb, IT, 2017, http://www.imdb.com/title/tt1396484/ [Accessed 14 March 2018]

Influencer marketing hub, What is an Influencer?, ND https://influencermarketinghub.com/what-is-an-influencer/ [Accessed 12 March 2018]

Statista, Film and Movie Industry - Statistics & Facts, 2017, https://www.statista.com/topics/964/film/ [Accessed 12 March 2018]

The Numbers, ND, Movie Budgets, https://www.the-numbers.com/movie/budgets/all [Accessed 15 March 2018]

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