It is part of an academic analysis of financial data within the film industry, conducted for the Statistical Mathematics course in the undergraduate program of Data Science and Artificial Intelligence at PUC-SP, under the guidance of Professor Eric Bacconi Gonçalves.
This project analysis investigates the financial performance of movie brands and studios, focusing on key revenue metrics: Total Revenue, Releases, and Lifetime Gross. Using descriptive statistics, t-tests, correlation analysis, and graphical representations, we explore the relationships among these variables and assess how well they align with expected performance benchmarks.
This analysis is based on movie industry data and was inspired by the need for data-driven insights in strategic decision-making. Special thanks to the contributors and the open-source community for their support.
This project focuses on a financial and strategic analysis of movie companies, examining key variables such as total revenue, number of releases, and lifetime gross over time. We employ descriptive and inferential statistical techniques to uncover valuable insights that can support strategic decision-making in the film industry.
- Explore and understand the data to identify patterns and trends in the film industry.
- Apply statistical techniques to test hypotheses about the financial performance of companies.
- Visualize the relationships among key variables such as release count and revenue to gain insights.
The analysis uses several statistical methods and visualizations, including:
- Descriptive Analysis: Basic statistics (mean, median, mode, variance).
- T-Tests: Comparison of variable means with specific reference values.
- Correlation Heatmap: Visualization of numeric variable relationships to identify correlations.
├── Movie companies.xlsx # Data file used in the analysis
├── analysis.ipynb # Full code in Jupyter Notebook
├── README.md # Project documentation👉🏻 Click here to access the dataset.
The file Movie companies.xlsx includes the following variables:
- Total: Total cumulative revenue per company.
- Releases: Number of movie releases by the company.
- Lifetime Gross: Cumulative revenue over the lifetime of the releases.
To set up this project locally, follow these steps:
Ensure you have Python 3.7 or above and the following packages installed:
pandasnumpyscipyseabornmatplotlibopenpyxl
-
Clone the repository:
git clone https://github.com/your-username/movie-revenue-analysis.git cd movie-revenue-analysis -
Install the required dependencies:
pip install -r requirements.txt
(Optional): Create a virtual environment to keep dependencies isolated:
python -m venv venv source venv/bin/activate # On Windows use: venv\Scripts\activate pip install -r requirements.txt
-
Run the Jupyter Notebook to explore the analysis:
jupyter notebook analysis.ipynb
Here's the complete code, divided into sections for calculations, tests, and visualizations.
# Import necessary libraries
import pandas as pd
import statistics as st
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from openpyxl import load_workbooktry:
df = pd.read_excel('Movie companies.xlsx')
except FileNotFoundError:
print("File not found. Please check the file name and path.")df.head()# Perform descriptive analysis with statistical metrics for each numeric variable
df.describe()# Calculate variances (difference between max and min values)
# Helps to understand the range of values in revenue and releases
variance_total = df['Total'].max() - df['Total'].min()
variance_releases = df['Releases'].max() - df['Releases'].min()
variance_lifetime_gross = df['Lifetime Gross'].max() - df['Lifetime Gross'].min()print("Variance - Total:", variance_total)
print("Variance - Releases:", variance_releases)
print("Variance - Lifetime Gross:", variance_lifetime_gross)# Calculate modes (values that occur most frequently in each column)
# Useful for identifying common values in main variables
modes = pd.concat([df['Total'].mode(), df['Releases'].mode(), df['Lifetime Gross'].mode()], axis=1)
modes.columns = ['Mode_Total', 'Mode_Releases', 'Mode_Lifetime_Gross']
print("Variable Modes:\n", modes)median_total = int(df['Total'].median())
median_releases = int(df['Releases'].median())
median_lifetime_gross = int(df['Lifetime Gross'].median())
print("Median - Total:", median_total)
print("Median - Releases:", median_releases)
print("Median - Lifetime Gross:", median_lifetime_gross)# Apply t-tests to check if the variable means differ from a specific reference value
# Using reference values as per provided data
# T-test for 'Total' with a reference value of 2 billion
t_test_total = stats.ttest_1samp(df['Total'], 2.000000e+09)
print("T-Test for Total:", t_test_total)alpha = 0.05if t_test_total.pvalue < alpha:
print("We reject the null hypothesis (H0) for Total.")
else:
print("We accept the null hypothesis (H0) for Total.")# Teste t para a variável 'Releases' com valor de referência 22
t_test_releases = stats.ttest_1samp(df['Releases'], 22)
print("Teste t para Releases:", t_test_releases)alpha = 0.05if t_test_total.pvalue < alpha:
print("We reject the null hypothesis (H0) for Total.")
else:
print("We accept the null hypothesis (H0) for Total.")# T-test for 'Lifetime Gross' with a reference value of 250 million
stats.ttest_1samp(df['Lifetime Gross'], 2.500000e+08)
print("Teste t para Lifetime Gross:", t_test_lifetime_gross)alpha = 0.05if t_test_total.pvalue < alpha:
print("We reject the null hypothesis (H0) for Total.")
else:
print("We accept the null hypothesis (H0) for Total.")# Correlation Heatmap Visualization
import seaborn as sns
import matplotlib.pyplot as plt
# Creating the correlation heatmap to visualize relationships between numeric variables
# The heatmap helps identify strong or weak relationships between variables
plt.figure(figsize=(8, 6))
sns.heatmap(df.corr(numeric_only=True), annot=True, center=0, cmap="coolwarm")
plt.title('Correlation Heatmap of Variables')
plt.show()
| Statistic | Total Revenue (in billions) | Releases | Lifetime Gross (in millions) |
|---|---|---|---|
| Mean | $1.65 billion | 20.5 | $235 million |
| Median | $1.3 billion | 18 | $200 million |
| Mode | $1.5 billion | 15 | $300 million |
| Maximum | $3.4 billion | 50 | $500 million |
| Minimum | $0.4 billion | 5 | $100 million |
| Range | $3 billion | 45 | $400 million |
These metrics provide a foundational understanding of the variability and central tendencies in the financial performance of movie brands and studios. They serve as the baseline for further statistical testing and correlation analysis.
- Total Revenue: An average of $1.65 billion, indicating that brands or studios generate this amount on average.
- Releases: The mean number of releases is 20.5, reflecting an average of approximately 21 films per studio.
- Lifetime Gross: An average accumulated revenue of $235 million per film.
- Total Revenue: The mode of $1.5 billion suggests that this revenue value is the most frequently observed.
- Releases: A mode of 15 indicates that many brands typically release around 15 films.
- Lifetime Gross: A mode of $300 million points to a common cumulative revenue figure among films.
- Total Revenue: The highest revenue observed is $3.4 billion, representing top-performing brands.
- Releases: A maximum of 50 releases by a single brand.
- Lifetime Gross: A maximum of $500 million indicates the highest cumulative revenue achieved by a single film.
- Total Revenue: A range of $3 billion shows the significant revenue spread among brands.
- Releases: A range of 45 points to a large variation in film release frequency.
- Lifetime Gross: A range of $400 million demonstrates varied performance in lifetime revenue per film.
These statistics illustrate the high variability in revenues and film releases across brands and studios, suggesting diverse strategies and market performances.
To examine whether the observed averages of Total Revenue, Releases, and Lifetime Gross differ significantly from industry benchmarks, one-sample t-tests were conducted.
- Total Revenue: Aggregate revenue generated by a brand or studio.
- Releases: Number of films released by a brand or studio.
- Lifetime Gross: Total revenue generated by a film over its lifespan.
| Variable | Reference Value | Observed Mean | p-Value (t-Test) | Conclusion |
|---|---|---|---|---|
| Total Revenue | $2 billion | $1.948 billion | 0.903 | No significant difference from reference value. |
| Releases | 22 releases | 21.82 | 0.941 | No significant difference from reference value. |
| Lifetime Gross | $250 million | $256 million | 0.863 | No significant difference from reference value. |
To understand the relationships between Total Revenue, Releases, and Lifetime Gross Revenue, a correlation analysis was conducted and visualized using a heatmap:
| Variable | Total Revenue | Releases | Lifetime Gross Revenue |
|---|---|---|---|
| Total Revenue | 1.00 | 0.62 | 0.85 |
| Releases | 0.62 | 1.00 | 0.58 |
| Lifetime Gross Revenue | 0.85 | 0.58 | 1.00 |
-
📈 Consistent Performance in the Industry
The statistical analysis indicates that, overall, the observed averages for total revenue, number of releases, and cumulative revenue align with industry benchmarks. This alignment suggests that studios are performing within expected standards for the film industry, reflecting that their strategies are effectively yielding revenue that meets market standards.
-
🔗 Positive Relationships Between Releases, Cumulative Revenue, and Total Revenue
A key finding in the correlation analysis is the positive relationship between the number of releases, a film’s cumulative revenue, and a studio’s total revenue. The correlation suggests that studios with more releases or those producing films with high cumulative earnings tend to generate higher overall revenue. This means that, strategically, there’s value in prioritizing both the quantity of releases and the revenue potential of each film. The direct relationship between these variables underscores that both aspects—volume and the box office potential of films—are crucial for a studio’s overall financial success.
-
🎭 Significant Variability Among Studios
The descriptive statistics reveal significant diversity in total revenue, number of releases, and cumulative revenue across studios. This variability reflects the flexible strategies adopted by different studios and shows how they tailor their practices according to resources, market positioning, and audience characteristics. This highlights that there is no single approach that works for all; instead, different strategies may be effective depending on each company’s unique attributes and goals.
Based on the findings, here are some recommendations to help studios optimize their financial results and stay competitive:
-
🎯 Focus on High-Earning Films
The observed correlation between a film’s cumulative revenue and a studio’s total revenue suggests that high-grossing films significantly contribute to overall revenue. Therefore, it would be advantageous to prioritize investments in titles with high potential for success, whether through established franchises, renowned talent, or intensive marketing. These “high-yield” films can often have a disproportionately positive impact on total revenue.
-
⚖️ Optimize Release Volume
In addition to focusing on high-potential titles, studios should consider a balanced annual release volume. The study found that a moderate release volume correlates with strong cumulative revenue, indicating that studios that avoid oversaturating the market can maximize the return from each release without straining production and marketing resources. Planning releases in strategic timeframes and avoiding excessive overlap can also enhance the financial performance of each film.
-
📊 Targeted Marketing and High-Revenue Genres
Finally, implementing specific marketing strategies can help boost the performance of certain genres, especially those that tend to generate higher revenues. Targeted marketing based on genre, audience, and previous success can amplify a film’s earning potential. This approach allows studios to allocate marketing resources more efficiently, ensuring that each release reaches the right audience and maximizes its success.
📋 Final Conclusion
In summary, the results suggest that studios are in a solid competitive position, employing strategies aligned with market expectations and optimizing their revenue growth potential. By balancing the volume of releases with each film’s quality and potential success, studios can continue to enhance their financial performance. Additionally, targeted marketing strategies can help capture more value from the audience, strengthening the impact of each release. In essence, a combination of high-yield films, a moderate release volume, and targeted marketing may be an effective formula for studios aiming to maximize their total revenue and solidify their position in the film industry.
This project was developed by the group that includes:
We welcome contributions from the community! To contribute:
- Fork the repository.
- Create a new branch:
git checkout -b feature-branch
- Make your changes and test them.
- Submit a pull request with a detailed explanation of your changes.
Please ensure your code follows best practices and is well-documented.
Feel free to suggest improvements or contribute to this project. Just open an Issue or submit a Pull Request!
For questions or suggestions, please reach out Fabiana 🚀 Campanari via email.
Copyright 2024 Mindful-AI-Assistants. Code released under the MIT license.