Global Movie Franchise Revenue and Budget Data

Tracks Lifetime Gross, Budgets, Ratings, and Release Dates

By Emma Culwell [source]

About this dataset

This dataset offers an extensive look at some of the most popular movie franchises in history, shedding light on their financial success and public reception. It includes data on the lifetime gross sales, budgets, ratings, and release dates of each featured movie. Furthermore, this dataset provides invaluable insights into how different elements such as ratings and runtime can affect the performance of a film at the box office. Whether you are an aspiring or established filmmaker looking for inspiration to craft your own successful blockbuster or simply a fan curious about these films’ inner workings, this dataset offers an unprecedented level of detail regarding many beloved franchises

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How to use the dataset

This dataset provides comprehensive information on movie franchises released worldwide between 2000 and 2020. It includes data such as lifetime gross, budget, rating, runtime, release date and vote count/average. This dataset can be used to gain insights on the global movie industry trends over this time period.

The data can be explored in various ways to identify patterns of success or failure among movie franchises across countries, genres or decades. For example, you may want to examine the average budget for movies released each year or calculate the average number of votes received by movies of a particular genre. Additionally, you could use this dataset to compare different types of media (e.g., cable vs streaming) and understand how they impact box-office performance.

To get the most out of this data set it is essential that you first familiarize yourself with all the columns provided: Title: The title of the movie; Lifetime Gross: Total amount money earned by a franchise in all territories; Year: The year in which it was first made available publicly; Studio: The production company behind the production; Rating: Classification given by MPAA/BBFC; Runtime: Length in minutes/hours; Budget: Amount spent producing it ; Release Date : Date when publically announced Availability ; Vote Average : Average ratings based on user reviews ; Vote Count : Number people who rated franchise).
Once you have become comfortable with these variables then feel free to try out some larger analysis techniques such as predictive analytics (predicting future success based on existing trends) or clustering (grouping similar outcomes together). No matter which methods you decide to utilize it is important that you remember – always validate your assumptions! Good luck exploring!

Research Ideas

• A comparison of movie budget to box office returns, to identify over/underperforming movies.
• A study of the correlation between movie rating and viewership.
• An analysis of what types of movies tend to become franchise success stories (big budget, PG-13 rating, etc.)

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: MovieFranchises.csv | Column name | Description | |:-------------------|:------------------------------------------------------------------------| | Title | The title of the movie. (String) | | Lifetime Gross | The total amount of money the movie has made in its lifetime. (Integer) | | Year | The year the movie was released. (Integer) | | Studio | The studio that produced the movie. (String) | | Rating | The rating of the movie (e.g. PG-13, R, etc). (String) | | Runtime | The length of the movie in minutes. (Integer) | | Budget | The budget of the movie in USD. (Integer) | | ReleaseDate | The date the movie was released. (Date) | | VoteAvg | The average rating of the movie from users. (Float) | | VoteCount | The total number of votes the movie has received from users. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Emma Culwell.

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This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

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