Rockbuster Stealth LLC is a company that aims to have a resurgence in the video rental service industry in order to compete with well-known streaming services such as Amazon Prime and Netflix. How can they use their existing data on video rentals and regional sales data to understand where to focus first?
This project was conducted by analyzing a relational database of Rockbuster's video rental data. Queries, subqueries and Common Table Expressions were used to various business questions about top movies, customers, regions, and total sales.
Relational Databases
Entity Relationship Diagrams
Database querying
Joining Tables
Performing Subqueries
Common Table Expressions
Excel
PowerPoint
Tableau
SQL
The relational database only has information about movies 2006. Time series analysis could not be completed as a result.
All movies were only available in English, limiting the potential amount of sales in various regions.
The top 5 movies best selling movies were Telegraph Voyage, Zorro Ark, Wife Turn, Innocent Usual, and Hustler Party.
While Rockbuster has customers across the globe, the Asian region has been the most lucrative.
The European region has the second largest amount of sales due to the large number of countries with customers.
India and China lead in total revenue, followed by the United States, Japan, and Mexico.
The top categories by rental duration include Thriller, Travel, and Music films.
However, the most profitable film categories were Sports, Sci-Fi, and Animation movies.
In particular, despite Thriller films being rented the longest, they are the least profitable by far. This is due to the fact that there's only one available Thriller film.
The largest amount of Rockbuster customers are in Asia, in particular India, China, and Japan.
The United States and Mexico are also within the top 5 countries based on customer count.
High lifetime value customers were classified based on whether or not their total sales were at least 1.5 times the average sales of all customers.
These customers were located throughout various countries, with India, the Philippines, and the United States having the largest numbers.
Offer to also rent TV shows instead of just films in order to attract a wider audience.
Create a machine learning program to offer potential recommendations for users to maintain engagement.
Update inventory to include titles from years other than 2006. The current selection limits potential interest.
Include more film options for popular genres such as Travel, Music, and Family and also profitable genres such as Sports, Sci-Fi, and Animation.
Offer films in other languages to attract more customers.
Target top countries in Asia and North America such as India, China, and the United States.
Focus on advertising in these regions to increase popularity, as customers are currently spread far apart with an average of only one customer per city.