Rockbuster Stealth Sales Analysis

Project Overview
Rockbuster Stealth LLC, a movie rental company with global store presence, aims to combat competition from streaming services like Netflix and Amazon Prime. To stay relevant, they plan to leverage their movie licenses and launch an online video rental service.
In this project, data is leveraged to provide an answer to the ad-hoc business questions other departments may have regarding Rockbuster's sales and market.
The dataset for this project was fabricated, hence there were no concerns around violation of data privacy practices. However, in real world while analyzing actual customer data, various measures can be implemented for sensitive PII such as anonymization, pseudonymization, and data masking to protect customer privacy during data analysis and reporting.
The goal of this project was to provide me with practical exposure to relational databases and SQL querying techniques.
Tools and Techniques
Dataset was loaded onto PostgreSQL database management system.
Data was queried and analyzed using SQL through different methods such as subqueries and CTEs.
Visualizations were created using Tableau Public.
Database Exploration
I loaded the dataset onto PostgreSQL and extracted an entity relationship diagram in order to understand the relationships between the tables and use it to create a data dictionary.
Descriptive Analysis
As the first step of the analysis, I provided a summary of data to obtain an initial insight into Rockbuster's current customer base, market reach, and sales revenue.
As part of this phase, I conducted geospatial analysis to identify the countries in which Rockbuster has the largest market and generates the most revenue. I referred to the entity relationship diagram and used JOIN functions to merge the tables and extract the target data. Doing this allowed me to determine the top 5 countries by revenue and customer base.
I also identified high lifetime value customers across the globe using a CTE as it was more readable and proved to be more efficient during query testing.
Lastly, I created the maps in a Tableau dashboard in order to make the findings more comprehensible for the audience.
In the final step of my descriptive analysis, I identified the most and least revenue generating genres and movies in each of the top countries using CTEs and aggregations.
This project not only helped me acquire valuable familiarity with SQL and relational databases, it significantly increased my analytical thinking abilities. It enabled me to sharpen my understanding on how to join tables that are related through complicated routes within the database and use groupings and aggregations to achieve the desired analytical insights.
Tableau Dashboard
GitHub Repository

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