Rockbuster Stealth LLC

Repository on Github

Client Goal

To discover data-driven insights that will inform their new business model and launch with success.

Project Summary

Overview

Rockbuster Stealth LLC wanted to transition their business model towards an online video rental service.

To support this venture, management was looking for some data-driven answers to inform their launch strategy.

Purpose and Context

This project was built as part of the Career Foundry "Become a Data Analyst" curriculum. The client, Rockbuster Stealth LLC, is a fictional company. Their challenge, though, is real – how can an established business leverage data to set itself up for success as it moves into the future.

My Role

For this project, I served as business intelligence analyst and storyteller. I worked with the data from start to finish, building a data dictionary, providing the business insights and presentation, as well as sharing insights and recommendations with the executive team.

Tools and Analytical Techniques

This project was created with the following tools:

• SQL in PostgreSQL
• Tableau Public

The skills used to complete this project include:

• Relational databases
• Database querying
• Filtering
• Cleaning and summarizing data
• Joining tables
• Subqueries and Common Table Expressions
• Insight development
• Data visualization
• Presentation development
• Storytelling

Project

Project Scope and Planning

Align requirements, project scope, and desired outcomes of project.

In this project, I set out to analyze historical Rockbuster data regarding the films, rental patterns, customers, geography, and revenue.

Specifically, I aimed to uncover:

• What movies contribute to rentals and revenue
• Where the current customers are located
• Any other helpful insights

Through this analysis, I would be able to make recommendations for a strategic launch of the client's online video rental services.

Data Prep and Exploration

Determine and collect data for project, then clean, profile, and explore.

As the data was provided by Career Foundry, I was able to start off on the exploration and cleaning right away.

As part of this process, I completed the following steps:

• Loaded the data into PostgreSQL and extracted the entity relationship diagram (ERD)
• Created a data dictionary (linked below)
• Familiarized myself with the data to start thinking about how to answer the business questions
• Began to organize, sort, and filter the data with various SQL queries
• Completed queries to identify any dirty data that could skew analysis – none found so no action taken to clean
• Created a data profile with summary statistics

Challenges and Decisions in this Phase

• During the analysis, it was noted that the dataset timeline was limited in that it only included information from 2006 . This presented a limited view for the analysis as rental trends over time could not be evaluated.
• Had this been a real project, it may have been more critical to access a longer timeframe of rental and customer history. Between this being a fictional project (and this was the only data available) and also considering that most recent history would present the most recent patterns, the analysis continued with this limited dataset.

Analysis, Insights, and Visualization

Interpret data patterns and trends to uncover most impactful elements for project.

Once the initial exploration of the data was complete, it was time to start answering the key business questions and deriving the insights that would help the client form their strategy.

The primary insights and visualizations that were selected for presentation to stakeholders included themes in:

1. Rental count and revenue generated by movie genre and rating – to confirm customer preferences
2. Current customer base – to confirm which countries have the highest number of and highest value customers

Customer Preferences

Key Observation: The number of rentals and revenue reveal similar patterns in customer preference. For genre, Sports and Animation dominate the customer preference. For rating, PG-13 movies are the primary source of revenue and rentals.

Key Insights from Rental Numbers

Customer Base

Key Observation: The countries with the most number of customers provide the most revenue. 

Key Insights from Customer Base

Average Spend:

$106

for the Top 5 customers

Average Number of Customers:

32

for the Top 10 countries

Most Customers + Revenue:

India, China, USA

GLOBAL CUSTOMER HOTSPOTS

Major markets, including India, China and the USA, account for 57% of total customers, indicating high-potential regions for focused marketing as part of launch of a new streaming platform. Secondary markets like Japan, Mexico, Russia, and Brazil also show promising demand and could benefit from tailored promotional efforts. The image above presents a ranking of the top markets by customer count, reinforcing the importance of specific regions. High customer concentration in these markets suggests opportunities for regionalized promotions to further boost engagement.

Storytelling and Presentation

Assemble actionable recommendations to drive the key outcomes for stakeholder presentation.

A story evolved as I reviewed the most crucial observations from the data. By aligning these findings with Rockbuster's objectives, I formulated data-informed recommendations to shape their revised business model.

Conclusions

By rental count and revenue:

1. the most popular movie genre is sports.
2. the most popular movie rating is PG-13.

The majority of Rockbuster's customer base exists in India, China, and the USA.

Recommendations

To launch successfully:

1. Focus marketing and service towards movies in the most popular genres and ratings.
2. Use current loyal customer base (top countries) for launch focus.

Next Steps

Define specific strategy with focus on…

1. Most popular genres
2. Most favored ratings
3. Current customer geography

…to maximize success as Rockbuster moves into new online rental spaces.

Dataset

The data used in this project was provided by Career Foundry and included data tables with information such as film inventory, customers, and payments.