Rockbuster
Analysis


ABOUT THE PROJECT

  • As a data analyst at Rockbuster Stealth LLC, I've been tasked with aiding the
    launch strategy for their new online video rental service. The company aims to adapt to the competitive landscape dominated by streaming services like Netflix and Amazon Prime. My initial responsibility involves loading all of Rockbuster's data into a relational database management system (RDBMS).

  • This data will be utilized to conduct SQL-based analysis and address ad-hoc
    business inquiries from various departments, ranging from inventory
    management to customer insights. Ultimately, the insights derived from this
    analysis will be consolidated into a concise format for presentation to the
    Rockbuster Stealth Management Board.

Challenges:

  • Writing complex Common Table Expressions (CTEs) in SQL can bedaunting, especially when dealing with intricate data relationshipsand multiple subqueries.
  • Using my mentor advices I could break down the query into smaller,more manageable sections. This approach enhanced readabilityand comprehension, making it easier to troubleshoot.

Objectives:

Analyze Rockbuster's move to launch an online rental service with a data-driven approach to uncover insights and recommendations aligning with the company's 2020 strategy.

Key Points:
• Customers base
• Sales figures variation between different Geographic regions
• Most and least movies revenue gain


Tasks & Utilities:
Executed data cleaning, integration, and transformationusing PostgreSQL, adept in performing subqueries and CTEs, and demonstrated proficiency in visualizing and
presenting results using Microsoft Excel, PowerPoint, and Tableau.

Data Exploration & Analysis:

                
  • By employing subqueries and multiple joins, a script was crafted to determine the countries with the highest customer counts,be nefiting our marketing team.

  • Understanding the structure of different tables and the relationships between primary and foreign keys proved crucial in executing these joins efficiently. This enabled us to focus marketing efforts on regions with the largest customer bases.

    Loyal Customers

    • Loyal customers hold significant value for companies. Here, we present a tree map generated with Tableau showcasing customers who have made the highest expenditures. This visualization is created using complex sub queries to provide insightful data analysis.

    Recommendations:

    • As we can see, 10 countries contributingmore than 30% of our sales.  Understanding customer preferences in Genres and movies within thesemarkets is crucial.

    •  Implement AI algorithms to analyze customer behavior and forecast their needs.

    • Leverage technology to tailor our offerings and services based on customer preferences.

    Technical lessons:

    SQL Proficiency: Mastering complex SQL queries, including Common Table Expressions (CTEs), subqueries, and joins, is essential for extracting insights from relational databases efficiently.

  • Data Cleaning and Transformation: Executing data cleaning, integration, and transformation tasks using tools like PostgreSQL ensures data quality and consistency, laying a solid foundation for analysis.

  • Data Visualization Skills: Proficiency in visualizing and presenting results using tools like Microsoft Excel, PowerPoint, and Tableau enhances communication of insights to stakeholders and management effectively.

  • Understanding Data Relationships: Understanding the structure of different tables and relationships between primary and foreign keys is crucial for executing joins efficiently and deriving meaningful insights from data.


    • Thanks for reviewing this Analysis, if you would like to see more details please visit its Tableau Story-Board.