Spotify-Music-Store-SQL-Analysis

This project showcases end-to-end SQL queries for analyzing a music store database. The queries are categorized into Beginner, Intermediate, and Advanced levels, each addressing business questions around customers, artists, invoices, and music preferences.

🧠 Key Business Questions Answered

🟢 Beginner Level

• Who is the most senior employee?
• Which countries have the most invoices?
• What are the top 3 invoice totals?
• Which city generates the most revenue (for a music festival promotion)?
• Who is the top customer based on spending?

🟡 Intermediate Level

• Who listens to Rock music? (with email and name)
• Top 10 artists with the most Rock music tracks
• Tracks with duration longer than the average

🔴 Advanced Level

• How much has each customer spent on the top-selling artist?
• Most popular music genre per country
• Top customer by spending in each country

📊 Techniques & Concepts Used

• Joins (INNER JOIN, SELF JOIN)
• Subqueries
• CTEs (Common Table Expressions)
• Aggregation (Sum, Count, Avg)
• Window Functions (ROW_NUMBER)
• Filtering & Sorting (WHERE, ORDER BY, GROUP BY)

💡 Insights & Use Cases

• Marketing Strategy: Identify top cities and countries for targeted campaigns.
• Customer Analytics: Discover loyal and high-spending customers.
• Artist Management: Recognize high-performing artists and genres.
• Business Expansion: Decide regions with high revenue potential for concerts or new releases.

📌 Tools Used

• SQL (My SQL Syntax)
• MySQL Workbench