SQL portfolio project based on the classic Chinook dataset (albums, artists, tracks, customers, invoices).
The goal is to analyze music sales performance, customer behavior, and employee productivity using SQL.
Key business questions:

• What are the monthly revenue trends?
• Who are the top customers and employees?
• Which genres and artists generate the most sales?
• What are the cohort and RFM segmentation patterns?

• Database: chinook.db (SQLite version of Chinook)
• Helper View: v_invoice_detail (created via bootstrap_chinook.sql)
• Key Tables:
  invoices, invoice_items, customers, employees, tracks, albums, artists, genres, media_types

# 1️⃣ Place 'chinook.db' in the project folder

# 2️⃣ Build the helper view (required for queries)
sqlite3 chinook.db ".read bootstrap_chinook.sql"

# 3️⃣ Run analysis queries
# Open queries.sql in VS Code (SQLTools) → select query → Ctrl+E, Ctrl+E

The analysis includes:

• 💰 Monthly revenue and MoM (Month-over-Month) growth.
• 🎧 Top customers by total purchases and revenue.
• 🎵 Genre and artist performance by total sales.
• 👨‍💼 Employee productivity and support rep metrics.
• 🧺 Market basket analysis (track pair sales).
• 📈 Cohorts and RFM segmentation.

• Rock and Latin genres dominate total sales (~45% combined).
• Customers from the USA and Germany generate the highest revenue.
• Employees Jane Peacock and Steve Johnson consistently outperform in client support.
• Loyal customer cohorts exhibit strong repeat-purchase behavior after 6 months.
• High RFM customers (top 20%) contribute ~60% of total revenue.

These insights can help music store managers and digital distributors:

• Identify high-value customers and optimize loyalty programs.
• Refocus marketing efforts on top-performing genres and regions.
• Reward high-performing employees and optimize sales processes.

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