🛒 Superstore Sales Analysis (SQLite + SQL)

📊 Project Overview

This is a SQL portfolio project based on the classic Sample Superstore dataset.
The goal is to explore and analyze key business questions such as:

• Which customers generate the most profit?
• Which regions and categories perform best?
• How do discounts impact profit margins?

The project demonstrates proficiency in SQL, business analysis, and data storytelling through queries.

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🧱 Dataset

• Source: sample_superstore.csv (public Kaggle dataset)
• Size: ~10,000 rows
• Date Range: 2014-10-10 → 2017-12-30
• Target Table: superstore (created by setup_superstore.sql)
• Key Columns:
  OrderDate (YYYY-MM-DD), Sales (REAL), Profit (REAL), Discount (0–1),
  Category, SubCategory, Segment, Region, CustomerName, ProductName

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⚙️ How to Reproduce

# 1️⃣ Build or refresh the SQLite database and load the CSV
sqlite3 superstore.db ".read setup_superstore.sql"

# 2️⃣ Run analysis queries (e.g. queries.sql)
# Inside VS Code with SQLTools: open queries.sql → select a statement → Ctrl+E, Ctrl+E

🧮 Analysis Highlights

The analysis includes:

• 🏆 Top-10 customers by total profit and sales volume.
• 💰 AOV by category (Average Order Value).
• 🌍 Regional and segment-level performance.
• 📉 Impact of discounts on margins and profit.
• 🧩 ABC classification and profitability cohorts.

🧠 Key Insights

• The Consumer segment accounts for ~50% of total sales but lower profit margins.
• The Corporate segment is the most profitable overall.
• Furniture category has the lowest profit-to-sales ratio due to high shipping costs.
• Regions West and East outperform others in both revenue and profit.
• Discounts above 30% consistently destroy profit margins.

💼 Business Relevance

This analysis can help retail and e-commerce managers:

• Identify the most profitable customer segments and regions.
• Optimize discount strategies to avoid margin erosion.
• Focus marketing campaigns on high-value customers.

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