This project analyzes a fictional bookstore's operational data using SQL. We explored three core datasets — Books, Customers, and Orders — to extract insights into sales performance, customer behavior, and stock management. The goal is to support data-driven strategies for improving profitability and customer satisfaction.
🟦 Objective
Identify top-selling books and most profitable genres
Analyze customer purchase behavior across cities and countries
Detect inventory gaps and low-stock items
Understand revenue trends over time
Highlight customers with high purchase values or frequent orders
🟦 Methodology
1.Cleaned and loaded data from three CSV files
2.Established relationships via Customer_ID and Book_ID
Wrote SQL queries to:
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Join tables
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Aggregate revenue
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Filter top customers
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Identify underperforming genres and stock shortages
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Derived actionable insights from query outputs
🟦 Key Insights
Examples (you can customize based on actual query results):
Fiction and Thriller genres contributed 60%+ of total revenue
Top 5 customers generated 25% of sales
15 books had zero sales — potential for markdown or promotion
Multiple customers from tier-2 cities showed high engagement
Several books have fewer than 5 units left in stock
🟦 Recommendations
Restock high-performing books in Fiction & Thriller categories
Promote unsold books through bundled offers or discounts
Target high-spending customers with loyalty programs
Expand marketing to cities showing strong growth
Optimize stock management by tracking reorder thresholds
🟦 Conclusion
SQL-based analysis of bookstore data provided actionable insights into sales trends, customer behavior, and inventory gaps. These findings can help management enhance operational efficiency and boost revenue.