📊 Kultra Mega Stores – SQL Business Case Study

This project analyzes customer, sales, and shipping data from Kultra Mega Stores (KMS) between 2009–2012.
As a Business Intelligence Analyst, I was tasked with delivering insights to support strategic decisions for the Abuja division.

🎯 Project Objective

To extract meaningful business insights from transactional order data using SQL, with the goal of identifying opportunities to increase revenue, optimize operations, and strengthen customer engagement.

🛠 Tools Used

• Microsoft SQL Server
• Excel (for data cleaning)
• Microsoft Word (for insight documentation)

📁 Project Files

• KMS Insight Report – Full Word report with SQL queries and business insights
• Kultra Mega Business Analysis.sql – SQL scripts used for the analysis
• KMS SQL Analysis Images – Images of result outputs from SQL Server
• Data used for the project (KMS) – The raw dataset used for this analysis

🔑 Key Insights (Summary)

• Technology led all product categories in sales, highlighting strong customer demand.
• West, Ontario, and Prairie were top-performing regions, while Yukon, Northwest Territories, and Nunavut underperformed.
• Appliance sales in Ontario reached ₦202,347, highlighting regional demand.
• Bottom 10 customers had very low purchase values, signaling low engagement or one-time buyers.
• Delivery Truck had the highest shipping cost overall, despite not always being urgent.
• Top customers like Emily Phan and Deborah Brumfield focused mainly on Technology and Office Supplies.
• Dennis Kane was the top small business customer with ₦75,968 in sales.
• Most active corporate clients were Adam Hart, Bill Donatelli, and Justin Knight with 19 orders each between 2009-2012.
• Emily Phan stood out as the most profitable consumer customer.
• Returns were mostly linked to Corporate and Small Business segments, with certain customers returning multiple items.
• High-cost shipping methods were sometimes used for low-priority orders, indicating misalignment in logistics.

📌 Project Outcome Summary

The analysis uncovered significant sales performance patterns, identified high-value customers, and highlighted inefficiencies in shipping and returns. It revealed that Technology products are market leaders, some regions and customers underperform, and operational costs (like shipping) are not always aligned with business priorities. These findings provide a clear foundation for strategic decision-making.

✅ Recommendation Summary

KMS should continue investing in high-performing categories and prioritize loyal, high-value customers through tailored engagement and rewards. Underperforming regions and low-engagement customers require targeted marketing or reactivation strategies. Operationally, shipping methods should be aligned with order urgency, and product returns should be closely monitored to reduce losses and improve service quality.

👤 Author

Adedokun Elizabeth
Business Intelligence Analyst 📧 [email protected]