E-Commerce Sales Analytics

Overview

This project analyzes a fictional e-commerce dataset to understand customer behavior, sales performance, and product trends.

Dataset

• Orders
• Customers
• Products
• Returns

Tools Used

• SQL
• Python (Pandas)
• Tableau

Key Analysis

1. Sales Performance

• Total revenue and order trends analyzed
• Seasonal patterns identified

2. Customer Analysis

• RFM (Recency, Frequency, Monetary) analysis performed
• Customer segmentation using K-Means
• ~49% customers identified as churned

3. Product Insights

• Clothing category drives highest revenue
• Electronics shows higher return rates

4. Returns Analysis

• Return rate varies by category
• Key categories contributing to returns identified

Dashboard

Includes:

• KPI metrics (Revenue, Orders, Customers, AOV)
• Sales trend
• Revenue by category and city
• Top customers
• Return rate

Key Insights

• Loyal customers contribute majority of revenue
• Significant churn observed in customer base
• Certain categories have higher return risk
• Sales peak observed in Q4

Note

This project uses a synthetic dataset to demonstrate real-world analytics workflow including SQL analysis, Python modeling, and dashboarding.

Conclusion

This project demonstrates ability to analyze transactional data, extract business insights, and build dashboards for decision-making.