E-Commerce Sales Performance Analytics

Project Overview

This project demonstrates an end-to-end Data Analytics workflow for an e-commerce business: data preparation → KPI analysis → trend analysis → category/region/channel analysis → business insights → recommendations.

The project is designed as a simple, reproducible portfolio project and uses a synthetic dataset generated by the Python script. This avoids dependence on proprietary data and makes the project runnable without a separate download.

Business Objectives

• Measure overall revenue and profitability.
• Track monthly sales performance.
• Identify high-performing product categories.
• Compare regional profitability.
• Compare sales channels.
• Convert analysis into practical business recommendations.

Key KPIs

• Total Revenue
• Total Profit
• Total Orders
• Average Order Value (AOV)
• Profit Margin

Tools & Technologies

• Python
• Pandas
• NumPy
• Matplotlib
• Jupyter Notebook / Google Colab / VS Code

Project Structure

E-Commerce-Sales-Performance-Analytics/
│
├── ecommerce_sales_analytics.py
├── requirements.txt
├── README.md
├── Project_Report.pdf
└── ecommerce_sales_data.csv

How to Run

1. Install Python 3.10+.
2. Install dependencies:

pip install -r requirements.txt

3. Run:

python ecommerce_sales_analytics.py

4. The script creates an outputs folder containing the cleaned data, summary tables, and charts.

Dataset

The dataset is synthetically generated inside the Python script using a fixed random seed for reproducibility. It contains 1,200 e-commerce transactions across categories, regions, sales channels, products, quantities, prices, discounts, revenue and profit.

Because the data is generated by the project itself, there is no external dataset download or proprietary customer information.

Key Findings from the Project Run

• Total Revenue: ₹7,328,061.58
• Total Profit: ₹2,070,172.88
• Orders: 1,200
• Average Order Value: ₹6,106.72
• Profit Margin: 28.25%
• Revenue is analyzed monthly to identify seasonal movement.
• Category, region and channel summaries are used to identify business opportunities.

Business Recommendations

1. Monitor monthly revenue trends and investigate sudden drops.
2. Maintain inventory and marketing focus on high-revenue categories.
3. Review regional profit performance before allocating promotional budgets.
4. Compare channel-level revenue and order contribution to improve digital sales strategy.
5. Monitor discount levels because discounts directly affect realized revenue.

Deliverables

• Python code file
• requirements.txt
• Project report
• README
• GitHub repository

Note

This project is intended for academic/internship portfolio submission and demonstrates the analytics workflow rather than representing real company financial data.