Turning retail data into meaningful insights and smarter business decisions.
This project presents an end-to-end analysis of retail sales data using Python and data visualization.
The analysis explores sales, profit, customer segments, regions, product categories, discounts, and sales trends to identify business opportunities, profitability challenges, and actionable recommendations.
The project follows a complete data analytics workflow:
Data Loading → Data Cleaning → Feature Engineering → EDA → Visualization → Business Insights → Recommendations
- Understand overall retail sales and profitability
- Identify the best-performing product categories
- Analyze regional sales and profit performance
- Compare customer segments
- Study yearly and monthly sales trends
- Analyze the relationship between discounts and profit
- Identify highly profitable and loss-making subcategories
- Generate practical business recommendations
| Metric | Value |
|---|---|
| 💰 Total Sales | $2,296,919.49 |
| 📊 Total Profit | $286,409.08 |
| 📦 Total Quantity Sold | 37,871 |
| 🧾 Original Records | 9,994 |
| 🧹 Cleaned Records | 9,993 |
Technology generated the highest sales and profit among the major product categories, making it an important growth area for the business.
Furniture generated strong sales but significantly lower profit compared with Technology and Office Supplies.
This indicates an opportunity to review pricing, discounts, product mix, and operational costs.
The West region generated the highest sales and profit.
Its performance can provide useful benchmarks for improving weaker regions.
The Consumer segment contributed the highest sales and profit among customer segments.
This makes customer retention and targeted marketing particularly important.
Sales increased from approximately $484K in 2019 to approximately $733K in 2022, demonstrating strong overall growth.
November recorded the highest monthly sales.
This creates an opportunity for advance inventory planning, marketing campaigns, and operational preparation.
Tables and Bookcases were among the weakest subcategories in terms of profitability.
These products require further investigation into pricing, discounts, shipping costs, and product economics.
Prioritize inventory and marketing for profitable Technology products while maintaining healthy margins.
Review Furniture pricing, discount levels, product mix, and fulfillment costs.
Analyze Tables and Bookcases to identify the causes of negative profitability.
Evaluate discounts based on their effect on profit, not sales volume alone.
Identify successful strategies in the West and explore opportunities to apply them to weaker regions.
Develop targeted promotions and loyalty strategies for the Consumer segment.
Increase inventory and marketing readiness before high-performing periods such as November.
- 🐍 Python
- 🐼 Pandas
- 📊 Matplotlib
- 📓 Jupyter Notebook
- 🔧 Git
- ☁️ GitHub
git clone https://github.com/Rahul-codehub/Retail-sales-analysis.git