An end-to-end retail sales analytics project using MySQL, Excel, and Power BI to analyze sales, profit, customers, products, regions, segments, and business performance.
Which regions, segments, and product categories should this retailer prioritize for growth — and where is profitability being quietly eroded by discounting? This project uses SQL, Excel, and Power BI to turn ~10,194 raw transaction records into a clear answer.
This project analyzes a Superstore-style retail dataset containing approximately 10,194 orders.
The objective is to transform raw retail transaction data into meaningful business insights using SQL, Excel, and Power BI.
The analysis focuses on:
- Sales and profit performance
- Category and sub-category performance
- Regional performance
- Customer segmentation
- Monthly sales and profit trends
- Product performance
- Discount impact
- Business recommendations
| Tool | Purpose |
|---|---|
| MySQL | Data loading, cleaning, querying and analysis |
| Excel | Business analysis and supporting calculations |
| Power BI | Interactive dashboard and visualization |
| GitHub | Project documentation and version control |
Retail-Sales-Analytics/
│
├── Dataset/
│ └── samplesuperstore.csv
│
├── Excel/
│ └── Retail_Sales_Business_Analysis.xlsx
│
├── PowerBI/
│ └── Retail_Sales_Analytics_Dashboard.pbix
│
├── SQL/
│ └── retail_sales_analysis.sql
│
├── Screenshots/
│ ├── SQL1.png
│ ├── SQL2.png
│ ├── SQL3.png
│ ├── SQL4.png
│ ├── business-insights.png
│ ├── dashboard.png
│ ├── excel-business-analysis1.png
│ └── excel-business-analysis2.png
│
└── README.md
The MySQL analysis covers Beginner, Intermediate, and Advanced SQL concepts.
- SELECT, WHERE, ORDER BY
- GROUP BY and aggregate functions
- Subqueries
- CASE statements
- Common Table Expressions (CTEs)
- Window functions
- Running totals
- Month-over-month growth
- Product profitability ranking
- Customer segmentation
- SQL Views
The complete SQL analysis is available here:
SQL/retail_sales_analysis.sql
Excel was used to perform business analysis and prepare supporting calculations.
- KPI analysis
- Category performance
- Regional performance
- Segment performance
- Profitability analysis
- Monthly sales and profit trends
- Product performance
- Discount impact
- Business recommendations
The Power BI dashboard provides an interactive view of retail sales performance.
- Total Sales
- Total Profit
- Order Count
- Profit Margin
- YTD Sales
- Sales by Category
- Sales by Region
- Monthly Sales Trend
- Sales by Segment
- Top 5 Sub-Categories by Sales
- Interactive filters
Power BI file:
PowerBI/Retail_Sales_Analytics_Dashboard.pbix
- Technology generated the highest sales and strong profitability.
- Furniture generated substantial sales but had a significantly lower profit margin.
- West generated the highest regional sales and profit.
- Central showed the weakest regional profit margin.
- Consumer generated the highest sales and total profit.
- Home Office had the highest profit margin among the segments.
- Furniture had approximately a 3% profit margin, compared with around 17% for Technology and Office Supplies.
- Orders discounted at 50% or above generated a combined loss of approximately $77K across 874 orders — a negative contribution of about 26% relative to total profit.
- Binders, Chairs, Phones, Storage, and Tables were among the leading sub-categories by sales.
- Review Furniture pricing and product costs to improve profitability.
- Reduce excessive discounting and establish appropriate discount limits.
- Maintain focus on the high-performing Consumer segment.
- Explore opportunities to grow the higher-margin Home Office segment.
- Improve profitability in the Central region.
- Prioritize inventory for high-performing sub-categories.
- Use historical sales trends for inventory and promotional planning.
👉 View Live Power BI Dashboard
Open:
SQL/retail_sales_analysis.sql
Load the dataset from:
Dataset/samplesuperstore.csv
Then execute the SQL queries in MySQL Workbench.
Open:
Excel/Retail_Sales_Business_Analysis.xlsx
Open:
PowerBI/Retail_Sales_Analytics_Dashboard.pbix
The project uses a Superstore-style retail dataset containing approximately 10,194 orders.
The dataset includes information about:
- Orders
- Customers
- Products
- Categories
- Regions
- Sales
- Quantity
- Discounts
- Profit
The raw dataset is included in the Dataset/ folder for reproducibility.
Orlotu Sirisha
Retail Sales Analytics Project