This project analyzes retail sales transactions to uncover product performance, regional sales trends, and customer purchasing behavior.
Using Excel, SQL Server, and Power BI, I transformed raw sales data into actionable business insights and developed an interactive dashboard for decision-making.
This project analyzes retail sales data using Excel, SQL Server, and Power BI.
The objective was to identify sales trends, top-performing products, regional performance, and customer purchasing patterns.
The project demonstrates the complete data analysis process from data exploration to dashboard development.
- Microsoft Excel
- SQL Server
- Power BI
The dataset contains retail sales transaction records with the following fields:
| Column |
|---|
| OrderID |
| Customer_ID |
| Product |
| Region |
| OrderDate |
| Quantity |
| UnitPrice |
A calculated revenue field was created:
TotalSales = Quantity × UnitPrice
- Total Sales by Product
- Sales by Region
- Monthly Sales Trend
- Total Revenue
- Total Orders
- Average Revenue
- Total Sales per Product
- Number of Transactions by Region
- Highest Selling Product
- Revenue by Product
- Monthly Sales Analysis
- Top 5 Customers
- Regional Revenue Contribution
Located in:
Dataset/
Located in:
SQL Queries/
Located in:
Power BI File/
- Certain products generated significantly higher revenue than others.
- Revenue performance varied across regions.
- Monthly sales trends highlighted periods of stronger business performance.
- Customer purchasing patterns helped identify high-value customers.
-
Microsoft Excel
- Pivot Tables
- Data Cleaning
- Revenue Analysis
-
SQL Server
- Aggregations
- GROUP BY
- Sorting and Filtering
- Business Query Development
-
Power BI
- DAX Measures
- KPI Cards
- Interactive Dashboards
- Data Visualization
-
Business Analysis
- Sales Performance Analysis
- Customer Analysis
- Regional Performance Analysis
Tolulope Glory Awoyemi
Data Analyst | Power BI Developer | Business Intelligence Analyst
GitHub: https://github.com/Simpletee17