This project presents an interactive Sales SuperMarket Analysis Dashboard developed using Microsoft Power BI. The dashboard provides a comprehensive view of supermarket sales performance through interactive visualizations, KPI tracking, regional analysis, customer segmentation, and time-series forecasting.
The objective of the dashboard is to transform raw retail sales data into meaningful business insights that support data-driven decision-making.
The primary objective of this project is to contribute to business success by utilizing data analysis techniques, specifically time-series analysis, to deliver valuable insights and accurate 15-day sales forecasting.
The dashboard focuses on four major areas:
- 📊 Dashboard Creation
- 📈 Data Analysis
- 🔮 Sales Forecasting
- 💡 Actionable Business Recommendations
- Executive KPI Dashboard
- Interactive Dashboard Navigation
- Dynamic Filters & Slicers
- Regional Sales Analysis
- Customer Segment Analysis
- Payment Mode Analysis
- Shipping Mode Analysis
- Category & Sub-Category Analysis
- Monthly Sales Trend
- Monthly Profit Trend
- State-wise Sales Analysis
- 15-Day Sales Forecast
- Microsoft Power BI Desktop
- Power Query
- DAX (Data Analysis Expressions)
- Data Modeling
- Time-Series Forecasting
- Interactive Dashboard Design
| KPI | Value |
|---|---|
| 💰 Total Sales | 1.57M |
| 💵 Total Profit | 175.26K |
| 📦 Total Quantity | 22K |
| 🚚 Average Delivery Time | 4 Days |
- Total Sales
- Total Profit
- Total Quantity
- Average Delivery Time
- Sales by Payment Mode
- Sales by Region
- Sales by Customer Segment
- Monthly Sales Trend
- Monthly Profit Trend
- Sales by Ship Mode
- Sales by Category
- Sales by Sub-Category
- State-wise Sales Analysis
- Historical Daily Sales
- 15-Day Sales Forecast
- Forecast Confidence Interval
- State-wise Sales Distribution
- Generated 1.57 Million in total sales.
- Earned 175.26K in total profit.
- Successfully sold over 22K products.
- Maintained an average delivery time of 4 days, demonstrating efficient logistics.
- The West Region recorded the highest sales.
- The East Region emerged as the second-best performing region.
- The Central and South regions contributed comparatively lower revenue.
Focus marketing campaigns and inventory expansion in underperforming regions to improve overall revenue.
- The Consumer segment contributes nearly half of total revenue.
- Corporate customers represent the second-largest customer group.
- Home Office customers contribute the smallest share of sales.
Introduce loyalty programs for Consumer customers and volume discounts for Corporate clients.
- Cash on Delivery (COD) is the most preferred payment method.
- Online Payments contribute a significant portion of total sales.
- Card Payments account for the lowest sales contribution.
Promote digital payment methods using cashback offers and exclusive online discounts.
- Sales fluctuate throughout the year.
- Significant growth is observed during the final quarter, indicating seasonal demand.
Increase inventory and promotional activities before peak shopping seasons.
- Profit follows a trend similar to sales.
- Higher profitability is observed during the final months of the year.
Focus promotional campaigns on high-margin products during peak periods.
- Standard Class is the most frequently used shipping method.
- Second Class is the second preferred option.
- Same Day shipping has the lowest usage.
Encourage premium shipping adoption through promotional pricing.
- Office Supplies generated the highest sales.
- Technology ranked second.
- Furniture contributed the lowest revenue among the three categories.
Increase promotional efforts for Furniture products while maintaining inventory for high-performing categories.
- Phones generated the highest sales.
- Chairs and Binders are among the strongest contributors.
Maintain adequate stock levels for high-demand products and introduce bundle offers to increase average order value.
- California recorded the highest sales.
- Sales are concentrated in a few major states, indicating potential growth opportunities elsewhere.
Launch region-specific marketing campaigns to improve sales in underperforming states.
The Forecast Dashboard utilizes Power BI's built-in Time-Series Forecasting capability to predict sales for the next 15 days based on historical daily sales data.
- Historical Sales Trend
- Forecasted Sales
- Confidence Interval
- Daily Sales Projection
- Inventory Planning
- Demand Forecasting
- Revenue Estimation
- Business Decision Support
PowerBI-Sales-SuperMarket-Analysis/
│
├── Images/
│ ├── Overview_Dashboard.png
│ └── Forecast_Dashboard.png
│
├── Dataset/
│ └── SuperStore.csv
│
├── Documentation/
│ ├── Business_Insights.md
│ └── Forecast_Methodology.md
│
├── README.md
├── LICENSE
└── .gitignore
- Power BI Dashboard Development
- Business Intelligence
- Data Visualization
- KPI Dashboard Design
- DAX Calculations
- Power Query
- Data Modeling
- Time-Series Forecasting
- Retail Sales Analytics
- Business Analysis
- Interactive Reporting
- Decision Support Systems
- Customer Profitability Dashboard
- Product-Level Sales Forecasting
- Inventory Management Dashboard
- Target vs Actual Sales Dashboard
- Customer Churn Analysis
- Advanced DAX Measures
- Row-Level Security (RLS)
- Power BI Service Deployment
- Automated Data Refresh
The dashboard is built using a retail supermarket sales dataset containing:
- Orders
- Sales
- Profit
- Customers
- Products
- Categories
- Sub-Categories
- Regions
- States
- Shipping Information
- Payment Modes
Vishnu G
Aspiring Data Analyst | Business Intelligence Developer
- GitHub: https://github.com/Vishnug21
- LinkedIn: https://www.linkedin.com/in/vishnugprof/