This project analyzes retail sales data to identify key revenue drivers, evaluate regional and product performance, and uncover opportunities to improve profitability and sales efficiency.
The objective is to go beyond basic reporting and provide actionable insights for business decision-making.
- SQL Server (Data analysis)
- Power BI (Dashboard visualization)
- Excel (Dataset)
- Which regions and states contribute most to revenue?
- Are high-sales products also the most profitable?
- Which product categories and subcategories underperform?
- How does sales performance vary across customer segments?
- A small number of states contribute a significant portion of total revenue, indicating regional concentration
- Certain high-sales subcategories do not generate proportional profit, suggesting inefficiencies
- Some subcategories consistently underperform, indicating potential for removal or repositioning
- Sales distribution across customer segments is uneven, highlighting opportunities for targeted marketing
- Focus on high-performing regions to maximize revenue growth
- Re-evaluate pricing or cost structure of high-sales but low-profit products
- Improve or discontinue consistently underperforming subcategories
- Develop targeted strategies for underperforming customer segments
- KPI Cards (Total Sales, Orders, Profit)
- Sales trends over time
- Regional performance (state-wise analysis)
- Product category and subcategory performance
- Segment-wise sales distribution
- Interactive filters for dynamic analysis
Superstore dataset containing retail sales transactions across regions, products, and customer segments.
Kesar Deaulkar