Superstore Sales Analysis Using Power BI

1. Project Overview This project focuses on analyzing Superstore Sales Data to understand customer behavior, sales trends, and shipping performance. The goal was to identify key patterns and optimize business decisions. 2. Data Preparation ️ Uploaded and cleaned the dataset, ensuring accurate data types. ️ Used DAX Measures to calculate total sales, order count, repeated customer rate, and average order value. ️ Created interactive dashboards to visualize insights across different business aspects. 3. Key Insights: Total Sales: $2.26M across 4,922 orders Repeated Customers Rate: 15.6% – opportunity to enhance retention strategies. Avg Order Value (AOV): $459.48 per order. Best-selling category: Technology (36.95%) Top state in sales: California ($446.31K) Most used shipping mode: Standard Class (64.89%) Avg Shipping Time: 4 days, with furniture taking the longest. 4-Recommendations: Implement loyalty programs to increase repeated customer rate. Optimize shipping strategies for furniture to reduce delivery time. Focus on underperforming regions to drive sales growth. Improve marketing campaigns for high-value customers to boost revenue