Retail Sales Analysis & Dashboard

Overview

This project analyzes a retail sales dataset to identify key revenue drivers, product performance trends, and customer behavior patterns using SQL and Excel.

---

Objectives

• Identify top-performing product categories by revenue
• Compare sales volume versus revenue contribution
• Evaluate customer purchasing behavior

---

Tools Used

• SQL (MySQL) – data validation, aggregation
• Microsoft Excel – PivotTables, dashboard

---

Data Preparation

• Imported dataset into MySQL
• Resolved date format issue (MM/DD/YYYY → YYYY-MM-DD)
• Validated totals and data consistency

---

Key SQL Queries

Revenue by category:

SELECT Product_Category, SUM(Total_Amount) AS revenue
FROM retail_sales
GROUP BY Product_Category
ORDER BY revenue DESC;

Sales volume by category:

SELECT Product_Category, SUM(Quantity) AS total_quantity
FROM retail_sales
GROUP BY Product_Category
ORDER BY total_quantity DESC;

Age-based revenue segmentation:

SELECT 
    CASE 
        WHEN Age < 25 THEN 'Under 25'
        WHEN Age BETWEEN 25 AND 40 THEN '25-40'
        WHEN Age BETWEEN 41 AND 60 THEN '41-60'
        ELSE '60+'
    END AS age_group,
    SUM(Total_Amount) AS revenue
FROM retail_sales
GROUP BY age_group
ORDER BY revenue DESC;

---

Dashboard

---

Key Insights

• Electronics generates the highest revenue despite lower sales volume, indicating higher unit pricing
• Clothing has the highest quantity sold but lower revenue per unit
• Customer purchases are evenly distributed across gender, indicating no strong demographic bias
• Younger customers (18–27) generate the highest revenue, while older customers (48–57) show higher transaction frequency, indicating differences in spending behavior

---

Recommendations

• Focus on high-value categories such as Electronics to drive revenue growth
• Optimize pricing or bundling strategies in Clothing to improve margins
• Target younger customers for high-value sales opportunities
• Leverage frequent buyers in older segments through retention strategies

---

Outcome

Developed a structured analysis using SQL and Excel to transform raw sales data into actionable insights and a clear, executive-level dashboard.