SuperStore_Sales_Project

📊 Overview

This project analyzes retail sales data to uncover key business insights and improve decision-making using an interactive Power BI dashboard.

📁 Dataset

The dataset includes:

• Order details (Order Date, Ship Mode)
• Customer information (Segment, Region)
• Product details (Category, Sub-category)
• Sales, Profit, Discount, Quantity

🧹 Data Cleaning

• Removed duplicates and handled missing values
• Standardized formats (dates, categories)
• Created calculated fields (KPIs, profit metrics)
• Prepared data for analysis and visualization

🔍 Key Insights

• Discounts above 20% reduce profit by 30–40%
• Technology category contributes ~35–40% of total revenue
• West region generates ~30–35% of total profit
• Q4 contributes ~38% of annual sales (seasonal peak)

📈 Dashboard Features

• KPI Cards (Sales, Profit, Orders)
• Sales trends over time
• Category and region analysis
• Discount vs Profit insights
• Interactive filters (Date, Category, Region)

🛠 Tools & Technologies

• Excel (Data Cleaning)
• Power BI (Dashboard & Visualization)
• DAX (KPIs & Calculations)

🎯 Business Value

• Identified key profit drivers and loss areas
• Highlighted impact of discounting on profitability
• Provided insights to support data-driven decisions

📘 Detailed Case Study

For a complete breakdown (business problem, recommendations, challenges), refer to the full project documentation.