📊 Retail Superstore Analysis

🔍 Project Overview

Comprehensive retail sales data analysis to:

• Identify growth opportunities
• Optimize logistics operations
• Segment customers

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🗃️ Data Source

Superstore Sales Dataset from Kaggle
🔗 Link to data
📆 Period: 2015–2018
📝 12,994 records - 18 columns

🔄 Analysis Process

1. Data Preparation

• Date format conversion (DD/MM/YYYY → SQL DATE)
• Data quality validation
• Anomaly detection (inconsistent dates, missing values)

2. Core Analysis

• Sales by category: Identify top performers
• Monthly trends: Month-over-month growth
• Geographical analysis: Performance by region/state

3. Advanced Analysis

• RFM segmentation of customers
• Logistics benchmark: Delivery times by shipping mode

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💡 Key Insights

📌 Sales Performance

Category	% of Total Revenue	Recommendation
Furniture	48.27%	Expand assortment
Office Supplies	29.09%	Maintain
Technology	22.64%	Targeted promotions

🚚 Logistics

• 100% of orders delivered on time
• Average delivery time: 5 days (Standard Class)

📈 Growth

• Growth peak: June 2015 (+6592%)
• Strong seasonal trend in Q2

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📊 Power BI Dashboard

Key Features:

• Global KPI overview
• Customer RFM analysis
• Product segment details

📌 Strategic Recommendations

• Expand furniture offering (top category)
• Leverage Q2 seasonality
• Maintain logistics excellence
• Target high-potential customers (RFM segmentation)

📂 Project Files

• superstore_analysis.sql – Full SQL scripts
• superstore_dashboard.pbix – Power BI file
• superstore.csv - csv file