customer_behavior_analysis

An end-to-end analytics project combining SQL, Python, and Power BI to uncover customer insights, perform segmentation, and deliver a business-focused dashboard.

📌 Project Overview

This project simulates a real-world data analytics workflow, transforming raw transaction data into actionable business insights.

Using Python, SQL Server, and Power BI, I analyzed ~3,900 customer transactions to understand:

• Customer behavior and spending patterns
• Product performance across categories
• Opportunities to improve retention and revenue

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⚙️ Tools & Technologies

• Python (Pandas) - data cleaning & feature engineering
• SQL Server (T-SQL) - data storage & business analysis
• JupyterLab - interactive analysis
• Power BI - dashboard & data visualization

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🔄 Project Workflow

1. Data Preparation (Python)

   • Cleaned missing values and standardized dataset
   • Created new features such as age_group and purchase frequency

2. Data Analysis (SQL)

   • Loaded data into SQL Server
   • Answered key business questions using:
     • CASE statements
     • CTEs
     • Window functions

3. Visualization (Power BI)

   • Built an interactive dashboard
   • Enabled filtering by category, subscription status, and shipping type

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📊 Key Insights

• 💰 Revenue Distribution
  Male customers generate 68% of total revenue, while female customers have slightly higher average spend per transaction

• 🎯 High-Value Customers
  Customers using discounts still spend above average → not purely price-sensitive

• ⭐ Top Performing Products
  Accessories and footwear categories lead in customer satisfaction

• 🔁 Customer Loyalty
  ~80% of customers are repeat buyers → strong retention but limited new acquisition

• 🚀 Business Opportunity
  Over 2,500 repeat customers are not subscribed, representing a major conversion opportunity

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📈 Dashboard Preview