🚀 Customer Behavior Analysis & Revenue Insights

📌 Business Problem

How can a retail business increase customer retention, optimize sales strategy, and maximize revenue using data-driven insights?

This project analyzes customer purchasing patterns to identify high-value customers, key revenue drivers, and actionable business strategies.

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🎯 Project Objectives

• Understand customer behavior and purchase trends
• Identify high-value and low-value customer segments
• Analyze sales performance across categories and regions
• Provide data-driven recommendations to improve retention and revenue

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🛠 Tech Stack

• Python → Data Cleaning, EDA, Feature Engineering
• SQL → Data Extraction, Transformation, Advanced Queries
• Power BI → Interactive Dashboard & Business Insights

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📂 Project Structure

data/ → Raw & cleaned datasets notebooks/ → Python analysis (EDA & visualization) sql/ → Business queries & insights powerbi/ → Dashboard file (.pbix) README.md → Project documentation

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🔍 Key Analysis Performed

🐍 Python (EDA & Data Processing)

• Data cleaning (handling nulls, duplicates)
• Exploratory Data Analysis (EDA)
• Customer purchase behavior analysis
• Correlation analysis & trend identification

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🗄 SQL (Business Insights)

• Customer segmentation (High / Medium / Low value)
• Revenue contribution analysis
• Category-wise performance
• Advanced queries using Window Functions

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📊 Power BI Dashboard

• KPI Metrics:

  • Total Revenue
  • Average Order Value
  • Customer Segmentation

• Interactive filters (Category, Region, Gender)

• Trend analysis (monthly/seasonal patterns)

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📈 Key Insights

• 💡 Top 20% customers contribute majority of revenue
• 💡 Certain product categories drive repeat purchases
• 💡 Seasonal trends significantly impact sales
• 💡 Customer segmentation helps target marketing effectively

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💼 Business Recommendations

• 🎯 Focus on high-value customers with loyalty programs
• 📢 Target high-performing categories in marketing campaigns
• 🔄 Improve customer retention strategies
• 📊 Use data-driven decision making for pricing & promotions

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⚡ Key Learnings

• End-to-end data analysis workflow
• Translating raw data into business insights
• Writing optimized SQL queries
• Building interactive dashboards

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🔗 Project Links

• GitHub Repository: https://github.com/apoorvds99/Customer-Trends-Data-Analysis-SQL-Python-PowerBI

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🙌 About Me

Aspiring Data Analyst skilled in SQL, Python, Power BI, and data-driven problem solving, passionate about turning data into actionable insights.