📊 Customer Churn Analysis using MySQL

📌 Project Overview

Customer churn is one of the biggest challenges faced by subscription-based businesses. This project analyzes a telecom customer churn dataset using MySQL to identify customer segments with high churn, understand the factors influencing customer attrition, estimate revenue loss, and provide actionable business recommendations.

The project demonstrates SQL skills through exploratory data analysis, customer segmentation, and business insight generation.

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🎯 Objectives

• Analyze customer churn patterns
• Identify high-risk customer segments
• Measure churn across different customer categories
• Estimate revenue loss caused by churn
• Generate business recommendations for customer retention

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🗂 Dataset Information

• Dataset: Telecom Customer Churn Dataset
• Rows: 7,043
• Columns: 21

Features include:

• Customer Demographics
• Contract Type
• Internet Service
• Payment Method
• Monthly Charges
• Total Charges
• Customer Tenure
• Churn Status

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🛠️ Technologies Used

• MySQL
• MySQL Workbench
• SQL

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📚 SQL Concepts Demonstrated

• SELECT
• WHERE
• GROUP BY
• ORDER BY
• HAVING
• CASE WHEN
• Aggregate Functions
  • COUNT()
  • SUM()
  • AVG()
  • ROUND()
• Data Segmentation
• Business Analysis

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📁 Project Structure

Customer-Churn-Analysis-SQL

│
├── dataset/
│     customer_churn.csv
│
├── sql/
│     01_database_setup.sql
│     02_data_cleaning.sql
│     03_exploratory_analysis.sql
│     04_business_insights.sql
│
├── screenshots/
│
├── README.md
│
└── LICENSE

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📈 Business Questions Solved

✔ Which contract type has the highest churn?

✔ Which payment method experiences the highest churn?

✔ Do senior citizens churn more frequently?

✔ Which internet service has the highest churn rate?

✔ Does Tech Support reduce customer churn?

✔ Which tenure group is most likely to churn?

✔ Do higher monthly charges increase churn?

✔ Which customer segment generates the highest revenue loss?

✔ Which payment method contributes the highest revenue loss?

✔ Which customer profile represents the highest churn risk?

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📊 Key Insights

• Month-to-Month contracts showed the highest churn rate.
• Customers using Electronic Check experienced the highest churn.
• Fiber Optic users had a significantly higher churn rate than other internet services.
• Customers without Tech Support churned considerably more.
• Higher Monthly Charges were associated with higher customer churn.
• Senior Citizens had a higher churn rate than non-senior customers.
• Electronic Check customers contributed the greatest revenue loss.

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💡 Business Recommendations

• Promote long-term contracts using loyalty discounts.
• Improve customer support for Fiber Optic users.
• Encourage customers to subscribe to Tech Support.
• Review pricing strategies for customers with high monthly charges.
• Investigate the causes of high churn among Electronic Check users.
• Design retention campaigns targeting high-risk customer segments.

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🚀 Future Improvements

• Build an interactive Power BI Dashboard.
• Develop churn prediction models using Machine Learning.
• Perform customer lifetime value (CLV) analysis.
• Create automated SQL reports using Stored Procedures.

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👨‍💻 Author

Sarthak Rupnar

Aspiring Data Analyst | SQL | Python | Excel | Power BI (Learning)

GitHub: https://github.com/SarthakRupnar