📊 Customer Churn Analysis & Business Insights

Using Python & SQL

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🔎 Project Overview

Customer churn is one of the biggest challenges for subscription-based businesses.
Losing customers directly impacts revenue, profitability, and long-term growth.

This project analyzes customer churn patterns using Python and SQL to:

• Identify high-risk customer segments
• Discover key churn drivers
• Generate actionable business insights
• Recommend retention strategies

The analysis simulates a real-world business scenario for telecom/subscription-based companies.

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🎯 Business Problem

Companies struggle to answer:

• Why are customers leaving?
• Which customer segments are most at risk?
• How can churn be reduced strategically?
• What business actions improve retention?

This project addresses these questions using structured data analysis.

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🛠 Tools & Technologies Used

• Python
  • Pandas (Data Cleaning & Manipulation)
  • NumPy
  • Matplotlib
  • Seaborn
• SQL
  • Aggregation Functions
  • CASE Statements
  • Group By Analysis
  • Window Functions
• Jupyter Notebook

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📁 Dataset Description

Dataset: Telco Customer Churn Dataset

The dataset includes:

• Customer demographics
• Contract details
• Billing information
• Service subscriptions
• Churn status (Yes/No)

Target Variable: Churn

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🧹 Data Cleaning & Preparation

The following preprocessing steps were performed:

• Converted TotalCharges from object to numeric
• Handled missing values
• Created tenure-based customer segments
• Validated data consistency

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📊 Exploratory Data Analysis (EDA)

Key analyses performed:

• Overall churn rate calculation
• Churn distribution visualization
• Contract type vs churn comparison
• Tenure vs churn behavior
• Monthly charges vs churn pattern
• Tenure group segmentation

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📈 Key Business Insights

• Month-to-month contract customers show significantly higher churn.
• Customers with low tenure (0–12 months) are most likely to churn.
• Higher monthly charges correlate with increased churn probability.
• Long-term contract customers demonstrate stronger retention behavior.
• Early-stage customers require stronger engagement strategies.

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💡 Business Recommendations

Based on analysis:

• Promote long-term contracts with loyalty incentives.
• Offer retention discounts for high monthly charge customers.
• Improve onboarding strategy for new customers.
• Design targeted campaigns for high-risk tenure segments.
• Implement proactive engagement during first 12 months.

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📷 Project Visualizations

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📌 Skills Demonstrated

• Data Cleaning & Preprocessing
• Exploratory Data Analysis
• Business Insight Generation
• SQL Aggregations & Conditional Logic
• Data Visualization
• Analytical Thinking
• Problem Solving

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🎓 Learning Outcome

This project demonstrates how structured data analysis can:

• Identify churn drivers
• Improve customer retention strategies
• Support data-driven decision-making

It reflects real-world business analytics workflow used by Data Analysts.

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👤 Author

Sunil Kumar Aspiring Data Analyst SQL | Power BI | Python | Business Analytics