Customer Retention & Churn Analysis

Project Overview

This project analyzes customer churn patterns in a telecom subscription-based business.

The objective is to identify key churn drivers, understand customer retention trends, and provide actionable recommendations to reduce customer loss.

Dataset

Telco Customer Churn dataset containing information about customer demographics, service subscriptions, payment methods, and churn status.

Key variables include:

* Customer tenure

* Contract type

* Payment method

* Internet service

* Monthly charges

* Customer churn status

Tools Used

* Python

* Pandas

* Seaborn

* Matplotlib

* Google Colab

Key Insights

* Month-to-month contracts show the highest churn rate.

* Customers with longer tenure are more likely to stay.

* Electronic check payment method has higher churn.

* Overall customer retention rate is ~73%.

* Overall churn rate is ~27%.

Business Recommendations

* Encourage customers to switch to yearly or long-term contracts.

* Promote automatic payment methods instead of electronic checks.

* Provide loyalty incentives for long-tenure customers.

* Improve customer experience for segments with high churn risk.

Project Structure

customer_churn_analysis.ipynb → Python analysis notebook

telco_churn.xlsx → Dataset

churn_distribution.png → Visualization

contract_churn.png → Visualization

payment_churn.png → Visualization

README.md → Project documentation