Customer Retention & Churn Analysis

🔍 Project Overview

In many subscription-based businesses, retaining existing customers is as important as acquiring new ones. Customer churn directly impacts revenue and long-term growth. This project focuses on analyzing customer data from a telecommunications provider to identify patterns that lead to churn and provide actionable insights to improve retention.

This task was completed as part of the Future Interns Data Science & Analytics Internship (Task 2 - 2026).

🎯 Objectives

The primary goal is to help business teams answer:

• Why are customers leaving the platform?
• Which customer segments are most likely to churn?
• How long do customers typically stay active?
• What actions can improve customer retention?

📊 Dataset Description

The analysis was performed on the Telco Customer Churn Dataset, which contains:

• Customer Demographics: Gender, Seniority, Partners, Dependents.
• Service Information: Phone service, Multiple lines, Internet service (DSL, Fiber optic), Online security, Tech support, Streaming TV/Movies.
• Account details: Tenure, Contract type, Payment method, Paperless billing, Monthly charges, Total charges.
• Churn status: Whether the customer left within the last month.

🛠️ Key Steps Involved

1. Data Cleaning: Handled missing values (specifically in TotalCharges), converted data types, and prepared the target variable for analysis.
2. Exploratory Data Analysis (EDA): Investigated the distribution of churn across various demographics and services.
3. Visualization: Used Matplotlib and Seaborn to create intuitive charts showing correlations and drivers of churn.
4. Insight Generation: Derived business-focused conclusions from the data patterns.

📈 Analysis & Insights

1. Churn Distribution

The overall churn rate in the dataset is approximately 26.5%. Understanding the baseline helps in measuring the effectiveness of future retention strategies.

2. Contract Type and Retention

Customers on Month-to-month contracts are significantly more likely to churn compared to those on one or two-year contracts.

• Actionable Insight: Offering discounts or loyalty rewards for moving to longer-term contracts can drastically reduce churn.

3. Internet Service Impact

Interestingly, customers using Fiber Optic service have a higher churn rate compared to DSL users. This suggests possible issues with service reliability or price sensitivity in this segment.

• Actionable Insight: Targeted feedback surveys should be sent to fiber optic users to understand service pain points.

4. Financial Drivers (Monthly Charges)

Churned customers tend to have higher median monthly charges. Pricing pressure is a clear indicator of customer departure.

• Actionable Insight: Periodic "Loyalty Discounts" or bundle optimization for high-paying customers could prevent exit.

5. Tenure and Lifetime

Customers with shorter tenure (especially in the first 6 months) represent the highest churn risk. Once a customer stays past 2 years, retention rates improve significantly.

• Actionable Insight: Onboarding programs and "First 90 Days" engagement campaigns are crucial.

💡 Business Recommendations

Based on the analysis, the following strategies are recommended:

1. Contract Incentives: Transition month-to-month customers to annual plans through promotional offers.
2. Onboarding Focus: Create high-touch engagement for new customers in their initial months.
3. Service Quality Review: Audit Fiber Optic service performance as it shows unexpectedly high churn.
4. Value-Add Services: Customers with "Tech Support" and "Online Security" were found to stay longer. Bundling these services can improve stickiness.

🚀 How to Run

1. Clone the repository.
2. Ensure you have pandas, seaborn, and matplotlib installed.
3. Open churn_analysis.ipynb in Jupyter Notebook or VS Code to see the full code and detailed analysis.

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Created by Aryan - Future Interns Internship 2026