Bank-customer-churn-analysis

Customer Churn Analysis Report

Introduction

Customer churn is a critical issue for financial institutions, impacting revenue and long-term business sustainability.
This project aims to identify factors leading to customer churn based on various attributes:

credit_score country gender age
tenure balance products_number credit_card
active_member estimated_salary churn
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Objectives

What are the key factors influencing customer churn?
How does customer churn vary across countries?
Which customer segments are most at risk of churning?
What is the accuracy of the predictive model?

Technical Tools

MySQL
R Programming

Data Preprocessing and Manipulation

The data was imported into MySQL for cleaning. There were no missing values or duplicates. The column Customer_Id was dropped to optimize the dataset for analysis.

Using SQL, the distribution of customer churn was analyzed, revealing the following:

Churned Customers: 20.4%
Non-Churned Customers: 79.6%

View SQL Code

The cleaned data was then imported into R for modeling.

Model Approach

Decision Tree Vs. Regression Tree

The Regression Tree outperformed the Decision Tree with an accuracy of 86.1%, offering more precise probability estimates for churn.
The Decision Tree provided interpretable rules but had a slightly lower accuracy of 83.6%.

Thus, the Regression Tree model was selected for deeper insights.

Key Findings

1. What are the key factors influencing customer churn?

Age: Customers over 50 are more likely to churn.
Active Membership: Inactive customers show a higher churn probability.
Number of Products: Customers with less than two products have higher churn risk.
Estimated Salary: Customers with lower estimated salaries tend to churn more.
Country: Customers from Germany exhibit a higher churn rate.

2. How does customer churn vary across countries?

Germany: Highest churn rate, especially among older customers with limited product holdings.
France & Spain: Lower churn rates, especially for customers with active memberships.

3. Which customer segments are most at risk of churning?

Customers over 50 years old
German customers with low salaries and inactive memberships
Individuals with only 1 or 2 products

Conclusion & Recommendations

To reduce customer churn, the bank can implement the following strategies:

Target Inactive Customers – Launch engagement campaigns for inactive members.
Increase Product Adoption – Encourage single-product holders to adopt additional products.
Monitor High-Risk Customers – Develop retention programs for customers over 50 years old.
Customize Regional Strategies – Focus on Germany with personalized offers.
Salary-Based Incentives – Provide special incentives for customers with lower estimated salaries.