Bank Customer Churn Analysis

Introduction

Customer churn is a major challenge for banks, affecting revenue and increasing customer acquisition costs. Understanding why customers leave and what factors contribute to churn can help banks implement effective retention strategies. In this project, I analyzed customer churn data from a fictional bank to uncover key insights and recommend data-driven solutions for improving customer retention.

Problem Statement

High customer churn can indicate dissatisfaction, unmet expectations, or poor customer engagement. The goal of this analysis is to identify key drivers of churn and provide actionable recommendations for customer retention. Specifically, we aim to answer the following questions:

1. What customer demographics and behaviors are most associated with churn in a bank?
2. Does product engagement (number of bank products owned) influence churn rates?
3. Are tenure and credit score significant indicators of churn in banking?

By answering these questions, we can provide insights into how banks can improve customer loyalty and reduce churn.

About the Dataset

The dataset used for this analysis contains information about bank customers, including demographic details, account activity, and churn status. Below are the key fields: