This project delivers a structured and data-driven Customer Churn Analysis for a retail bank using Power BI. It provides a clear understanding of customer behavior, identifies the segments most likely to leave, and equips decision-makers with insights needed to strengthen long-term customer retention strategies. 📊 Bank Customer Churn Analysis & Retention Strategy Using Power BI 📘 Project Overview
Customer churn is a critical challenge in the banking industry. This project provides a complete end-to-end churn analysis using Power BI, helping the bank identify:
Which customers are churning
Why they are leaving
What patterns exist across demographics, financial behavior, engagement, and product usage
What actions can be taken to reduce churn
The analysis is entirely based on interactive Power BI dashboards. No machine learning is used.
🎯 Business Objective
To understand churn drivers and support the bank with data-driven strategies to improve customer retention.
Key Questions:
What is the overall churn rate?
Who is most likely to churn?
What financial or behavioral patterns contribute to churn?
Which customer segments require the most attention?
What strategic actions can reduce churn?
📂 Dataset
The dataset contains 10,000 customers with attributes including:
Demographics: Age, Gender, Geography
Financial Data: Balance, Estimated Salary, Credit Score
Behavioral Data: Tenure, Active Membership, Credit Card
Product Usage: Number of products
Target Field: Exited (1 = churned, 0 = retained)
🛠 Tools & Technologies
Power BI (Dashboard creation)
Power Query (Data cleaning & transformations)
DAX (Measures & KPIs)
CSV Dataset
🔧 Data Preparation (Power Query)
The following steps were performed:
✔ Data Cleaning
Removed duplicates
Validated data types
Cleaned text fields
Checked for missing values
✔ Feature Engineering
Created new categorical columns:
Age Group
Salary Group
Credit Score Rating
Tenure Buckets
These support better segmentation in dashboard visuals.
📈 Dashboard Pages 1️⃣ Executive Overview
Total Customers
Total Churn
Churn Rate
Average Balance
Comparison: Churned vs Retained Customers
2️⃣ Churn by Demographics
Churn Rate by Geography
Churn Count by Age
Churn Rate by Gender
Churn Rate by Age Groups
Churn Matrix for Age × Geography
3️⃣ Financial & Behavioral Insights
Churn Rate by Salary Group
Churn Rate by Tenure
Churn Rate by Credit Score Rating
Balance Distribution & Patterns
4️⃣ Product & Engagement Insights
Churn Rate by Number of Products
Churn Rate by Active Member
Churn Rate by Credit Card Ownership
📝 Executive Summary (One-Page)
This Power BI project delivers a comprehensive churn analysis for a retail bank. With a churn rate of 20.37%, the bank faces significant customer loss, especially among customers in Germany, females, middle-aged groups, inactive members, and single-product users. Financial and behavioral patterns strongly influence churn, such as lower balance, mid-income salary groups, and poor credit scores.
The dashboard offers a 360° view of customer behavior, helping stakeholders identify high-risk segments and implement targeted retention strategies. Recommendations include region-specific interventions, engagement programs, mid-life financial solutions, and cross-selling strategies. This analysis equips the bank with actionable insights to reduce churn and strengthen long-term customer relationships.