Retail Banking Customer Churn Analytics

This project analyzes customer churn in a retail banking dataset using Power BI dashboards and a Random Forest machine learning model. The objective is to identify customers who are likely to leave the bank and support data-driven retention strategies.

Dashboard Preview

Customer Overview

Churn Analysis

Churn Risk Prediction

Dataset

The project uses the Bank Customer Churn Modelling Dataset from Kaggle.

• Total customers: 10,000
• Target variable: Exited
  • 1 = Customer churned
  • 0 = Customer retained

Key features include:

• CreditScore
• Geography
• Gender
• Age
• Tenure
• Balance
• NumOfProducts
• IsActiveMember
• EstimatedSalary

Data Preparation (Power BI)

Several transformations were applied in Power Query:

• Removed identifier columns

  • RowNumber
  • CustomerId
  • Surname

• Created segmentation features:

  • AgeGroup → Young, Middle Age, Senior
  • CreditScoreCategory → Poor, Average, Good, Excellent
  • BalanceCategory → No Balance, Low, Medium, High

These transformations improve interpretability when analyzing churn behavior.

Machine Learning Model

A Random Forest Classifier was used to estimate churn probability.

Libraries used

• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn

Model configuration

• Train/Test Split: 80/20
• Cross Validation: 5-fold
• n_estimators: 200
• max_depth: 8

Average model accuracy: ~0.82

The model generates a churn probability score for each customer. These probability scores are exported and used in Power BI to classify customers into churn risk segments.

Risk Segmentation

Customers were categorized based on predicted churn probability.

Probability	Segment
≥ 0.565	High Risk
0.35 – 0.565	Medium Risk
< 0.35	Low Risk

Approximately 22% of customers fall into the high-risk segment.

Power BI Dashboard

The dashboard consists of three pages:

Customer Overview

• Customer distribution by geography
• Customer demographics by age group
• Product ownership analysis

Churn Analysis

• Churn by geography
• Churn by age group
• Credit score analysis
• Balance vs credit score behavior

Churn Risk Prediction

• Customer distribution by risk level
• Average churn probability by segment
• High-risk customer identification
• Business insights for retention strategies

Key Insights

• ~22% of customers fall into the high-risk churn segment
• High-risk customers have an average churn probability of ~0.75
• Middle-aged customers represent the largest churn segment
• Customers with moderate credit scores account for significant churn

These insights help banks prioritize targeted retention campaigns.

Technologies Used

• Power BI
• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn

Author

Sowdeshwar Survesha Kumaar
Master of Data Science — University of Queensland