Project Overview
This project aims to tackle two key challenges in the banking industry:
- Customer Churn Prediction: Identifying at-risk customers and understanding the factors driving churn.
- Customer Lifetime Value (CLV) Analysis: Evaluating long-term customer profitability to prioritize retention efforts effectively.
Author: Hijir Della Wirasti
GitHub: Bank Customer Churn Prediction and CLV Optimization
Code: Python
Dataset: Bank Customer Churn Dataset
Objectives
- Develop a churn prediction model using machine learning to identify at-risk customers.
- Perform CLV segmentation to identify high-value customers and derive actionable insights.
- Provide practical recommendations to reduce churn and maximize customer lifetime value.
Data Summary
The dataset includes customer demographics, financial behavior, product usage, and churn labels. The target variable (Churn) is highly imbalanced, requiring resampling techniques such as SMOTE (oversampling) and RandomUnderSampler.
Tested Models
A total of 11 models were evaluated:
- Logistic Regression
- Random Forest
- Gradient Boosting
- AdaBoost
- Support Vector Machine (SVM)
- K-Nearest Neighbors (KNN)
- Naive Bayes
- Decision Tree
- XGBoost
- LightGBM
- CatBoost
Top Models per Dataset Type:
Normal Data:
- Gradient Boosting: F1 = 0.611872
- Random Forest: F1 = 0.584235
- CatBoost: F1 = 0.591463
Oversampled Data (SMOTE):
- CatBoost: F1 = 0.808713 ✅ Best Overall
- LightGBM: F1 = 0.795393
- XGBoost: F1 = 0.787676
Undersampled Data:
- CatBoost: F1 = 0.759494
- Gradient Boosting: F1 = 0.744544
- LightGBM: F1 = 0.744304
Best Model: CatBoost (Oversampled Data)
Business Recommendations
1. Address High-Churn Age Groups:
- Older Customers: Provide personalized communication, financial advisory services, or exclusive offers.
- Younger Customers: Implement onboarding programs to engage customers with low tenure, such as gamified milestones.
2. Focus on Low-Tenure Customers:
- Offer loyalty incentives like welcome discounts or milestone-based rewards for early retention.
3. Improve Retention for Low-Balance Customers:
- Introduce flexible payment plans or rewards for increasing account balances.
4. Tailor Strategies Based on Country Segmentation:
- Develop country-specific campaigns for Spain and Germany with localized messaging and culturally relevant offers.
5. Enhance Engagement for Low Product Usage:
- Cross-sell or bundle relevant products to encourage customers to expand their usage.
6. Leverage High CLV Segments (VIP & High):
- Provide premium services, tiered loyalty benefits, and upselling opportunities for high-value customers.
7. Promote Balanced Spending:
- Encourage medium-CLV customers to move to higher tiers by offering exclusive rewards.
8. Increase Tenure for Higher CLV:
- Launch campaigns promoting long-term contracts and time-based loyalty rewards.
9. Monitor Credit Score Impact:
- Offer financial education programs or personalized financial advice to low-credit-score customers.
10. Incentivize Product Expansion:
- Promote bundled packages or discounts for adding multiple products to increase customer value.
Conclusion
This project demonstrates how machine learning models and CLV analysis can provide actionable insights for reducing churn and optimizing customer profitability. The CatBoost model trained on oversampled data emerged as the best predictor, offering a balanced approach to precision and recall. These insights can empower the bank to implement targeted retention strategies, foster customer loyalty, and maximize long-term profitability.