Churn Prediction: Building a bank customer churn prediction model using machine learning

Prerequisite

This article serves as a resource for individuals looking to gain insights into churn prediction, machine learning techniques, and their applications in the banking sector. To follow this article, readers should have:

Knowledge of Python programming for Data Science.
Basic understanding of machine learning and various machine learning algorithms.
Anaconda installed on your computer or a cloud platform like Kaggle or Google Colaboratory.

Repository

All the code used in this Article can be found here.

Customer Churn

Customer churn, also known as customer attrition or customer defection, refers to the phenomenon when customers or clients of a business or organization cease their relationship with the company by ending their subscription, canceling their services, or not making further purchases. In other words, it represents the loss of customers or clients over a specific period.

Churn is a critical metric for businesses across various industries, including telecommunications, banking, software, and subscription-based services, as it directly impacts the company's revenue and profitability. High churn rates can lead to increased costs for customer acquisition and hinder growth, making it crucial for businesses to identify and understand the factors that contribute to customer churn.

Predicting customer churn is an essential aspect of customer relationship management (CRM) and plays a significant role in implementing retention strategies. By analyzing historical customer data and behavior, businesses can develop churn prediction models using machine learning and other analytical techniques to identify customers at risk of churning. This allows businesses to proactively intervene and implement targeted retention efforts, such as personalized offers, loyalty programs, or improved customer support, to reduce churn and enhance customer loyalty.

Importance of Churn Prediction for Banks

Churn prediction is of significant importance for banks due to several reasons:

Retaining Profitable Customers: Identifying customers who are at a high risk of churning allows banks to focus their efforts on retaining those profitable customers. Customer acquisition is more expensive than customer retention, so keeping existing customers can lead to cost savings and increased revenue.
Reducing Revenue Loss: Churn can lead to a direct loss of revenue for banks, as they lose out on potential interest income, transaction fees, and other revenue-generating activities associated with active customers.
Enhancing Customer Experience: Understanding why customers churn can provide insights into improving the overall customer experience. Addressing pain points and providing better services can increase customer satisfaction and loyalty.
Data-Driven Decision-Making: Churn prediction involves analyzing vast amounts of customer data, which can lead to data-driven decision-making within the bank. This can help optimize marketing strategies, product offerings, and customer interactions.
Improving Customer Segmentation: Predicting churn can help banks segment their customer base more effectively. By categorizing customers based on their churn risk, banks can tailor personalized retention strategies for each segment.
Optimizing Marketing Efforts: Instead of spending resources on blanket marketing campaigns, banks can focus their marketing efforts on specific customer segments that are more likely to churn. This targeted approach can lead to higher campaign effectiveness and return on investment.
Risk Management: Churn prediction can also serve as an early warning system for potential financial distress in the bank. High churn rates might indicate issues with specific products or services, allowing banks to take corrective actions.
Competitive Advantage: Banks that effectively predict and manage churn can gain a competitive advantage over their peers. Retaining customers and offering personalized services can differentiate a bank in a crowded marketplace.
Long-term Customer Relationships: By proactively addressing customer churn, banks can foster long-term relationships with their customers. Loyal and satisfied customers are more likely to become brand advocates and refer others to the bank.
Increasing Customer Lifetime Value (CLV): Predicting churn and taking appropriate actions to retain customers can extend their lifetime value to the bank. A higher CLV translates to better profitability over the long run.

Machine Learning in Churn Prediction

Machine learning has become an invaluable tool in churn prediction for businesses, including banks and other industries where customer retention is crucial. By leveraging advanced algorithms and data analysis, machine learning models can identify patterns and indicators that help predict which customers are likely to churn. Here's an overview of how machine learning is applied in churn prediction:

Data Collection: The first step is to gather relevant data about the customers, their behaviors, interactions with the bank's products and services, demographics, transaction history, customer service interactions, and other relevant information. This data is typically collected from various sources, such as customer databases, transaction logs, and customer feedback.

In this article, we'll use this dataset from Kaggle.
Data Preprocessing: Before feeding the data into machine learning algorithms, it undergoes preprocessing. This involves data cleaning, dealing with missing values, scaling, normalization, and feature engineering to ensure that the data is in a suitable format for analysis.
Feature Selection: Feature selection is a crucial step in churn prediction. It involves choosing the most relevant and informative features (variables) that are likely to influence customer churn. Feature selection helps reduce noise and dimensionality in the data, making the model more efficient and accurate.
Model Selection: Different machine learning algorithms can be used for churn prediction, including logistic regression, decision trees, random forests, support vector machines (SVM), gradient boosting, and neural networks. The choice of model depends on factors such as data characteristics, interpretability, and performance requirements.
Model Training: The selected model is trained using preprocessed data. During training, the model learns patterns and relationships from historical data to predict customer churn. The data is typically split into training and validation sets to evaluate the model's performance.
Model Evaluation: After training, the model's performance is evaluated using metrics like accuracy, precision, recall, F1-score, and ROC-AUC. The model's ability to correctly predict churn and non-churn instances is assessed and adjustments may be made to improve its performance.
Hyperparameter Tuning: Many machine learning models have hyperparameters that need to be tuned to achieve optimal performance. Hyperparameter tuning involves searching for the best combination of hyperparameters through techniques like grid search or random search.
Deployment and Monitoring: Once a satisfactory model is obtained, it can be deployed to predict churn on new customer data. The model's performance is continuously monitored to ensure it remains accurate and up-to-date as customer behavior patterns may change over time.
Retaining Customers: Based on the churn predictions, banks can devise targeted retention strategies for high-risk customers. These strategies may include personalized offers, proactive customer service, loyalty programs, or incentives to keep customers engaged and satisfied.
Iterative Improvement: Machine learning in churn prediction is an iterative process. As new data becomes available, the model can be retrained to incorporate the latest information, leading to continuous improvement in its predictive capabilities.

Understanding the Data

The first thing is to try to understand the data. On examining the data, It shows there are 12 columns and 10000 rows. Irrelevant columns were dropped and null values were checked for.

df = pd.read_csv("/content/Bank Customer Churn Prediction.csv")
df.info()
df.drop('customer_id', axis=1, inplace=True)
df.isnull().sum()

Data Preprocessing

The categorical variables; "gender and country" were preprocessed by one-hot encoding. One-hot encoding is a technique used to convert categorical variables into a numerical format that machine learning models can work with effectively. It creates new binary columns (also known as dummy variables) for each unique category in the specified columns. The presence of a category is represented by a 1, and the absence is represented by a 0 in the corresponding dummy variable.

After encoding, the data is split into training and testing sets using the train_test_split function from the scikit-learn library with a test size of 30% and a random state of 42, this allows you to obtain the same split every time you run the code.

The data is further scaled using the StandardScaler from scikit-learn to scale the features of the training and testing sets.

df = pd.get_dummies(df, columns = ['gender', 'country'])

y = df['churn']
X = df.drop('churn', axis=1)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()

X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

Building Prediction Models

Before building the models, Hyperparameter tuning is done using GridSearchCV from scikit-learn. GridSearchCV is used to systematically search through a specified parameter grid and find the best combination of hyperparameters that yield the highest accuracy (or any other specified scoring metric).

A logistic regression classifier with the best hyperparameters found using the GridSearchCV is trained on the training data set and evaluated on the testing set.

We now went ahead to calculate some important metrics for the model:

The accuracy of the model is calculated by comparing the predicted target values (y_pred) with the actual target values from the testing set (y_test). The accuracy score is the proportion of correctly classified samples among all samples in the testing set.
The confusion matrix provides a detailed breakdown of the model's predictions, showing the number of true positives, true negatives, false positives, and false negatives.

The confusion matrix is organized as follows:

	Predicted Negative (0)	Predicted Positive (1)
Actual Negative (0)	True Negative (TN)	False Positive (FP)
Actual Positive (1)	False Negative (FN)	True Positive (TP)

The F1-score is the harmonic mean of precision and recall, and it is used to evaluate binary classification models.

from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV

log_clf = LogisticRegression(random_state = 42)
param_grid = {
            'penalty' : ['l2','l1'],
            'C' : [0.001, 0.01, 0.1, 1, 10, 100, 1000]
            }

CV_log_clf = GridSearchCV(estimator = log_clf, param_grid = param_grid , scoring = 'accuracy', verbose = 1, n_jobs = -1)
CV_log_clf.fit(X_train, y_train)

best_parameters = CV_log_clf.best_params_
print('The best parameters for using this model is', best_parameters)

classifier = LogisticRegression(C = best_parameters['C'],
                                penalty = best_parameters['penalty'],
                                random_state = 42)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
from sklearn.metrics import accuracy_score, f1_score, confusion_matrix

accuracy = accuracy_score(y_test, y_pred)
matrix = confusion_matrix(y_test, y_pred)
f1 = f1_score(y_pred, y_test, average = 'macro')

print('Accuracy is: {}'.format(accuracy))
print('F1_score is: {}'.format(f1))
print(matrix)

It is good practice to try different algorithms and compare performance so we went ahead to create a Balanced Bagging Classifier.

from imblearn.ensemble import BalancedBaggingClassifier
from sklearn.tree import DecisionTreeClassifier

#Create an object of the classifier.
bbc = BalancedBaggingClassifier(base_estimator=DecisionTreeClassifier(),
                                sampling_strategy='auto',
                                replacement=False,
                                random_state=0)


#Train the classifier.
bbc.fit(X_train, y_train)
preds = bbc.predict(X_test)

After running this code, it shows the BalancedBagging Classifier performed better than the logistic regression. This could be due to the fact that the BalancedBaggingClassifier is particularly useful when dealing with imbalanced datasets, as it allows the model to be more robust against the bias caused by the class imbalance. By using balanced sampling, it ensures that each bagging iteration includes a balanced representation of both classes, leading to more reliable and accurate predictions, especially for the minority class.

Summary of the Churn Prediction Model

The churn prediction model developed in this article aims to address the critical issue of customer churn in the banking sector. Customer churn, which refers to customers ending their relationship with the bank, can have significant financial implications and hinder growth. To combat this, the model employs machine learning techniques to predict potential churners and implement targeted retention strategies. Here is a summary of the churn prediction model:

The model utilizes historical customer data, including behaviors, interactions, and demographics.
Data preprocessing involves one-hot encoding and scaling for effective model training.
Feature selection reduces noise and dimensionality to enhance model efficiency.
Different algorithms, such as logistic regression and Balanced Bagging Classifier, can be used for churn prediction.
Model training and evaluation involve splitting data, tuning hyperparameters, and monitoring performance.
Evaluation Metrics:
- The model's accuracy, F1-score, and confusion matrix assess its predictive capabilities.
- Accuracy measures the proportion of correctly classified samples in the testing set.
- F1-score balances precision and recall, evaluating the model's binary classification performance.
- The confusion matrix provides detailed information on true positives, true negatives, false positives, and false negatives.
Balanced Bagging Classifier:
- The Balanced Bagging Classifier effectively handles imbalanced datasets.
- It uses balanced sampling to ensure equal representation of both classes during bagging iterations.
- This approach improves prediction accuracy, especially for the minority class (churners).

Future Scope and Enhancements

The churn prediction model presented in this article serves as a strong foundation for addressing customer churn in the banking sector using machine learning techniques. However, there are several potential future scopes and enhancements that can further improve the model's performance and applicability. Some of these include:

Feature Engineering: Exploring additional features or creating new features from existing data could lead to more informative predictors for churn. Domain knowledge and customer behavior insights can be utilized to engineer features that capture customer engagement, satisfaction, and loyalty.
Data Augmentation: If the dataset is limited, data augmentation techniques can be applied to generate synthetic samples to balance class distribution. Techniques like Synthetic Minority Over-sampling Technique (SMOTE) can help increase the representation of the minority class, improving the model's performance on predicting churn.
Ensemble Methods: Combining multiple models using ensemble techniques like stacking, boosting, or bagging can further enhance predictive accuracy and robustness. Ensemble methods can effectively harness the strengths of different algorithms, resulting in better generalization.
Deep Learning: Considering the availability of large datasets, deep learning models like neural networks can be explored for churn prediction. These models have the potential to capture complex patterns in the data and offer higher accuracy, though they may require more computational resources.
Time Series Analysis: Incorporating time series analysis into the model can provide insights into customer behavior trends and seasonal patterns, enabling better understanding of churn dynamics and predicting future churn more accurately.
Customer Segmentation: Implementing advanced customer segmentation techniques can help tailor personalized retention strategies for different customer groups. This can lead to more effective churn prevention efforts based on individual needs and preferences.
Real-time Prediction: Adapting the model to enable real-time churn prediction can empower banks to proactively respond to potential churners as soon as their behavior indicates a likelihood to churn.
External Data Integration: Integrating external data sources, such as social media activity, economic indicators, or customer sentiment data, can enrich the model's predictive power and provide a broader perspective on customer behavior.
A/B Testing: Conducting A/B testing to evaluate the effectiveness of different retention strategies can guide continuous improvement and optimization of customer retention efforts.
Interpretability and Explainability: Enhancing the model's interpretability and explainability can build trust and understanding among stakeholders, making it easier to implement the model's recommendations and actions.
Performance Monitoring: Implementing a robust performance monitoring system is crucial to ensure the model's accuracy remains consistent over time. Periodic evaluation and retraining of the model with fresh data are essential to maintain its relevance.
Deployment on Cloud: Deploying the churn prediction model on cloud platforms enables scalability and accessibility. Cloud-based deployment also facilitates easy integration with other business systems and applications.

By exploring these future scopes and enhancements, the churn prediction model can become even more effective in helping banks reduce churn, increase customer retention, and enhance overall customer satisfaction. Additionally, continuous research and development in the field of machine learning and data analytics will provide further opportunities for innovation and improvement in churn prediction models for the banking industry.

Conclusion

The churn prediction model presented in this article offers a comprehensive solution for banks seeking to reduce churn and enhance customer loyalty. By incorporating machine learning and data-driven decision-making, banks can optimize their operations and stay competitive in the ever-evolving banking industry.

The model's iterative nature allows continuous improvement, enabling banks to stay ahead in a competitive market, foster long-term customer relationships, and increase customer lifetime value. By following this guide, readers can gain insights into implementing churn prediction models, enabling banks to proactively address customer churn, retain profitable customers, and optimize their marketing strategies for long-term success.