In today’s highly competitive financial services industry, retaining customers is more critical—and challenging—than ever. For credit card companies, predicting customer churn (when a customer stops using a service) is essential for maintaining profitability and growth. With this in mind, I embarked on a data science project to predict credit card customer churn, utilizing machine learning techniques and Kaggle's credit card customer dataset.
Project Overview
Customer churn prediction uses historical customer data to identify patterns indicating a likelihood of departure. My project involved analyzing a wide range of customer attributes and transaction data to build a predictive model that helps pinpoint potential churners. This model allows credit card companies to proactively reach out to high-risk customers with targeted retention strategies.
The Dataset
The dataset, sourced from Kaggle, included a variety of features describing customer demographics, account details, transaction history, and credit card usage patterns. Here’s a brief look at some of the key features:
Customer Demographics: Age, gender, education level, and marital status. Account Details: Credit limit, card category, months of inactivity, total revolving balance.
Transaction Patterns: Total transactions in the last year, transaction amounts, and the ratio of transaction types.
Approach and Methodology
1. Exploratory Data Analysis (EDA): Before diving into model building, I conducted EDA to gain insights into the data’s structure and distribution. Visualizations helped reveal trends in customer behavior, such as spending habits, credit limit utilization, and account activity levels. I also looked for correlations between features to identify the most significant indicators of churn.
2. Data Preprocessing: Data preprocessing was crucial to ensure accuracy. This included handling missing values, encoding categorical variables (like gender and card category), and scaling numerical features to ensure uniformity. Additionally, I balanced the dataset to mitigate any impact from class imbalance, which often skews predictions in favor of the majority class.
3. Feature Engineering: I created new features that could capture more nuanced aspects of customer behavior, such as the ratio of inactive months to active months and a normalized transaction frequency. These engineered features allowed the model to capture additional patterns that weren’t obvious in the original data.
4. Model Selection: After prepping the data, I experimented with various machine learning algorithms, including Logistic Regression, Decision Trees, Random Forest, and Gradient Boosting. I chose the best-performing model based on evaluation metrics like accuracy, precision, recall, and F1-score. The goal was to balance predictive accuracy with interpretability, ensuring the model’s insights could guide real-world decisions.
5. Model Evaluation: Beyond accuracy, I evaluated the model using precision and recall metrics to understand its effectiveness in identifying actual churners without too many false positives. The confusion matrix provided a breakdown of correct and incorrect classifications, helping fine-tune the model for optimal performance.
Key Findings
High Churn Predictors: Features like the number of inactive months, credit limit utilization, and total revolving balance were strong indicators of potential churn. Customers with higher inactivity periods and lower engagement with their credit cards were more likely to churn.
Importance of Account Activity: The model showed that customers with a consistently high number of transactions and regular card usage were less likely to churn. This insight highlights the importance of encouraging regular card usage to retain customers.
Customer Profile: Younger customers with lower income brackets and limited credit histories showed higher churn rates. This finding suggests that tailored retention efforts for these segments could help improve customer loyalty.
Business Implications and Recommendations
The predictive model and analysis provided actionable insights that could guide credit card companies in their customer retention efforts:
Targeted Retention Campaigns: Using the churn predictions, companies can identify high-risk customers and create personalized retention offers, such as loyalty points, reduced fees, or exclusive promotions, to encourage ongoing engagement.
Inactivity Alerts: The model identified inactivity as a significant churn predictor. Credit card companies could introduce inactivity alerts or reminders for customers who haven’t made recent transactions, encouraging them to re-engage with their accounts.
Customized Services for At-Risk Segments: Certain customer demographics, such as younger, lower-income users, were more prone to churn. Companies can design customized financial products or loyalty programs to appeal to these segments, fostering long-term relationships.
Data-Driven Decision Making: With an accurate churn model, credit card providers can make data-backed decisions, allocating resources toward retaining the most at-risk customers while maintaining a focus on profitable customer segments.
Conclusion
This project demonstrates the power of data science in customer relationship management within the credit card industry. By predicting customer churn, companies can take a proactive approach to customer retention, strengthening their competitive position and improving overall profitability. As businesses continue to leverage machine learning and predictive analytics, the ability to understand and address churn at an individual level will become a core component of customer-centric strategies.
