Predicting Customer Churn with Machine Learning
This project leverages machine learning to predict customer churn by analyzing service usage, contract details, and payment history. The model identifies key churn drivers, helping businesses implement targeted retention strategies and improve customer engagement.
The raw data was obtained through a public dataset available on Kaggle.
Customer churn is a significant challenge for businesses. Losing customers means losing revenue, and acquiring new customers is often costlier than retaining existing ones. In this blog post, I explore how machine learning can help predict churn and guide retention strategies.
Why Predict Churn?
Understanding why customers leave enables businesses to:
- Improve customer experience.
- Personalize engagement efforts.
- Increase revenue by reducing churn rates.
How I Built the Model
I analyzed customer demographics, service usage, and payment behaviors to train machine learning models. Our approach included:
- Data cleaning & preprocessing to handle missing values and standardize data.
- Feature engineering to extract meaningful insights.
- Machine learning modeling with algorithms like Logistic Regression, Random Forest, and XGBoost.
- Model evaluation using precision, recall, and ROC curves.
Key Insights
- Customers on month-to-month contracts are more likely to churn.
- Paperless billing is associated with higher churn rates.
- Long-tenured customers are less likely to leave.
The Business Impact
With accurate churn predictions, companies can proactively:
- Offer personalized discounts to at-risk customers.
- Enhance customer support and engagement.
- Adjust pricing and contract structures to improve retention.
Conclusion
Machine learning provides a powerful way to anticipate customer behavior and optimize retention efforts. By leveraging data-driven insights, businesses can reduce churn and maximize customer lifetime value.
Interested in learning more? Check out the full project on my GitHub!