💼 7) Business Recommendations Based on Churn Prediction Insights
With the evaluation of machine learning models completed and key customer behavior drivers identified, the following strategic and tactical recommendations can help reduce churn and improve customer retention:
🎯 A. Target Month-to-Month Contract Customers
Insight: The type of contract was the most important feature. Action: - Offer incentives to convert month-to-month customers to longer-term contracts. - Launch retention campaigns specifically tailored for this high-risk segment.
💡 B. Focus on Customers with High Monthly Charges
Insight: Monthly charges are among the top predictors of churn. Action: - Conduct a price sensitivity analysis. - Provide customized packages or loyalty rewards for high-paying customers.
🧾 C. Improve Perceived Value of OnlineSecurity, TechSupport, and OnlineBackup
Insight: These services are important churn predictors. Action: - Upsell or bundle these services effectively. - Ensure quality and responsiveness in service delivery.
👥 D. Segment and Prioritize High Churn-Risk Customers
Insight: Top 10–15% of customers are up to 3.7x more likely to churn. Action: - Build a churn risk dashboard. - Prioritize proactive outreach and retention offers.
📊 E. Monitor and Adjust Based on Predicted Probabilities
Insight: Logistic Regression showed good calibration. Action: - Use churn probability as a score to personalize strategies. - Set thresholds for alerts and interventions.
🤝 F. Retention Strategy Based on Tenure
Insight: Tenure strongly correlates with churn. Action: - Create a 90-day onboarding and engagement program. - Schedule check-ins at key churn-risk intervals.
📈 G. Continuous Model Monitoring & A/B Testing
Insight: Models showed strong performance but require monitoring. Action: - Monitor model drift and retrain regularly. - Use A/B testing to evaluate effectiveness of retention interventions.