🧠 9) Final Project Conclusion – TelcoRetentionAI
After completing the end-to-end CRISP-DM pipeline, the TelcoRetentionAI project successfully met both its primary and secondary business objectives:
🎯 Primary Goal: Predictive Modeling for Churn
- Among all tested models, the Gradient Boosting Classifier provided the best overall performance in terms of:
- ROC AUC and F1-Score, ensuring high predictive power.
- Stability across resampled datasets (SMOTE) and unseen test sets.
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Interpretability through feature importance and SHAP explanations.
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As such, Gradient Boosting is recommended as the primary model for churn prediction in production environments.
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Logistic Regression remains a strong candidate when explainability and calibration are critical for business or compliance reasons.
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Random Forest provides consistent performance and can be used in ensemble strategies to enhance model robustness and generalization.
📊 Secondary Goal: Business Insights and Recommendations
Visual and descriptive analytics enabled the discovery of actionable insights that support business decision-making:
- Contract Type: Customers with month-to-month contracts showed the highest churn rate. Incentives for migrating to longer contracts are recommended.
- Tech Support: Lack of tech support correlates with high churn. Upselling or bundling support services can improve retention.
- Paperless Billing: Tech-savvy users on paperless billing tend to churn more. Loyalty perks and engagement tracking are key.
- Senior Citizens: This group showed a proportionally high churn rate. Tailored support and simplified processes are advised.
- Partner Status: Customers without a partner are more likely to churn. Campaigns should focus on personalization and independence.
- CLTV Quartile: Customers with low lifetime value are the most volatile. High-CLTV churners should be prioritized for retention.
🧠 Strategic Impact
- The project not only produced a high-performing AI model, but also delivered clear, explainable business intelligence.
- Insights were visualized in dashboards and supported by data, guiding targeted retention strategies.
- The combination of machine learning + human-readable analytics provides a solid foundation for data-driven decision making.
🚀 Next Steps
- Deploy the Gradient Boosting model and monitor real-time churn predictions.
- Integrate churn risk scores with CRM systems to trigger automated retention campaigns.
- Refine feature engineering and retrain the model periodically with updated data.
- Track ROI on retention campaigns to measure business impact.
TelcoRetentionAI represents a complete AI-powered solution for customer retention in the telecom industry, bridging the gap between data science, business strategy, and operational excellence.