How we built and deployed a machine learning churn prediction model for a telecom company, enabling proactive retention and preserving over $2.4M in annual recurring revenue.
A regional telecom provider was losing approximately 4.2% of its subscriber base monthly to competitors. Their retention strategy was entirely reactive — calling churned customers post-cancellation with win-back offers that had a less than 8% success rate.
The business had rich transaction, usage, and support data but no analytical capability to turn it into predictive insight. Leadership challenged us to build a system that would identify at-risk customers 30+ days before their likely cancellation date.
Ingested 3 years of customer data from billing, CRM, network usage, and support ticket systems. Cleaned and normalised 2.1M records, handling missing values and encoding categorical features.
Created 47 predictive features including usage trend scores, support escalation frequency, contract tenure ratios, and competitive market exposure indicators.
Trained and evaluated 6 model types. XGBoost outperformed others with 91% precision and 88% recall on the hold-out test set. Explainability via SHAP values was preserved for business users.
Deployed model via AWS SageMaker endpoint. Daily batch scoring pipeline pushes churn probability scores and top risk factors for each customer directly into Salesforce CRM fields.
Collaborated with the retention team to map churn probability score bands to specific intervention playbooks — personalised offers, service enhancements, or proactive support calls.
In the six months following deployment, the client's monthly churn rate fell from 4.2% to 2.8% — a 34% reduction. The retention team's outreach became highly targeted allowing them to focus efforts on the highest-value at-risk accounts.
“Before this model, we were chasing customers out the door. Now we can have the right conversation with the right customer at the right time. It has changed our retention strategy fundamentally.”
— Head of Customer Retention, Regional Telecom Provider
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