AI-Powered Churn Prediction Engine for Telecom
Ajay Kurchami
Case Study: Retention Savior — AI-Powered Churn Prediction Engine
The Situation:
The telecom business was facing a silent but severe revenue leak: customer churn.
With a churn rate of approximately 26%, the company was losing 1 out of every 4 customers. While acquisition efforts were aggressive, retention was reactive—teams attempted to save customers only after cancellation signals appeared.
Key problems included:
Blind spots: Retention offers were generic and often deployed too late
Wasted budget: Discounts were given to customers who were not at risk
Lack of clarity: Leadership knew customers were leaving, but not why
The challenge was not just predicting churn—it was understanding the drivers behind it early enough to intervene.
What the Business Needed:
To stop the revenue bleed, the retention team required three core capabilities:
Early Warning System
Identify customers at high risk months before cancellation.
Precision Targeting
Focus retention spend only on customers who actually needed intervention.
Explainability
Clearly understand why a customer was at risk (price, service quality, contract type) to guide conversations.
The objective was to move from damage control to proactive revenue protection.
The Solution:
I designed and deployed Retention Savior, a machine-learning-driven decision engine that continuously scores every customer’s likelihood to churn.
From a stakeholder’s perspective, this delivered:
Risk Scoring: A simple 0–100% churn probability per customer
Root-Cause Insight: AI-generated explanations behind each risk score
Operational Dashboard: A clean interface for support teams to test retention scenarios before making offers
This transformed churn from a post-mortem metric into a preventable event.
How the System Was Built :
Customer Behavior Intelligence
The system ingests customer demographics, service usage, and billing history and converts raw data into 30+ behavioral signals, including:
Contract sensitivity: Month-to-month vs long-term contracts
Service load: Impact of add-ons like streaming, security, and backups
Tenure stability: Identifying high-risk lifecycle periods (months 1–12)
What this means for stakeholders:
The model understands the quality of the customer relationship—not just total spend.
Handling Imbalanced Data (Why Most Models Fail Here)
Only 26% of customers churn—making churners a minority class that most models under-learn.
To solve this, I implemented SMOTE (Synthetic Minority Over-sampling) so the model treats churn prevention as seriously as customer acquisition.
What this means for stakeholders:
High-risk customers are no longer “lost in the noise.”
Predictive Modeling
An XGBoost Classifier was used due to its strong performance on structured business data.
The model was tuned specifically for Recall (74.33%)
Why Recall matters:
In retention, missing a churner is far more expensive than contacting a loyal customer. The system prioritizes catching risk early.
Explainable AI (Trust over Black Boxes)
To avoid “black-box AI,” I integrated SHAP (Explainable AI).
For every prediction, the system produces a clear reason code, such as:
Contract type
Payment method
Service quality indicators
Key insight discovered:
Fiber-optic customers showed the highest churn risk—flagging a potential service or hardware issue for engineering teams.
Technology Stack (Reliable & Auditable)
Programming: Python
Data Processing: Pandas, NumPy, Scikit-Learn
Machine Learning: XGBoost, Imbalanced-Learn (SMOTE)
Explainability: SHAP
Interface: Streamlit
Deployment: GitHub & Streamlit Cloud
This stack was chosen for stability, transparency, and ease of deployment, not experimentation.
Business Impact:
After implementation, the retention team gained:
74% Capture Rate: Identifies 3 out of 4 at-risk customers before churn
Strategic Clarity: Month-to-month contracts and electronic check payments surfaced as the top churn drivers
Real-Time Triage: Support agents can instantly assess risk and prioritize high-value accounts
Retention decisions became data-guided, not instinct-driven.
Reliability & Trust:
To ensure executive confidence:
Strict train/test separation (20% unseen hold-out data)
Transparent tracking of false positives vs false negatives
ROI-oriented evaluation of retention campaigns
Deliverables:
✅ Live Retention Dashboard (Risk Simulator)
✅ Production-Ready Churn Model (Versioned
.pkl)✅ Performance Report (Confusion Matrix & Metrics)
✅ Explainability Report (Top Drivers of Churn)
Scalability & Future Readiness:
The system is built to evolve:
Automated retraining with new monthly data
API-ready architecture for CRM integration (Salesforce, HubSpot)
Extendable to cross-sell and upsell intelligence
This case study demonstrates how predictive analytics can turn customer data into protected revenue—not just reports.
— Ajay (AjayDataLabs)
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Posted Jan 10, 2026
AI-powered churn prediction engine for telecom that identifies at-risk customers early and supports proactive, data-driven retention strategies.
