Reactive Retention Strategy
Retention teams only engaged customers after cancellation requests or payment failures — when win-back costs were highest and success rates were lowest.
Case Study
Customer Churn Prediction
Proactive Retention Through Machine Learning
An ML-powered churn prediction system that identifies at-risk telecom subscribers 30 days before they leave — enabling retention teams to act early with personalised offer recommendations tailored to each customer's profile.
Role: Data Scientist & ML Engineer
Category: AI Automation
Industry: Telecom
Lead Time: 30 Days Before Churn
Scope: Prediction · Segmentation · Offer Engine
Overview
Platform Overview
A mid-size telecom operator was losing subscribers at a rate that threatened revenue growth. Retention teams were reactive — intervening only after customers had already requested cancellation or stopped paying. By then, win-back offers were expensive and success rates were low. The business needed to see churn coming and act while customers were still engaged.
We built a machine learning churn prediction platform that scores every subscriber daily against hundreds of behavioural, usage, billing, and support signals. High-risk customers are surfaced 30 days before predicted churn with explainable risk factors and personalised retention offer recommendations — integrated directly into the CRM so retention agents can act without switching tools.
The Challenge
What Problem Were We Solving?
Telecom churn is expensive. Acquiring a new subscriber costs significantly more than retaining an existing one, yet most operators only discover churn risk when a customer calls to cancel or misses a payment. Retention campaigns sent to broad segments are inefficient — the right offer for a price-sensitive customer is not the right offer for someone leaving due to poor network experience.
No Early Warning Signals
Churn indicators were scattered across billing, usage, support, and network systems with no unified view — making it impossible to identify at-risk customers proactively.
Generic Retention Offers
One-size-fits-all discount campaigns were sent to broad segments — wasting budget on customers who would have stayed anyway and missing those with specific, addressable concerns.
Disconnected CRM Workflow
Risk insights lived in data reports that retention agents never saw. By the time intelligence reached the front line, the customer had often already churned.
Our Solution
How the Platform Solves It
We unified subscriber data from across the operator's systems into a feature engineering pipeline that generates hundreds of behavioural signals — usage trends, billing patterns, support ticket history, network quality scores, and contract lifecycle markers. A trained ensemble model scores every subscriber daily, surfacing high-risk customers with explainable top risk factors and a recommended retention action.
30-Day Churn Risk Scoring
Every subscriber receives a daily churn probability score with a 30-day prediction window — giving retention teams a full month to intervene before cancellation.
Explainable Risk Factors
Each prediction comes with the top contributing signals — declining usage, recent support complaints, price plan mismatch — so agents understand why a customer is at risk.
Personalised Offer Recommendations
A recommendation engine matches each at-risk profile to the most effective retention offer — tariff adjustment, loyalty credit, service upgrade, or proactive support outreach.
CRM-Integrated Agent Workflow
High-risk customers appear directly in the retention team's CRM with risk score, contributing factors, and recommended action — no separate reporting tools required.
Key Features
Platform Capabilities
Eight integrated capabilities covering data engineering, model training, scoring, explainability, offer personalisation, and CRM delivery.
Unified Subscriber Data Pipeline
Ingests and harmonises data from billing, usage, support, network, and contract systems into a single subscriber feature store updated daily.
Behavioural Feature Engineering
Generates hundreds of derived signals — usage trend deltas, support sentiment scores, billing anomaly flags, and contract lifecycle markers — tuned for telecom churn patterns.
Ensemble Churn Classifier
Gradient boosting and ensemble models trained on historical churn data, validated with time-series cross-validation to prevent data leakage and overfitting.
30-Day Prediction Window
Models optimised for a 30-day forward-looking churn window — balancing early enough to act with sufficient precision to avoid alert fatigue on false positives.
SHAP-Based Explainability
Every prediction includes top contributing risk factors with human-readable explanations — so retention agents understand the why, not just the score.
Personalised Offer Engine
Matches churn risk profiles to the most effective retention intervention — tariff changes, loyalty credits, service upgrades, or proactive support outreach.
CRM Integration & Agent Alerts
High-risk subscribers surfaced directly in the retention CRM with score, risk factors, and recommended action — triggering automated outreach workflows where configured.
Model Monitoring & Retraining
Continuous monitoring of prediction accuracy, feature drift, and campaign outcome feedback — with scheduled retraining to keep models current as subscriber behaviour evolves.
How It Works
From Data to Retention Action in 6 Steps
A daily scoring pipeline that turns raw subscriber data into actionable retention intelligence for front-line teams.
Data Ingested
Usage, billing, support, and network data pulled from source systems overnight.
Features Engineered
Behavioural signals computed and stored in the subscriber feature store.
Risk Scored
ML model assigns a 30-day churn probability to every active subscriber.
Risk Explained
Top contributing factors generated with human-readable explanations.
Offer Recommended
Personalised retention intervention matched to the customer's risk profile.
Agent Notified
High-risk customers surfaced in CRM for proactive retention outreach.
Tech Stack
Built With
A scalable ML engineering stack designed for daily scoring at telecom subscriber scale with full model explainability.
Python & scikit-learn
ML Model Development
XGBoost & Ensembles
Churn Classification Models
SHAP
Model Explainability
Feature Store
Subscriber Signal Pipeline
Apache Airflow
Daily Scoring Orchestration
CRM APIs
Agent Workflow Integration
Results & Impact
What We Delivered
A production churn prediction platform that shifted retention from reactive to proactive — with measurable improvement in subscriber retention rates.
0%
Improvement in Retention
Proactive interventions converted significantly more at-risk subscribers than reactive win-back campaigns
0 days
Average Lead Time Before Churn
Retention teams alerted a full month before predicted cancellation — time to act while customers are still engaged
0%
Model Precision on High-Risk Tier
Top-risk segment predictions validated against actual churn outcomes over a 6-month evaluation period
CRM-Ready
Agent Workflow Integration
Risk scores and offer recommendations delivered directly into the retention team's daily workflow
The difference between reactive and proactive retention is timing. By scoring every subscriber daily and surfacing at-risk customers 30 days before churn with a specific recommended action, we gave the retention team something they had never had before — enough time to make an offer that actually worked.
— Data Scientist, AI Consultants
Skills & Deliverables
What Was Built
Core Skills Applied
Churn Prediction Modelling
Feature Engineering
Model Explainability (SHAP)
Recommendation Systems
ML Pipeline Engineering
Project Deliverables
- Unified subscriber data pipeline and feature store
- Ensemble churn classification model with 30-day prediction window
- SHAP-based explainability layer for agent-facing risk factors
- Personalised retention offer recommendation engine
- CRM integration and automated agent alert workflows
- Daily batch scoring orchestration with Airflow
- Model monitoring, drift detection, and retraining pipeline