AI-Powered Customer Churn & Risk Prediction Engine for a Telecom Operator

Overview

A leading telecom operator serving 40+ million customers across prepaid, postpaid, and broadband segments faced rising churn rates and declining customer satisfaction. With intense competition, price wars, and shifting digital expectations, the operator struggled to retain high-value customers and lacked intelligence-driven interventions.

The operator engaged O2 Technologies to build a comprehensive AI-powered churn and risk prediction engine capable of predicting customer churn 30–90 days in advance, identifying root causes, and triggering intelligent retention actions across channels.

Challenge

The telecom provider’s retention strategy was reactive, inefficient, and expensive.

Key challenges included:

• High churn across prepaid and postpaid segments due to network issues, billing disputes, or aggressive competitor offers.
• Fragmented data across CRM, billing, network performance, usage behavior, and support logs.
• Retention teams depended on generic campaigns with low effectiveness.
• No visibility into which customers were at risk, why, or when they would churn.
• Limited ability to segment customers by value, behavior, or service experience.
• Retention decisions based heavily on intuition instead of analytics.

The business required a modern machine learning–driven decision engine for churn and risk management.

O2 Technologies’ Solution

1. Unified Customer 360 Data Foundation

O2 unified customer-level data from 40+ systems, including:

• Call Detail Records (CDRs)
• Recharge history and usage trends
• Network performance metrics
• Customer complaints and support tickets
• Billing and payment behavior
• Plan details, tenure, and product mix
• App engagement and website interactions

This resulted in a consolidated Customer 360 model supporting AI and analytics.

2. Churn Prediction Machine Learning Models

O2 built advanced ML models capable of forecasting churn with high accuracy:

• Predictive models identifying churn 30–90 days in advance.
• Ensemble algorithms using gradient boosting, sequence modeling, and time-series signals.
• Individual customer-level churn risk scoring.
• Root-cause classification: network issues, billing problems, low engagement, competitor impact, etc.
• Separate churn models for prepaid, postpaid, and broadband segments.

The models delivered high precision and actionable risk insights.

3. Value-Based Segmentation & Prioritization

To maximize ROI, O2 developed:

• Customer Lifetime Value (CLV) scoring
• Behavioral and profitability segmentation

Customers were grouped into:

• High-value, high-risk
• High-value, low-risk
• Low-value, high-risk
• Low-value, low-risk

This enabled precise and cost-effective retention strategies.

4. Prescriptive Retention Engine & Automation

The churn engine automatically triggered targeted interventions:

• Personalized offers such as data packs, discounts, or loyalty rewards.
• Network optimization checks for customers experiencing poor connectivity.
• Billing dispute alerts and automated resolution workflows.
• CRM signals for agents to prioritize high-value customers.
• Automated communication via SMS, app notifications, WhatsApp, email, and IVR.

AI-powered personalization significantly improved engagement and conversions.

Implementation

• Phase 1 — Data Unification & Feature Engineering: Integrated 40+ data sources and engineered 500+ predictive features.
• Phase 2 — Model Development & Validation: Built and tested churn models for each customer type.
• Phase 3 — CRM & Channel Integration: Connected the scoring engine with CRM, digital apps, and call center systems.
• Phase 4 — Campaign Automation: Enabled real-time scoring and automated retention workflows.
• Phase 5 — Continuous Monitoring & Optimization: Implemented drift detection, retraining pipelines, and impact dashboards.

Conclusion

The churn prediction engine delivered major business impact:

• 20% reduction in churn within the first 6 months.
• 35% improvement in retention efficiency with lower campaign costs.
• Higher customer satisfaction through personalized interventions.
• Proactive customer retention instead of reactive firefighting.
• Customer 360 insights supporting marketing, service, and network teams.
• Increased ARPU by prioritizing high-value customers for retention efforts.

The telecom operator now runs a predictive, data-driven, and proactive customer retention ecosystem.