AI-driven, real-time intelligence for customer retention
Millions in projected annual savings
Higher churn capture rate
Uplift in model performance
Improved real-time intervention
Traditional telecom churn models rely on static data and monthly scoring cycles, failing to capture real-time behavioral signals that indicate imminent churn. This delay prevents timely intervention, leading to missed retention opportunities and higher customer attrition in a highly competitive market.
Key challenges
Limited intervention window
Delayed churn detection cycles
Over-reliance on static features
Missing real-time behavioral signals
Ineffective targeting of at-risk users
The solution
Real-time intelligence
Hourly risk updates
Immediate signal detection
Intraday behavior tracking
Timely intervention triggers
Predictive modeling
CATBoost-based models
Dual scoring framework
Static + dynamic features
Continuous risk calibration
1
Feature engineering
Account
Recency data
Usage and interaction
2
Dual scoring
Daily baseline
Real-time updates
Prioritize active users
3
Platform
Scalable models
Automated pipelines
Monitoring and alerts
Financial impact
Reduced churn losses
Increased retention ROI
Significant cost savings
Customer retention
Improved engagement
Early churn detection
Targeted interventions
Business agility
Faster response cycles
Behavior-driven actions
Real-time decision-making
