Telecom AI Churn Intelligence
This AI solution uses machine learning on call patterns, usage behavior, and network data to predict which telecom subscribers are most likely to churn and why. It surfaces risk drivers, prioritizes at‑risk segments, and recommends targeted retention offers and CX interventions. The result is higher customer lifetime value, lower acquisition and retention costs, and more stable recurring revenue for telecom operators.
The Problem
“You’re finding churn after the revenue is gone—and guessing why customers leave”
Organizations face these key challenges:
Customer, billing, network QoE/QoS, and support data sit in silos, making churn drivers hard to diagnose quickly
Retention campaigns are blanket discounts with weak targeting, causing over-spend and margin erosion
Churn signals arrive too late (post-complaint/post-cancel) because reporting is batch-based and lagging
Hard to prove which interventions work: limited attribution, inconsistent segmentation, and no closed-loop learning