This is like having a smart early-warning system that spots which mobile or internet customers are about to leave and suggests the best way to keep them—before they call to cancel.
High customer churn in telecom by predicting which subscribers are likely to leave and enabling targeted retention actions at the right time.
Access to rich, proprietary telecom customer data (usage, network events, billing, support tickets) and embedding models into existing CRM/OSS/BSS workflows make the solution sticky and harder to replicate quickly.
Classical-ML (Scikit/XGBoost)
Feature Store
Medium (Integration logic)
Model performance and feature freshness at large telecom scale (millions of users, high-velocity event data) and integration with existing billing/CRM systems.
Early Majority
Positioned as an AI-focused, likely more nimble retention and analytics layer for telecoms, as opposed to heavy, monolithic BSS/OSS suites; can emphasize faster deployment and more modern ML/AI techniques over legacy rule-based churn models.