Imagine having a super-skilled analyst who watches every customer’s behavior in real time, predicts who is likely to leave, and tells your team exactly what offer or message will keep them—at telecom scale, 24/7.
Telecoms struggle with high churn and low loyalty because they can’t react to early warning signals in time or personalize retention actions for millions of customers. AI for customer retention turns raw customer data (usage, payments, support tickets, complaints) into churn predictions and recommended next-best-actions so operators can intervene before customers leave.
Proprietary historical customer data (usage, churn labels, interactions) and integration into core CRM/BSS workflows create a defensible moat; once models are tuned to a specific telco’s base and campaigns, switching providers becomes costly.
Classical-ML (Scikit/XGBoost)
Feature Store
Medium (Integration logic)
Maintaining real-time or near-real-time feature pipelines and model inference at telecom scale (millions of subscribers), plus data quality and governance across many disparate source systems.
Early Majority
Positioned specifically around telecom-grade customer data and retention workflows (rather than generic churn prediction), likely offering integrations into existing CRM/BSS stacks and tailored feature engineering for telco signals such as ARPU, usage profiles, and contract lifecycle events.