This is like having a smart early‑warning radar on your customer calls. It quietly watches patterns in how often people call, what they call about, and how their tone changes, then flags who is most likely to leave so your team can step in before they cancel.
High and often unexpected customer churn in telecom due to dissatisfaction that isn’t detected early; manual review of call logs and tickets is too slow and inconsistent to catch at‑risk customers in time.
Proprietary historical interaction data (calls, tickets, account history) combined with churn labels, embedded into telco CRM and support workflows for ongoing model improvement and high switching costs.
Classical-ML (Scikit/XGBoost)
Feature Store
Medium (Integration logic)
Data integration and feature engineering across call logs, CRM, and billing systems; maintaining model performance as customer behavior and products change.
Early Majority
Focus on telecom call patterns as primary signal for churn, rather than generic account health scores; deeper use of call frequency, duration, issue types, and possibly sentiment from voice interactions to create highly targeted churn‑risk alerts and playbooks.