This is like giving your retention and customer care team a super-smart analyst who watches every customer interaction, predicts who is likely to leave, and tells you exactly what offers or actions will keep them longer and make them more valuable.
Reduces customer churn and increases Customer Lifetime Value (CLV) by predicting which customers are at risk of leaving, why they are at risk, and what targeted interventions will best retain them.
Proprietary churn and CLV models trained on telecom-specific data and behavioral signals, embedded into customer service and retention workflows, creating stickiness and cumulative performance advantages over time.
Hybrid
Feature Store
Medium (Integration logic)
Feature engineering and data integration across multiple telecom systems (billing, usage, CRM, network, support), plus model maintenance as customer behavior and offers change.
Early Majority
Positioned specifically around telecom churn reduction and CLV uplift, likely leveraging domain-specific features (usage patterns, contact center data, billing events) and playbooks rather than generic horizontal churn models.