Subscriber churn significantly impacts revenue.
Subscriber churn significantly impacts revenue. Early indicators — declining usage, unresolved complaints, payment disputes, poor network experience — are scattered across systems and difficult to act on at scale.
ML classification models trained on historical churn data predict individual churn probability. Models combine usage patterns, billing history, complaint activity, demographic data and network quality signals. Propensity scores trigger automated retention interventions.
Join usage, billing, care, network quality and interaction data per customer.
Predict each customer's probability of leaving in the next period.
Show the main reasons behind each score.
Trigger the right retention offer or care action.
Compare churn in treated and control groups.
This page describes generic industry practice and public standards. It is not based on, and does not describe, any particular vendor's product or operator's systems.