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Telecommunications Operator

Cutting Subscriber Churn with Predictive Analytics

How real-time subscriber intelligence turned reactive retention into a proactive, revenue-protecting program.

Cutting Subscriber Churn with Predictive Analytics — platform overview

32%

Reduction in Churn

2.1M

Events Processed / Day

8 wks

Time to Launch

This operator's retention team was always one step behind — by the time a subscriber called to cancel, the decision had already been made. We partnered with them to replace reactive retention with a proactive, data-driven program.

The Challenge

Subscriber churn was trending upward and the retention team had no way to know who was at risk until it was too late. Retention offers went out to broad segments instead of the customers most likely to leave, wasting budget without moving the needle.

Our Approach

We built a real-time subscriber intelligence platform that ingests usage, billing, and network event data, then scores every subscriber's churn risk daily using a model trained on 18 months of historical behavior. Risk scores feed directly into the retention team's workflow, triggering personalized offers automatically for high-risk, high-value subscribers.

  • Streaming ingestion of network & billing events
  • Daily churn-risk scoring model
  • Automated, tiered retention-offer triggers
  • Real-time operations dashboard for the retention team

Technologies Used

Apache KafkaPythonXGBoostAmazon RedshiftGrafana

The Results

Within the first two quarters after launch, the operator saw a measurable drop in voluntary churn and a healthier allocation of retention budget toward the subscribers who actually needed it.

This platform gave our retention team a superpower — we finally know who's at risk before they call to cancel.

VP of Customer Operations, Telecommunications Operator client

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