Media & Telecom
AI-Driven Subscriber Churn Prediction
The Challenge: Escalating Subscriber Loss
A major telecom provider with over 8 million subscribers was losing 2.5% of its customer base monthly, translating to significant revenue erosion. Existing retention efforts relied on generic offers triggered only after customers had already initiated cancellation, resulting in low conversion rates and wasted marketing spend. The provider lacked the ability to identify at-risk subscribers before they decided to leave, and had no mechanism to personalize retention interventions based on individual customer behavior and preferences. A data-driven approach was needed to predict churn proactively and deliver targeted, personalized retention campaigns.
Our Solution: Predictive Churn Intelligence Platform
Lydatum built a comprehensive churn prediction and intervention platform that combines advanced machine learning with a unified customer data foundation:
- Customer 360 Platform: We consolidated data from billing systems, call center interactions, network usage logs, digital engagement touchpoints, and customer service tickets into a unified customer profile on Databricks. This 360-degree view provided the rich feature set needed for accurate churn modeling, capturing behavioral signals across every customer interaction channel.
- Gradient Boosting Churn Models: Using Databricks MLflow, we trained gradient boosting models (XGBoost and LightGBM) on historical churn patterns, engineering over 150 predictive features including usage trend changes, billing anomalies, service quality metrics, competitive market signals, and customer lifecycle stage. The ensemble approach achieved high predictive accuracy while providing interpretable risk factor attribution for each subscriber.
- Personalized Intervention Engine: Rather than generic retention offers, we built a recommendation engine that matched each at-risk subscriber with the most effective intervention based on their predicted churn drivers. Interventions ranged from targeted plan upgrades and loyalty rewards to proactive service quality improvements and personalized engagement campaigns, each optimized through continuous A/B testing.
- Real-Time Scoring and Orchestration: Churn risk scores were updated daily and integrated with the provider's CRM and marketing automation platforms. Automated workflows triggered the right intervention at the right time through the subscriber's preferred channel, whether that was an in-app offer, a targeted email, or a proactive outreach call from the retention team.
The Impact: Reduced Churn, Higher ROI, Revenue Preserved
The churn prediction platform delivered measurable results within the first quarter of deployment:
35%
Reduction in Monthly Churn
By identifying at-risk subscribers weeks before they would have churned and delivering personalized interventions, the provider significantly reduced its monthly churn rate. The shift from reactive, generic offers to proactive, personalized retention campaigns quadrupled campaign ROI while reducing overall retention spend. The Customer 360 platform also provided ongoing value beyond churn prediction, enabling more effective cross-sell and upsell targeting and informing product development decisions based on deep customer behavior insights.
Technologies Used: Databricks, MLflow, XGBoost, LightGBM, Customer 360 Platform, Python, CRM Integration, Marketing Automation
