Support Vector Machine-based customer retention system for telecommunications companies to identify at-risk customers and optimize retention strategies
A major telecommunications provider experienced high customer churn rates, losing valuable subscribers to competitors without sufficient warning to implement retention strategies. Their existing churn prediction methods were reactive rather than proactive, missing opportunities to retain customers before they made the decision to switch providers.
We implemented a sophisticated SVM model that analyzed customer usage patterns, billing history, service interactions, demographic data, and competitive market dynamics. The system incorporated advanced feature engineering techniques and kernel methods to capture complex non-linear relationships in customer behavior, providing early warning signals for potential churn.
The SVM-based churn prediction system achieved 88% accuracy in identifying customers likely to churn within 60 days, enabling proactive retention campaigns. Customer churn rates decreased by 31% through targeted interventions and personalized retention offers. The improved customer retention resulted in $4.2M in retained annual revenue and significantly improved customer lifetime value.