12% Retention Lift

A mid-market SaaS provider faced rising churn that was difficult to anticipate, with Customer Success teams relying heavily on subjective health scores and lagging indicators. Attrition was often discovered only at renewal, leading to reactive retention efforts and inefficient allocation of CS resources. Leadership lacked a clear, data-backed view of where churn risk was concentrated or which interventions would drive the highest ROI.

Machine learning models were developed to identify churn risk signals up to 90 days prior to renewal, integrating behavioral, usage, and commercial data into a single predictive framework. Accounts were prioritized based on economic impact rather than volume, and insights were operationalized directly into RevOps and CS workflows. This shift enabled earlier, targeted engagement and materially improved decision-making. The initiative delivered a 12% increase in retention, reduced last-minute firefighting, and established a repeatable analytics foundation for long-term customer value management.