Customer churn is no longer a lagging indicator - it is a predictable outcome. Our speaker explores how organizations can combine machine learning–driven risk detection with the contextual judgment of customer success teams to proactively retain high-value customers. This presentation outlines how behavioral signals, usage patterns, and engagement data can be transformed into actionable churn predictions, and how human expertise turns those predictions into meaningful, timely interventions. Attendees will learn how to move from reactive retention tactics to a scalable, AI-augmented customer success model that balances automation with empathy — ultimately improving retention, revenue stability, and long-term customer relationships.