Enterprise B2B customers rarely announce that they are becoming a churn risk. An account may continue logging in, attending meetings, and engaging with a product while warning signs remain scattered across support tickets, customer sentiment, complaints, usage gaps, and other operational signals. In this session, I will share my experience building an account-health and churn-risk framework at Molecule, a cloud-native energy trading platform supporting approximately $100 billion in commodity value daily. I will explore how diverse data sources can be brought together to create a more complete view of account health, identify meaningful patterns earlier, and support better business decisions. The session will cover practical approaches to structuring heterogeneous data, separating meaningful signals from noise, prioritizing risks, and translating data-driven insights into actionable interventions. Attendees will leave with a practical framework for turning fragmented operational data into clearer insights, earlier risk identification, and measurable business outcomes.