In consumer SaaS, churn often shows up in declining logins and engagement. In enterprise B2B platforms, it is quieter. Customers still sign in, file tickets, and attend QBRs, while the renewal decision is already forming months earlier.

This talk covers how product teams can combine heterogeneous signals into one account-risk call, drawing on firsthand work building an account-health and churn-risk system at Molecule, a cloud-native energy trading platform processing about $100 billion in commodity value daily.

The core argument: operational and relationship signals (support tickets, CST sentiment, complaints, usage gaps) often predict risk better than session counts alone. The hard problem is rarely a fancier model. It is stacking signals, weighting them with product judgment, and turning the output into a clear next action for account teams.

The session walks through a practical signal-stack framework and defensible outcomes from shipped work: identifying elevated risk about 6 to 8 weeks earlier than prior processes, and reducing account-manager labor by roughly $23K per year across about 100 accounts through better prioritization. Related reporting consolidation on the same platform saves customers about $100K to $150K per year in implementation and operational overhead where adopted.