Saving $2M in ARR with Predictive Churn Detection

ScaleUp SaaS had a churn problem they couldn't see coming. Customers would seem happy one month and cancel the next. By the time the CS team knew there was an issue, it was too late.

The company was losing $200K+ in ARR every month to unexpected churn. Leadership knew they needed to be more proactive, but they had no visibility into which accounts were at risk.

We built a churn prediction engine that analyzed multiple data sources: product usage patterns, support ticket sentiment, billing changes, and engagement metrics. The model learned to identify the subtle patterns that preceded churn.

The system flagged at-risk accounts 30-90 days before they would typically cancel, giving the customer success team time to intervene. We also built custom playbooks for different risk profiles.