Real-time churn risk scores
and retention cohort analysis

Customer health scoring — assigning a risk level to each subscriber based on behavioral and transactional signals — is standard practice at enterprise SaaS companies and almost entirely absent at the early-stage companies that need it most. The reason is data infrastructure: building a meaningful risk scoring system traditionally requires a data warehouse, a data analyst, and several months of implementation work. For a SaaS founder managing growth, retention, and product simultaneously, that's not a realistic investment. Automated risk scoring changes the math.

Payment history is the most predictive signal for subscription churn in most SaaS businesses. A customer who has had two failed payment episodes in the past 6 months is meaningfully more likely to churn in the next 90 days than one who has had zero. A customer who was on a pause (cancelled but reinstated) is at higher risk than one who has never paused. These signals are already in your Stripe data — they just need to be surfaced in a usable way. A risk score that synthesizes payment history, account age, plan tier, and recent activity into a simple High/Medium/Low signal gives you something you can act on without a data team.

The value of risk scoring compounds with your response system. A High-risk customer who has a payment failure should immediately trigger your most aggressive recovery sequence — not the standard one. A High-risk customer who hasn't had a failure yet is a candidate for proactive outreach: a check-in email, a CSM call, a usage review. Without a risk score, these customers are invisible until they churn. With a risk score, they're identifiable and actionable days or weeks before the churn event occurs.