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Most SaaS health scores are just login frequency with a traffic light on top. They tell you who already left, not who is leaving. Here is what to track instead.

AI has collapsed the timeline from innovative feature to table stakes. When competitors can clone your product in weeks, the only durable moat is making your product impossible to leave.

By the time a customer clicks the cancel button, the decision was made 2-8 weeks earlier. The real question is whether you can detect and intervene during the decision window.

Every skipped health check, every ignored usage drop, every deferred onboarding fix adds to your retention debt. Unlike tech debt, the interest rate is your MRR.

Most churn prediction focuses on product usage. But your Stripe data already contains five behavioral signals that predict cancellation weeks before it happens.

When the only person who uses your product stops showing up, you have days — not weeks. Champion dependency is the churn signal most tools miss entirely.

Soft declines leak 3-8% of MRR every month for the average SaaS company. Most never notice because the revenue disappears silently.

Breaking churn by acquisition cohort reveals retention gaps your topline metrics hide. If one channel retains at half the rate of another, that's a fixable problem.

20-40% of all SaaS churn is involuntary — failed payments that nobody actively decided on. Here's how to calculate your exposure and recover most of it.