#1
Recover ~$2,800 failed / at-risk MRR first
Involuntary risk — highest-MRR open invoices first.
Which SaaS Customers Are at Risk of Churning?
Which customers need outreach before they leave?
Rank failed payments, early-life cancels, and downgrades — risk signals, not ML.
Enter your numbers
Example output
Start with ~$2,800 failed / at-risk MRR — then early cancels and downgrades.
Outreach order: recover involuntary failures, interview early-life cancels, then reach accounts that downgraded. Not an ML churn score.
#2
9 cancels in the first 30 days
Early-life churn points to fit or activation — not more ads.
#3
~$650 in recent downgrades
Soft churn: reach step-downs before they cancel.
Connect Stripe so Guide ranks failed payments, early tenure, and plan churn with Evidence.
How it works
01
Enter risk signals
Failed MRR, early cancels, downgrades, concentration.
02
Get ranked outreach buckets
Recover payments, then early cancels and downgrades.
03
Connect Stripe for live Evidence
Guide ranks failed $, early tenure, and plan churn.
Why this happens
Founders ask who is about to churn and get a rate chart instead. Risk for outreach this week lives in failed payments, early-life cancels, downgrades, and concentration — not a black-box score.
How to identify it
Enter failed / at-risk MRR, early cancels, downgrade MRR, and top-account share. The tool ranks outreach buckets. Connect Stripe for the same cuts from synced data.
Common causes
Open payment failures, cancels in the first 30 days, soft churn via downgrades, and MRR concentrated in a few logos.
Recommended actions
Recover highest-MRR failures first, interview early cancels, reach downgrades, and schedule check-ins on concentrated accounts.
How FlarePath diagnoses it
Connect Stripe so Guide ranks failed payments, early tenure, and plan churn with Evidence. FlarePath does not predict cancels with ML.
Analyze this with FlarePath
Calculating the metric is only the first step.
Understanding why it changed is the harder problem. FlarePath checks connected data for meaningful movement, labels the strength of the evidence, and points to what to investigate next.
- Detect important changes in recurring metrics
- Diagnose causes behind the movement
- Surface trends worth acting on
- Recommend ranked next steps
Related tools
About this tool
How do I find at-risk customers without ML?
Start with behavioral risk: open failed payments, cancels in the first 30–60 days, recent downgrades, and concentration in a few large accounts. Those lists beat a black-box score.
Failed payment vs engagement risk — which first?
Failed payments first. They are involuntary and recoverable with dunning and outreach. Then early-life cancels (fit/activation), then downgrades (soft churn).
Who should I email first?
Highest-MRR open failures, then newest cancels with the largest lost MRR, then accounts that downgraded recently. Concentration check-ins sit beside those lists.
How is this different from a churn-rate diagnosis?
Churn-rate pages explain why the rate moved. This tool answers who needs outreach now from the risk signals you already see in Stripe.
How does FlarePath help?
Connect Stripe to see failed $, early-tenure pressure, and plan churn ranked in Guide with Evidence — not a predictive churn model.
Does FlarePath predict who will cancel?
No. It surfaces payment failures, early tenure, and plan movement from synced Stripe data. This free tool uses the numbers you enter.