How much of your book renews?
A single retention forecast is a wish. Split your book by health, then simulate the renewal year five thousand times to see your expected retained ARR, the accounts most likely to churn, and — crucially — the range you should actually be planning around.
Size it and split it by health.
Healthy accounts renew ~95% of the time, neutral ~85%, at-risk ~60%. Adjust the mix to match your book.
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Retained ARR, 5,000 simulated years.
Each run renews every account at its segment’s probability. The spread is the point: your retention is a distribution, and planning to the mean alone ignores the downside.
The catch: —
Retention is a forecast too — and usually a worse one than new business.
Net revenue retention now drives valuation, yet most teams forecast it with a single optimistic number and no range. Whether your retention forecast is trusted, evidenced, and segmented is part of Forecasting Trust, one of six dimensions in the Readiness Index. See where it stands.
How to model retention risk across your entire renewal book
Your CS team reports 90% gross retention. Finance plans on 88%. The CEO tells the board 92%. Nobody actually knows because nobody has modeled the range of outcomes. This simulator runs a Monte Carlo analysis on your renewal book: thousands of scenarios, each applying churn probability per account based on risk profile, producing a distribution of possible outcomes rather than a single guess.
Enter your accounts up for renewal with their ARR and health status. The simulator assigns churn probabilities by risk level and runs 1,000+ scenarios. The output is not a single retention number but a range: expected retained ARR, the 10th percentile (bad case), the 90th percentile (good case), and the specific accounts contributing the most variance to the outcome.
Who this tool is for
CS leaders planning retention targets. Finance teams modeling renewal revenue. CROs who need to know how much net new pipeline is required to cover potential churn shortfalls.
How to interpret your results
The accounts contributing the most variance to your outcome are where intervention has the highest impact. Focus CS resources on those accounts first. The gap between your expected outcome and your plan tells you how much net new revenue is needed to cover potential shortfalls. If your health score inputs are based on gut feel rather than data, the simulation inherits that uncertainty; pair with the Health Score Model Builder first.
This tool maps to the Forecasting Trust dimension of the AI-Ready RevOps Framework. For a broader assessment, take the free assessment.
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