How to Simulate Churn Risk Across Your Renewal Book
Your CS team reports 90% gross retention. Finance is planning on 88%. Your CEO is telling the board 92%. Nobody actually knows because nobody has modeled the range of outcomes.
Why simulation beats forecasting for renewals
A traditional renewal forecast treats each account as binary: renew or churn. A simulation assigns a probability to each account, then runs thousands of scenarios. The result is a distribution: expected value, 10th percentile (bad case), and 90th percentile (good case).
How to run a simulation
Start with your renewal book: every account up for renewal with its ARR and health score. Convert health scores into churn probabilities. Run at least 1,000 scenarios. The Churn Risk Simulator does this automatically.
Using the results
The simulation shows: expected retained ARR, the accounts contributing most variance (where your intervention has the most impact), and the gap between expected outcome and plan.
Churn simulation combines the Data Foundation dimension and Forecasting Trust dimension of the AI-Ready RevOps Framework.
Try it free →Churn Risk Simulator · Health Score Model Builder
Frequently asked questions
What is a Monte Carlo simulation for churn?
It runs thousands of random scenarios on your renewal book, each time applying churn probability per account based on risk profile. The result is a distribution of possible outcomes.
How accurate are churn simulations?
Accuracy depends on input quality. If health scores genuinely predict churn, the simulation is useful for planning. If health scores are arbitrary, the simulation is too.
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