SaaS Pricing Impact Simulator
See what a price change does to ARR once the customers you lose are counted, and how much churn the rise can absorb before it stops paying.
What this calculates
This applies a price change to your existing base, removes the accounts you expect to lose because of it, and reports the net effect on ARR. The number that matters most is the breakeven tolerance, which is the share of customers you could lose and still break even. It says nothing about whether new customers will accept the new price.
At typical inputs for a B2B SaaS company with 640 accounts at $840 a month, a 10% price rise that costs 0.4 points of extra churn changes ARR by +$616,735, and breaks even at 9.1% of accounts lost.
Your numbers
Your base today
The change
Results update as you type. Nothing is sent anywhere, the calculation runs in your browser.
Net ARR change
Live+$616,735
$6.5M to $7.1M of ARR, +10%
- Breakeven churn tolerance
- 9.1%
- Accounts lost
- 3
- New ARPU
- $924
Healthy
You could lose several times the churn you are expecting and still come out ahead. That gap is the real finding here. It usually means the last price rise was too small, and the one before it did not happen at all.
Pressure-test this against your real numbers
Thirty minutes on how to stage the increase so the first cohort tells you something.
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How is this calculated?
The new price is applied to every account, the accounts you expect to lose are removed, and the result is compared with what you have today. The breakeven tolerance falls out of the price ratio alone: a rise of x percent survives losing the fraction of customers that exactly cancels it, which is why a large rise tolerates proportionally more churn than a small one.
Formula
New ARPU = ARPU × (1 + price change) Accounts kept = accounts × (1 − extra churn) Net ARR change = (new ARPU × accounts kept × 12) − (ARPU × accounts × 12) Breakeven churn tolerance = 1 − 1 ÷ (1 + price change) Gross uplift is the same change with no accounts lost. The difference between the two bars is the churn drag.
- The rise applies to the whole base at once. Grandfathering existing accounts changes the answer, usually by delaying most of it by a renewal cycle.
- Extra churn is expressed in percentage points of accounts, not of revenue. If your largest accounts are the ones most likely to leave, the revenue effect is worse than this shows.
- Nothing here models the effect on new business. A price that works for your base can still slow acquisition, and that shows up a quarter later.
- Usage, overage and services revenue are excluded. Only the recurring subscription line moves.
When this number misleads
This treats every account as equally likely to leave. In practice churn from a price rise is concentrated in the accounts that were already getting the least value, which means the revenue effect is usually better than the account effect and the support burden falls too. It also means the opposite can be true if your discounted enterprise accounts are the ones renegotiating. The model cannot see which of those you have.
Questions founders ask about this
Why is the breakeven tolerance so much higher than my expected churn?
Because price is the only lever with no cost of goods attached. Ten percent more revenue from the same accounts drops to the bottom line almost intact, so it takes a large number of departures to cancel it. That gap is the usual argument for raising prices more often and by less.
Should I model this on ARPU or on list price?
On ARPU, which is what you actually collect after discounts. Modeling on list price will overstate the uplift by roughly your average discount, and discount is where most of the lost revenue in a B2B book already sits.
What churn number should I put in?
If you have never raised prices, start with the share of your base you know is unhappy or unengaged, because that is who leaves first. Then run it again at double that figure. If the answer holds at both, the decision is not close.
Do you store what I enter?
No. The calculation runs in your browser and nothing is transmitted. Your last inputs are saved in your own browser so the page remembers them when you return. If you use the email field, only the result summary and your address are sent.
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