Free SaaS Pricing Stress Tester

The three scenarios are hardcoded, and their coefficients matter more than your inputs do. Raising prices is modeled as a 20% lift with 3% of revenue lost. The usage tier is 12% up with 1% lost. Removing the free plan multiplies revenue by 0.88, 0.92 or 0.96 depending on the conversion rate you enter, and takes 1% off. Your churn figure is applied on top of all three. Nothing in the file sources those numbers, and how a specific customer base responds to a 20% rise is exactly what the tool has no way to know, so read the output as three shapes to argue with.

The defaults show what that produces. Tiers of $29, $79 and $199 held by 50%, 35% and 15% of customers give an average of $72, and 4,000 customers put the baseline at $288,000 of monthly revenue. Say churn at 3%. The price rise scenario returns $325,175, a gain of $37,175. The usage tier returns $309,754, a gain of $21,754. Removing the free plan at a 3% conversion rate returns $254,441, a loss of $33,559. The ordering of those three is driven by the coefficients above, so the useful output is the gap between them.

The obvious reply is to test it instead of modeling it, and for a new-customer price that is the right call. It is harder than it looks on an installed base. A price change is close to irreversible in public, since a rise that gets reversed teaches customers to wait; existing contracts renew on their own dates, so a cohort takes a year to report; and the customers who leave are the least engaged, which flatters the retention number while the revenue tells a different story. Model to bound the decision, test on new customers, and leave the installed base for a deliberate migration.