Understand how a pricing move changes willingness to pay before customers ever see it.
Set a new price against a configured population of buyers and see the willingness-to-pay distribution, the demand curve, and the revenue-maximizing price before rollout.
OVERVIEW
Pricing is one of the hardest decisions to reverse. Raise it and you may lose customers you never hear from; keep it low and you leave revenue on the table. The catch is that you usually only learn which one happened after the new price is already live.
Polyhyle lets you test the price change first. It puts the new price in front of a configured population of synthetic buyers, asks each one what they would actually pay and whether they would accept or walk away, and turns those answers into a willingness-to-pay curve and a revenue-maximizing price. You see the trade-off between conversion and revenue before a single customer is affected.
INSIDE Polyhyle
Pricing decisions
A 12% price increase paired with a time-saving message across EU SaaS product teams.
01
Define the price move and the buyers
Set the current and new price, the packaging, and the customer segments you want to test the change against.
02
Ask each buyer their ceiling
Every synthetic buyer states the exact maximum price they would pay, whether they accept, hesitate on, or reject the new price, and what would make it acceptable.
03
Build the demand curve
The maximum-price answers become a willingness-to-pay distribution, and at each candidate price Polyhyle computes the share who still convert and the revenue it implies.
04
Find the revenue-maximizing price
Get the optimal price, the accept-hesitate-reject split, and projected MRR and LTV per segment, before the change reaches a single customer.
Polyhyle.app/simulations/pricing-decisions
SIMULATION DETAIL
Pricing decisions
A 12% price increase paired with a time-saving message across EU SaaS product teams.
Running
Optimal price
$59
Revenue lift
+12.4%
Acceptance
58%
World inputs
Current packaging, price points, and discount rules
Customer segments by company size, urgency, and budget sensitivity
Competitor pricing assumptions and market pressure
Simulated outcome
Roll out the increase to high-intent teams first, keep a churn-save message ready for cost-sensitive cohorts, and avoid changing packaging until the copy signal is validated.
Behavior signal
30 day simulated horizon
SIGNALS YOU GET BACK
Optimal price$59Revenue lift+12.4%Acceptance58%
Roll out the increase to high-intent teams first, keep a churn-save message ready for cost-sensitive cohorts, and avoid changing packaging until the copy signal is validated.
WHY SIMULATE THIS
The usual way to de-risk a price change is to ship it and watch, or to run a long survey and hope the stated answers match real behavior. Both are slow, and a live mistake costs you churned accounts and a number you cannot quietly walk back.
Simulating it first turns that gamble into a comparison. In an afternoon you can test several price points, see which segments balk and which absorb the increase, and reach the rollout already knowing the revenue-versus-churn trade-off, instead of discovering it on real invoices.