Price is the highest-leverage profit lever most teams control and the least measured, so they raise or cut the number on instinct. This free Claude skill measures willingness to pay, estimates elasticity, simulates tiers, and tells a price problem from a value problem.
We had been guessing our price for two years. This designed a van Westendorp survey, read the responses, and showed we were sitting below the acceptable range. We raised the number on new customers and revenue climbed with almost no churn.
Fed it my price and volume history and got an elasticity estimate under 1, meaning underpriced. Then it simulated a good-better-best structure with a real fence. Best part, it kept reminding me the survey number is a ceiling to test.
The diagnose mode was the unlock, our losses were not about price at all, they were a value-communication gap. Fixed the packaging instead of discounting and win rate went up.
Built for founders, product and growth teams, not pricing consultants.
Install once in Claude. It designs and reads willingness-to-pay surveys, estimates price elasticity from your own history, simulates good-better-best tiers with a fence, and diagnoses whether a weak result is a price problem or a value problem, each with a checks block of honest caveats.
A short setup walkthrough, plus a no-install option if you cannot add skills.
A single prompt you paste into any Claude chat to run the same four modes without installing anything.
Three steps, from your data to a price you can defend.
Survey responses or a request to design one, your price and volume history, feature or usage data, or your win-loss and discount records.
It builds or reads a van Westendorp or Gabor-Granger survey, estimates elasticity from a log-log regression, simulates good-better-best tiers around a value metric, or diagnoses price versus value, in code where the math needs it.
The acceptable price range or revenue-maximizing price, the elasticity reading, the tier structure and simulation, or the diagnosis and fix, each with a checks block flagging missing fields and what still needs a live test.
The skill works from what you bring, it does not connect to your billing system or a survey platform. It designs, computes, and interprets, but every stated willingness-to-pay number is a ceiling, and the only proof of a price is a live test on new-customer cohorts.
Willingness to pay is the highest-leverage number in pricing and the one most teams never measure, so they raise or cut the price on instinct. Here is how to measure it with the standard survey methods, estimate elasticity from your own data, and build price tiers around what buyers will actually pay.
Willingness to pay is the most a customer will pay for your offer, and it is measurable rather than a matter of feel. Price is the highest-leverage profit lever you control, a 1 percent price rise with volume held lifts operating profit by about 8 percent in McKinsey's S&P 1500 model and 11.1 percent in the Marn and Rosiello study, both directional and assuming no volume loss, against 3.3 percent for a 1 percent volume gain. The lever is symmetric, so a careless 1 percent discount vaporizes the same profit, which is why the job is to measure willingness to pay rather than raise or cut the number on instinct.
Three workhorse methods measure willingness to pay. The van Westendorp Price Sensitivity Meter asks four price questions and reads an acceptable range and an optimal point off the cumulative curves, good for a new product with no benchmark. Gabor-Granger asks purchase intent across a series of prices and finds the revenue-maximizing price at the peak of the revenue curve. Choice-based conjoint, the indirect gold standard at a sample of at least 300, shows realistic bundles with a walk-away option and derives the dollar value of each feature and price level. The method moves the answer, in one Conjointly case a naive how-much-would-you-pay question averaged 31.84, van Westendorp put the range at 5.75 to 7.67, and Gabor-Granger a revenue-maximizing 13.89, and because stated willingness to pay runs well above real, every survey number is a ceiling to discount and test.
If you have price and volume history, you can estimate elasticity directly. A log-log regression of quantity on price gives a coefficient that approximates elasticity, and an absolute value under 1 means you are underpriced, raising price raises revenue. The estimate is only trustworthy when the history contains real price variation and controls for seasonality and promotions, because correlation is not causation, so the skill flags weak data plainly and points you to a designed in-market test.
The highest-order pricing decision is the value metric, what you charge for, because it scales revenue with delivered value even when the number is somewhat off. On top of it, a good-better-best structure captures budget buyers, retains the core, and adds a Best tier priced 40 to 100 percent above current to harvest high-willingness-to-pay buyers, often 30 to 60 percent of a suite's revenue, protected by a fence attribute that stops trade-down. The discipline is to cap at three or four tiers, because the paradox of choice is real and conversion falls past four.
The skill runs four modes, WTP to design and read the surveys, ELASTICITY to estimate it from your data, TIERS to simulate packaging, and DIAGNOSE to tell a price problem from a value problem, since much of what looks like a price loss is a value-communication gap or a no-decision. The limits are honest, stated willingness to pay is a ceiling, an elasticity estimate needs real price variation, conjoint needs a sample of at least 300, and no model output is a decision, only a live price test is proof. For the full argument and the evidence, from the profit math to the diagnosis, read Price Is the Profit Lever You Never Measure.