Money, Pricing & Model

How do I run a real pricing experiment to test a higher price without tanking my signups?

A starting point

Test the new price on new traffic only, hold your existing customers at their current rate, and watch conversion and revenue-per-visitor together, not signups alone. A drop in signups with higher total revenue means the price is working, not failing. Give it enough volume and time to be more than noise before you trust the result.

Go deeper

Hand-picked from around the web, each with a note on why it earns your time.

3 resources 3 link-checked Read Use

Read

📄 Article
✓ Link checked Free Intermediate

Why we picked it This is the vendor-neutral walkthrough of how to actually structure a pricing test so you can trust the result. It starts with a falsifiable hypothesis, forces you to change one variable, gets sample size right, and reads conversion, ARPU, and retention together instead of celebrating a single number. It also tells you plainly to keep existing customers on their current rate and think through what happens when people compare prices, which is the part most founders skip.

Pricing experiments: A guide for businesses

From Stripe by Stripe

  • Change one variable and use a real sample-size calculation, or your higher-price result is just noise you talked yourself into believing.
  • Judge the test on revenue per visitor and retention together, not conversion alone, because a lower price that converts better can still lose money.
  • Test on new signups and treat it as a limited-time frame so current customers do not feel switched on mid-relationship.
Open stripe.com →
✍️ Essay
✓ Link checked Free Intermediate

Why we picked it Most pricing advice stays abstract, so here is a real writeup of an experiment with the numbers left in. They raised the monthly plan from 25 dollars to 33 and 41 dollars, watched monthly signups drop, and still ended up 16 percent higher on revenue per visitor at 99 percent significance because quarterly plans jumped. It is a clean picture of the tradeoff a higher price actually creates, and a reminder to wait for the cohort data before calling it a win.

Using experimentation to find a product's optimal price (and increase RPV by 16% in the process)

From Conversion.com by Stephen Pavlovich

  • Fewer signups at a higher price still beat the old price on revenue per visitor, which is the metric that actually pays you.
  • Raising the monthly price quietly pushed buyers toward quarterly plans, so a price change reshapes the whole mix, not just one line.
  • They held off on a final verdict until long-term cohort metrics came in, which is the discipline the headline number can make you forget.
Open conversion.com →

Use

🛠️ Tool
✓ Link checked Freemium Beginner

Why we picked it Before you touch live traffic, the cheapest way to read price sensitivity is to just ask people, and the Van Westendorp method is the four-question survey that does it. OpinionX is a real tool you can run this on for free, and this guide walks through each question and how to plot the answers into a price corridor. Treat the output as a direction to test, not a final number, then confirm it on real signups.

Van Westendorp for Pricing Research: A Survey Guide

From OpinionX by Daniel Kyne

  • Four short questions (too cheap, cheap, expensive, too expensive) map out an acceptable price range before you risk any live conversions.
  • It is directional, not a revenue-maximizer, so use it to pick which higher price to actually A/B test.
  • You can run it free in OpinionX, which makes it realistic for a founder building outside the big startup hubs with no research budget.
Open opinionx.co →

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