Fake Door Pricing: The Validation Methodology That Shows Who Will Pay
Last updated: March 31, 2026
TLDR
Fake door pricing shows real pricing tiers on your site before the product exists. When a visitor clicks a tier CTA, you record the click and redirect to a waitlist. The click is the signal: that person saw a real price and took an action. This is more reliable than survey data because it costs the person something — the decision to click a $29/month button is not the same as answering 'would you pay $29/month?' on a form. This guide covers how to set it up, what to track, and how to read the results.
import DefinitionBlock from ‘@validation/ui/seo/definition-block.astro’;
import AnswerBlock from ‘@validation/ui/seo/answer-block.astro’;
import InlineSignup from ‘@validation/ui/components/inline-signup.astro’;
Q&A
How is fake door pricing different from a coming soon page?
A 'coming soon' page tells visitors the product doesn't exist yet before they take any action. A fake door pricing page shows real pricing and collects behavioral signals before revealing the product isn't available. The difference is in what you measure: a coming soon page measures brand awareness, a fake door pricing page measures willingness to pay at specific price points. For validation purposes, the behavioral signal from a fake door click is far more useful than the pageview of a coming soon page.
Q&A
Is it dishonest to show pricing for a product that doesn't exist?
The ethical line is: do not take payment for something you haven't built. Showing a pricing page and collecting intent signals (clicks, email signups) is standard practice. The redirect page makes the situation clear: the product is in development, the visitor is joining a waitlist. Dishonesty would be taking credit card information and charging it. Measuring intent by showing real pricing and recording clicks is how the practice is used responsibly.
How do I choose the right price points for fake door pricing?
Start with what you know about the market. If you have competitors, look at their published pricing and anchor around the same range. If you're creating a new category, use the price of the manual alternative (the time cost of the current workflow) as a ceiling. A common indie hacker starting point for B2B SaaS: $9/mo Starter, $29/mo Pro, $79/mo Agency. These are round numbers at intervals that are typical in the market. Adjust based on what tier distribution tells you — if 80% of clicks go to the $9 tier, you may be underpricing your pro tier or it's missing features.
What if nobody clicks any pricing tier?
Zero clicks after 100+ visitors reaching the pricing section means one of three things: the value proposition above pricing isn't convincing anyone to read that far, the pricing is too high for the audience, or the CTA copy isn't triggering action. Diagnose with a session recording tool (Hotjar, PostHog session replay): are visitors scrolling past the pricing section? If they're not even reaching it, the above-fold content isn't creating enough interest. If they're reaching it and not clicking, the pricing or CTA copy is the issue.
Can I run fake door pricing without any backend code?
PostHog's client-side event tracking (posthog.capture) can record tier clicks without a custom backend. Tally.so embeds can simulate a pricing CTA that opens a form. For pure no-code validation, a Carrd or Webflow page with a form-based 'Get Started' button that opens an interest form is a proxy for fake door pricing. The limitation: none of these approaches give you server-side reliability. Client-side tracking is sufficient for a quick experiment; server-side tracking is required for data you'll make a build decision on.
How should I present pricing tiers to maximize click data quality?
Horizontal layout with three columns is the standard. Mark the mid tier 'Most Popular' or 'Recommended'. Price the tiers clearly: the annual price (if applicable) alongside the monthly. List 4-5 specific features per tier, with the differentiating features clearly marked at each level. The CTA button on each tier should be the same color and same size — no visual hierarchy that implies one tier is 'right'. You want unbiased behavioral data, not a dark pattern that pushes people toward a tier.
