The Founder’s Fake‑Door Catalogue: 12 High‑Signal SERP Pages to Turn into No‑Code Pricing Experiments

Surveys and waitlists measure intent at the level of words. A fake‑door pricing experiment forces a purchase decision (or a very close proxy) and converts a user’s intent into observable behavior: clicks on a price, cart adds, or real deposits. That behavioral signal is far more predictive of actual revenue than ‘interested’ answers on a form. Sources across product experimentation guides call this the standard smoke‑test playbook for validating willingness to pay. (koji.so)

Run fake‑door experiments where the action required is proportional to the value claim: for a $10/month offer measure email+card intent (or a 'reserve with card' CTA); for an enterprise $1,000/month offer measure booked demos plus deposit. The choice of conversion action determines how strong the signal is and therefore how small a sample you can reliably use. Practical guides repeatedly warn: collect the strongest honest signal you can without committing to build (no sensitive payment data on the fake page itself unless you intend to take deposits). (validea.dev)

Each entry below maps a common SERP intent (what someone Googling a keyword is looking for) to a minimal no‑code page you can publish in under an hour (Carrd, Webflow, or a single-page on your product site). For each experiment you'll get: one headline + 1 line value prop, one CTA (explicit buy/reserve/demo), the KPI to measure, exact sample size and duration, and a clear binary go/no‑go rule.

We base the sample sizes and thresholds on standard conversion math for landing pages and guidance from contemporary fake‑door and smoke‑test playbooks. If your traffic source will be cold ads you should expect lower conversion rates (so run larger samples); if traffic is warm (email, existing users), you can use the smaller sample sizes below. Adjust the numbers if you use very different channels. (dowhatmatter.com)

Below are 12 high‑signal experiments. Each block is a full recipe you can paste into a Carrd/Webflow hero and test. For brevity we use the same structural shorthand: SERP intent → hero headline / 1‑line, CTA, KPI to track, sample size & expected duration, go/no‑go rule.

Run these as separate pages and route paid traffic to each variant (or use distinct ad copy targeting the SERP intent). If you have limited budget, prioritize experiments 1–4 (direct revenue signals) and 5–8 (mid‑funnel demonstration of value). Sources for fake‑door patterns and conversion expectations are cited at the end of each card. (validea.dev)

7) ‘Feature pricing anchor’ (price sensitivity A/B): Headline: 'Priority support + SLA — $199/mo or $299/mo'. CTA: 'Reserve at $199 / $299'. KPI: split preference ratio. Sample: 600 visitors split evenly across two price anchors (300 each). Rule: if higher price wins by >15 percentage points → choose higher anchor; if within 15pp → trial both in product.

8) ‘Lifetime deal smoke test’ (one‑time purchase signal): Headline: 'Lifetime access for early teams — $249 one‑time'. CTA: 'Buy lifetime (refund within 30 days)'. KPI: purchases. Sample: 1,000 cold visitors. Rule: ≥0.8% conversions → consider limited early offer; <0.2% → avoid one‑time model.

9) ‘Add‑on services’ (professional services funnel): Headline: 'Setup + migration — $1,500'. CTA: 'Request migration (pay refundable deposit)'. KPI: deposit clicks / booked calls. Sample: 300 targeted visitors. Rule: ≥1% → offer as paid service; <0.3% → offer free concierge to convert and learn.

10) ‘Community paid tier’ (network effects test): Headline: 'Private community + monthly office hours — $15/mo'. CTA: 'Join now — seats limited'. KPI: paid joins. Sample: 700 audience visitors. Rule: ≥2% join → build community features; <0.5% → switch to free with paid upsell.

Implement event tracking that maps to the KPI you chose: button click events, funnel drop‑offs, and referral source. Use GA4 or a lightweight server event endpoint for reliability; client events are acceptable for quick signals but may miscount if users block scripts. Keep the funnel simple: view → CTA click → post‑CTA confirmation. Count unique users, not raw events, when calculating conversion rates. (validea.dev)

Ethics and transparency matter. Don’t promise features you’ll never build; if you collect refundable deposits or take payment, be ready to refund if you decide not to ship. The practice is widely accepted in product validation circles if done honestly: the goal is to measure willingness to pay, not to deceive. If you plan to take card data, explicitly state refund terms and keep the commitment small. (preuve.ai)