The 30‑Day Prelaunch Experiment Matrix: 9 Cheap Tests That Predict First‑Month Conversion & CAC

Start by understanding that each experiment gives you a measurable conversion probability for a specific step in your funnel. Multiply those step probabilities to estimate end‑to‑end conversion (visitor → buyer) and combine with per‑test acquisition cost to approximate CAC. Keep the math simple for quick decisions: use conservative lower‑bound conversion rates for your forecast.

Conservative forecasting example: if a landing page fake‑door converts 5% of targeted visitors to ‘interested’ (email or preorder clicks), and a gated demo converts 25% of those to booked demos, and demo→paid (from your concierge or sales test) converts 20%, your first‑month visitor→paid estimate = 0.05 * 0.25 * 0.20 = 0.25%. Track absolute counts (clicks, form submissions, deposits) not just percentages — noisy small‑sample signals become useful when you aggregate across tests.

Fake‑door landing page: run a targeted landing page describing the paid feature, with a clear CTA (preorder, ‘buy now’, or ‘join waitlist — pay later’). Key KPI: buyer‑action conversion rate (CTA clicks / targeted visitors) and cost per CTA. A buyer action (click to pay / deposit) correlates with raw demand — use it to estimate top‑of‑funnel conversion in your forecast. Fake‑door tests are low‑cost and give immediate probability for visitor interest.

Preorder deposit test: accept small deposits or refundable preorders. Key KPI: paid conversion rate (deposits / visitors) and average deposit size. This is the strongest single predictor of actual month‑one paying behavior — deposits reveal willingness to pay and can be scaled to estimate LTV/CAC early. Use simple payment flows (Stripe Checkout or Gumroad) to minimize friction.

Creator seeding: hand the product or landing page to 10–50 creators in your niche (micro‑influencers, newsletter writers, community leaders). KPI: engaged audience conversion (clicks → CTA) and CPC (if you buy creator placements). Creator seeding reveals organic channel viability and CAC when you convert paid placements to predictable CPM/CPC.

Gated demo + concierge sales test: run a gated demo (video + 'book a demo' CTA) and convert demo signups using a concierge onboarding process (founder‑led calls, Loom walkthroughs). KPIs: visitors→demo request %, demo→paid % using concierge sales, and time‑to‑close. This combination replicates B2B purchase motions and produces the demo→paid number you’ll use as the lower funnel conversion in your forecast.

Ad microfunnel: run small, tightly targeted ad campaigns (search or social) that point to the validation landing page or fake‑door with strong CTA. KPIs: cost per click (CPC), click→CTA conversion, and net cost per CTA. Microfunnels reveal whether paid acquisition can produce volume at acceptable CAC and allow you to estimate month‑one spend required to hit revenue targets.

Freemium proxy and pricing anchor: release a very limited freemium or free trial (or show a realistic freemium option on the landing page) while simultaneously testing anchored price points. KPIs: free‑to‑paid trial conversion and price sensitivity via anchor variants. Anchoring experiments (showing 'premium' tiers or a high anchor price) change perceived value and can materially lift paid conversion in early tests; measure conversion lift and use that delta in your revenue forecast.

Referral seed: seed a small cohort with a double‑sided referral incentive (early access + rewards). KPI: invites sent per user, invite conversion rate, and viral coefficient. Even a small viral coefficient (>0.1) reduces effective CAC — measure invite acceptance and conversion within 7–14 days to estimate uplift to month‑one paid growth.

Turning signals into a forecast: build a simple calculator that takes each experiment’s KPIs (visitor acquisition cost, step conversion rates, paid conversion probability, average order value or deposit) and outputs expected paid customers and CAC for a target traffic volume. Run two scenarios: conservative (lower‑bound per‑step rates) and aggressive (median rates). Use the conservative scenario to decide whether to build, iterate, or pause.