The Signal‑to‑Launch Checklist: 9 Quantifiable Prelaunch Signals That Justify Building (with Measurement Templates)

This checklist assumes you have a simple prelaunch funnel: landing page → email/waitlist → prototype/demo/checkout. Track signals against cohorts (by acquisition channel and week) so thresholds remain comparable. Run experiments for 2–4 weeks or until you hit a statistical signal; if results stay below threshold, kill or pivot.

Signals are not binary. Use them together: a single weak signal (e.g., low landing conversion but strong WTP) may still justify building if you can fix the messaging or acquisition. Conversely, multiple weak signals are a clear stop sign. The downloadable measurement templates (CSV/Sheets) store raw metrics and compute rates automatically—use them to avoid arithmetic errors and to share results with cofounders or investors.

1) Landing page to waitlist conversion rate. Measure: signups ÷ unique visitors for qualified traffic. Practical threshold: 3%+ from cold traffic or 10%+ from warm traffic; if you run targeted outreach, expect higher. A consistent conversion above these thresholds indicates your messaging and value proposition resonate enough to justify a small build. Benchmarks for landing-page conversions vary; use your channel mix when comparing to published ranges.

2) Qualified lead activation rate (signup → engaged demo or prototype click). Measure the percent of waitlist signups who take a next-step action (book demo, click demo link, open prototype) within 14 days. Threshold: 20%+ suggests true activation interest; under 10% implies signups were low-intent. Activation is a stronger signal than signups alone because it shows users are willing to engage beyond a newsletter.

3) Waitlist-to-paid commitment proxy. Before a full product, test paid commitment via preorders, refundable deposits, or paid beta seats. Measure: paid commits ÷ waitlist size. A 1–3% preorder rate on a cold list is meaningful; 5–10% on warm lists is excellent. Money is the strongest prelaunch signal.

4) Landing page bounce and time-on-page. Low conversion paired with very short average time-on-page signals messaging or clarity problems. If people stay and read but don’t convert, you likely have a pricing, value, or trust problem rather than poor messaging.

5) Direct WTP surveys — Gabor‑Granger or Van Westendorp. Implement a short pricing survey (5–7 questions) to measure acceptable pricing ranges and perceived cheap/expensive points. The Van Westendorp Price Sensitivity Meter produces “too cheap, bargain, expensive, too expensive” curves you can combine into an acceptable price band. Use a qualified sample (your waitlist or paid panel) rather than anonymous visitors for better signal quality.

6) Real-money microtransactions or refundable deposits. A microtransaction (e.g., $5 refundable deposit to join beta) converts declared interest into economic commitment. Compare conversion and refund rates: high conversion with low refunds is a positive signal; many refunds suggest buyer’s remorse or mis-specified value.

7) Tradeoff or Gabor‑Granger tests for feature-level pricing. Present respondents with price points for different bundles to estimate price elasticity and which features drive WTP. Prioritize building features that increase WTP more than their engineering cost to accelerate unit economics toward viability.

8) Short-term retention proxy. True retention requires product usage, but prelaunch proxies work: repeat demo opens, returning visitors, or multi-day engagement with a prototype. Measure: percent of users who return at least once within 7 days after their first demo interaction. Threshold: 15–25% returning within 7 days suggests sticky value for an early MVP; lower requires probing whether your core experience is valuable or discoverable.

9) Referral rate and viral coefficient (K‑factor). Measure average invites per engaged user (i) and invite conversion rate (c). Viral coefficient K = i * c. If K ≥ 0.3 you have a shareable product; K ≥1 means organic exponential growth is possible. For prelaunch, aim for K ≥0.1 as an initial positive sign and K ≥0.3 to justify viral-focused scaling experiments.

Putting retention and viral signals together tells you whether early users will stick and invite others; both are essential for growth and lowering CAC.