SlipItIn/.github/skills/revenue-centric-design/references/metrics-and-experimentation.md

# Metrics, Experimentation & Business Math

> Curated, distilled wisdom from @richardrx ("Richard — Design for startups"), translated from Portuguese. Each entry is a reusable principle linked to its source post.

## Don't mistake signups for traction
**Principle.** Signups are the cheapest action a user takes, so they measure curiosity, not value — especially in freemium. Real growth is whether people come back and do the action that delivers value.
**Apply when.** A rising signup curve on the dashboard feels like proof of traction, particularly under a freemium model.
**The move.** Treat signups as top-of-funnel only — never stop reading there. Track who returns on day 2 and day 7 (D1/D7 retention) and how many complete the value-delivering action (activation). Paid media can inflate signups while real retention stays flat.
**Voice.** "The signup curve climbs with paid traffic, but usage and activation only climb with a good product."
**Source.** [@richardrx · 2026-06-11](https://x.com/richardrx/status/2065082771987394651)

## Don't bet your product on an underpowered A/B test
**Principle.** Most A/B tests in small SaaS lack the volume to prove anything, yet founders swap the whole product on the result. Testing without enough sample to conclude is the trap.
**Apply when.** You're in traction or survival stage, ran a test for a week, saw "variant B won by 12%," and want to ship it everywhere.
**The move.** Before running, compute the minimum sample size (free calculators exist); if you can't hit that floor in reasonable time, don't start. Test big things (headline, offer, pricing structure, onboarding) since large effects need less sample. Never stop a test because the number looked pretty mid-way. With no volume, decide by qualitative research — five good interviews beat an underpowered A/B test. Beware the law of small numbers and confirmation bias.
**Evidence.** ProfitWell is categorical: don't A/B test price — you'll never have the volume or context for it to mean anything.
**Source.** [@richardrx · 2026-06-01](https://x.com/richardrx/status/2061463480868229189)

## Celebrate signal quality, not list size
**Principle.** A waitlist exists to validate that a pain is one people pay to solve — not to sell. Absolute size is a vanity metric; conversion-weighted quality is the real signal.
**Apply when.** You launch a waitlist and feel tempted to celebrate raw headcount.
**The move.** Convert size to expected customers before reacting: a good waitlist converts 15–20% to paying, above 30% is excellent. 53 people at 20% = 10 customers; 1,000 people at 1% = 10 customers — same result, different perception. Until there's a transaction, there's no validated hypothesis.
**Voice.** "Founders celebrate the size of the list when they should celebrate the quality of the signal."
**Source.** [@richardrx · 2026-04-17](https://x.com/richardrx/status/2045094511106220220)

## Translate churn points into LTV, not percentages
**Principle.** Most people watch churn %, but few compute what each point costs in accumulated LTV over 12 months. Cutting churn is a cash lever that needs no price hike or new acquisition.
**Apply when.** You're staring at a churn percentage and treating it as a vanity number rather than money.
**The move.** Do the churn→LTV math: at 25% monthly churn on 1,000 users you must add 250 new users/month just to break even — kill paid traffic and the product dies in ~4 months. Then improve retention without Figma: define the Aha Moment, measure time-to-value (TTV) from signup to it, ask "how do I deliver this faster?", break it into micro-wins if you can't, then test, measure, repeat.
**Evidence.** Finance SaaS, ARPU R$120: cutting churn 5 points (25%→20%) is +R$72,000/year in cash, with no price change and no extra acquisition.
**Source.** [@richardrx · 2026-04-06](https://x.com/richardrx/status/2041184077106004289)