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Free tool

Waitlist Growth Simulator

Model viral waitlist growth with realistic decay, verification drop-off, and sensitivity bands.

Simulation parameters

Unlike simple K × i formulas, this runs cohort-by-cohort with decay and verification drop-off.

How many signups you have today. Default: 100.

Invites sent per signup × % who accept. If each signup invites 3 friends and 20% join, K = 0.6. Typical waitlist: 0.3–0.8.

How long before referred signups share again. One week (7) is a safe default.

New signups from SEO, social, ads — not from referrals. Check your last 7-day average. Default: 5.

How far ahead to project. 90 days = one quarter. Default: 90.

% of signups who confirm their email (0–1). Only verified users can refer. Industry median: 0.55. Find yours in LaunchList analytics.

Referral fatigue — K shrinks by this fraction each cycle. 0.08 means K drops ~8% per week. Use 0 if unsure (optimistic case).

Why this beats K × i

Most viral coefficient calculators blindly compound K = i × c without accounting for the three forces that kill waitlist growth in practice:

  • 1 K-factor decay: each cohort invites slightly fewer people as referral fatigue sets in. This tool lets you model that directly.
  • 2 Verification drop-off: 30–60% of signups never confirm their email, so they don't actually refer anyone.
  • 3 Organic baseline: direct traffic, SEO, and social keep adding signups independently of the viral loop.

Typical K-factor ranges

Typical consumer app0.3 – 0.7
Strong viral waitlist0.8 – 1.5
Exceptional (rare)> 1.5
SaaS median (LaunchList data)~0.09

A K > 1.0 means true viral growth; a K < 1.0 means referrals supplement organic but don't sustain growth alone.

Frequently asked questions

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