What is a K-factor for a waitlist?

K-factor (viral coefficient) measures how many new signups each existing signup generates. It equals the number of invites sent per user multiplied by the percentage who accept. A K-factor above 1.0 means the waitlist grows on its own; below 1.0 means referrals supplement but don't sustain growth. Most waitlists have a K-factor between 0.1 and 0.8.

How do I calculate my waitlist K-factor?

Divide the number of signups that came through referral links by the total number of verified signups who had a referral link to share. For example, if 100 verified users shared links and generated 60 new signups, your K-factor is 0.6. LaunchList shows this in your waitlist analytics dashboard.

Why does the simulator show lower numbers than a simple K × signups calculation?

Simple K-factor calculators assume 100% email verification and no referral fatigue. This simulator applies a verification rate (typically 40–70% of signups confirm their email) and K-factor decay per cycle (each cohort invites slightly fewer people over time). The result is a more realistic projection with a ±30% sensitivity band.

What is K-factor decay?

K-factor decay is the reduction in referral effectiveness each cycle as word-of-mouth enthusiasm fades. A decay of 0.08 means the K-factor drops by 8% each referral cycle. Most waitlists experience 5–15% decay per cycle. Setting decay to 0 gives the optimistic upper-bound scenario.

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.

Starting signups

How many signups you have today. Default: 100.

K-factor

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

Cycle length (days)

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

Organic signups / day

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

Time horizon (days)

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

Verification rate

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

K-factor decay / cycle

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.

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