How does the viral growth simulator work?
Borrowed from Epidemiology: SIR model, K = i × c
Epidemiologists use the reproduction number to predict whether a disease spreads or dies out. The K-factor is the same idea applied to invitations: each user is a carrier, and each invite is a possible infection.
When K is below 1, total users settle at the starting users divided by one minus K. When K reaches 1 or more, there is no natural ceiling until the market runs out.
Worked example
A US SaaS app starts with 1,000 users. Each user sends 2 invites and 20% of invites convert, so K = 2 × 0.20 = 0.40. Total users settle at 1,000 ÷ 0.60 = 1,667, which means referrals add about 667 users.
How should I read my result?
Referrals add a small lift. Treat them as a bonus and invest in paid or organic acquisition.
Referrals meaningfully cut acquisition cost. Improve invite conversion first, since it is usually easier than sending more invites.
Growth compounds until the market saturates. Check retention, because viral growth without retention inflates and then collapses.
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Questions about viral growth simulator
What is a good K-factor?
Most products sit between 0.1 and 0.5. A K above 1 is rare and usually short-lived.
How do I measure invites per user?
Divide total invites sent in a period by the number of active users in the same period.
Does K include the time each cycle takes?
No. The cycle time decides how fast growth happens, while K decides whether it compounds at all.