What does your K actually compound into?
Two inputs give you K. The chart shows what it means over eight referral cycles — and the inverse mode tells you what K a growth target would need. No signup, all in your browser.
Your numbers
With 14-day cycles, that target needs K ≈ 0.05 — which your current K already clears.
Below 1, referrals multiply every acquired user by 1 ÷ (1 − K) over time — free amplification on everything else you do.
Solid line: your starting cohort plus every referral generation it produces. Dashed: the same cohort with no referrals. Cycle length matters as much as K — a K of 0.5 every two weeks beats a K of 0.8 every quarter.
Most healthy B2B products live well below K = 1 and still profit enormously from referrals — the point of the multiplier, not the myth of "going viral". The definitions behind this tool live on viral coefficient and what is virality.
How do you calculate viral coefficient?
Multiply the average invites each user sends by the share of invites that become users. Three invites at a 15% conversion is K = 0.45: every hundred customers bring forty-five more, who bring twenty more, and so on. The calculator runs the compounding so you can see the series, not just the coefficient.
Both inputs are measurable from a live program: invites sent ÷ active users, and referred signups ÷ invites sent. Resist estimating them from intent surveys — stated willingness to invite runs several times higher than observed inviting, everywhere.
Why does cycle time matter as much as K?
Because compounding is per cycle, not per month. A K of 0.5 that plays out in two weeks produces far more growth in a quarter than a K of 0.8 that takes three months per generation. When you tune a program, shortening the join-to-invite gap is often cheaper than raising conversion.
How should you use the inverse mode?
Set the monthly growth you'd want from referrals alone and read off the K it requires at your cycle length. Its real job is expectation-setting: seeing that 10% monthly needs K ≈ 0.2 at two-week cycles turns 'go viral' into two concrete levers — invites per user, and invite conversion.
What counts as a good K, why almost nothing sustains K above 1, and how the coefficient relates to virality generally are covered on the reference pages linked below — this page just does the arithmetic honestly.
Is this viral coefficient calculator free?
Yes — free, no signup, all computed in your browser.
What is a good viral coefficient?
Most healthy B2B products sit well below 1 and still profit enormously: at K = 0.4, referrals multiply every acquired customer by about 1.7× over time at zero media cost. Sustained K above 1 is essentially confined to communication products where inviting is the product.
Why does the chart flatten when K is below 1?
Each referral generation is smaller than the last — 1,000 users bring 450, who bring ~200, and the series converges to the starting cohort × 1 ÷ (1 − K). That ceiling is the honest way to read sub-1 virality: amplification, not perpetual motion.
K is an output. The inputs are ask, offer and timing.
ReferralFlo raises invites-per-user and invite conversion with personal links, well-timed asks and rewards that fit your margins — the levers this calculator just showed you.