Viral coefficient
The average number of new users one existing user creates through invites: invites sent per user multiplied by invite conversion rate.
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What it is
The viral coefficient, often called K, estimates how many new users one existing user produces through invitations in one cycle. If each user sends four invites and 20% of recipients become users, K is 0.8. A K above 1 means each cohort can produce a larger next cohort through that loop alone. The textbook case is Hotmail in 1996, which appended 'PS: I love you. Get your free email at Hotmail' to every message users sent, turning ordinary email into the invite.
Use it to diagnose a referral or sharing loop. Split K into its two levers, invites per eligible user and conversion per invite, then track the time from one signup to the referred signup. The same K compounds very differently over a two-day cycle and a two-month cycle.
Gotcha: K is not total product growth. Churn, paid acquisition, organic traffic, repeat invitations, market saturation, and blocked or ignored messages all sit outside the simple multiplication. Calculate it by cohort and count activated referred users, not link clicks.
Ask AI for it
Create a Google Sheets cohort model for our viral coefficient using [eligible users], [invites sent], [unique recipients], [activated referred users], and [median cycle time]. Calculate invites per eligible user, activation rate per unique recipient, and K as their product. Project 12 cycles for K values of 0.5, 0.8, 1.0, and 1.2 with separate cells for starting cohort and cycle length. Return the exact sheet layout and formulas, flag duplicate recipients and self-referrals, and chart referred activations by cohort. Do not mix paid or organic users into the referred-user numerator.