K-Factor

K-factor measures how many new users each existing user generates. K = i × c, where i is the average number of invitations (or shares, exposures) a user sends and c is the rate at which those convert into users. K = 2 means each user brings two more — exponential growth. K = 0.4 means each hundred users recruit forty, who recruit sixteen — a boost on top of other channels, decaying geometrically rather than compounding.

The threshold is 1, exactly as in the epidemiology it borrows from: above it, spread self-sustains; below, it dies out without external acquisition. Cycle time matters as much as K — a K of 1.2 with a two-day invite cycle beats a K of 1.5 that takes two months. Measure K per loop (referral program, content sharing, collaboration invites each have their own), instrument both i and c separately (they're different levers), and expect decay: early-adopter enthusiasm overstates steady-state K every time.

K matters for products with a genuine sharing mechanic — collaboration tools (the invite IS usage), referral-incentive programs, content platforms. Sustained K above 1 is vanishingly rare (the famous cases — early Hotmail, Dropbox referrals, viral-era Facebook games — are famous because they're rare); the practical use is engineering K upward as a CAC subsidy: a K of 0.5 halves your effective acquisition cost, since every paid user brings half a free one.

Borrowed from epidemiology, where the k/R factor describes how many people each infected person infects — the >1 spread threshold transfers intact. Adopted into tech vocabulary by 2000s social-gaming and viral-marketing practitioners (Facebook-platform era), with the growth literature (Chen, Skok, Ellis) standardizing the i × c formulation.

Etymology: source.

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