K-factor (viral coefficient)
K-factor, or viral coefficient, measures how many new users each existing user brings in: the average number of invitations a user sends multiplied by the rate at which those invitations convert to new users. A K of 1 means each user replaces themselves through referral; above 1 implies self-sustaining viral growth. It is a growth convention adapted from epidemiology, with the invite and conversion definitions set per product.
What this means
K-factor = (average invitations sent per user) × (conversion rate of those invitations). If each user sends 4 invites and 25% convert, K = 4 × 0.25 = 1.0, meaning each user brings in one new user. The term and the threshold-of-1 logic come from epidemiology's basic reproduction number: above 1, each 'case' produces more than one, so the population grows on its own.
Why K is often over-read
A K above 1 implies compounding growth only in an idealized, unsaturated model. In reality the addressable network saturates, invite rates decay, and conversion falls as the obvious recipients are already users — so sustained K above 1 is rare and usually temporary. K-factor also ignores cycle time (how long a viral loop takes) and churn, both of which gate real growth. The invite and conversion definitions are product-specific, so K-factor is not comparable across products and is best read as a directional, decaying signal within one product, alongside retention.
This page is educational and not legal advice.
- Invites per user × invite conversion rate
- K > 1 implies self-sustaining spread in an unsaturated model
- Ignores cycle time, saturation, and churn — read with retention
How it appears in analytics and logs
A K-factor below 1 means referral amplifies but does not sustain growth; at 1 each user reproduces once; above 1 implies compounding viral spread (in theory, before saturation). It isolates word-of-mouth from paid or organic acquisition.
Diagnostic use case
Quantify how much organic growth comes from existing users inviting new ones, to gauge whether a product spreads on its own.
What WebmasterID can help detect
WebmasterID measures first-party referral and signup events, so the invite and invite-conversion sides of K-factor can be tracked without cross-site tracking.
Common mistakes
- Treating a brief K above 1 as permanent viral growth.
- Ignoring network saturation and viral cycle time.
- Comparing K-factor across products with different invite definitions.
Privacy and accuracy notes
K-factor aggregates invite and conversion counts and needs no third-party identifiers. Invite data should follow applicable privacy rules; this page is educational, not legal advice.
Related pages
- ARPDAU (average revenue per daily active user)
ARPDAU (average revenue per daily active user) is total revenue on a day divided by that day's daily active users. It is a high-frequency monetization signal common in mobile apps and games, where revenue from ads and in-app purchases is averaged across the active base each day. Because it is daily, it reacts fast to changes — but it depends entirely on how a 'daily active user' is defined, which is a per-product convention.
- Day-N retention (D1/D7/D30)
Day-N retention measures the percentage of a user cohort that returns on a specific day after first use — D1, D7, and D30 being the common checkpoints. It is a core mobile and product retention curve. The subtlety is that 'returned on day N' has three competing definitions — classic (exactly day N), range (by day N), and rolling — which produce different numbers from the same data, so the definition must always be stated.
- New vs returning visitors
New vs returning classifies a visitor by whether the analytics tool recognizes them from a prior visit, usually via a client identifier. The split is fragile: cleared cookies, multiple devices, private browsing, and privacy-driven storage limits all make returning visitors look new. So the 'new' share is systematically overstated, and the dimension says more about identifier persistence than loyalty.
- Event Explorer
Track referral and signup events first-party.
Sources and verification notes
- Google — [GA4] Key events (conversions)Background on counting invite/signup events; K-factor is a growth convention adapted from epidemiology.
Last reviewed 2026-06-24. Facts are checked against primary/official sources where available; uncertain specifics are marked “Data not yet verified” rather than guessed.