Viral coefficient / k-factor
Multiplier on all growth (retention gates it).
- Formula
- Invites per user x invite conversion rate
- Unit
- ratio
- Models
- All models
| good | 0.15×–0.25× | Andrew Chen; Saxifrage |
| great | 0.4× | Andrew Chen; Saxifrage |
| outstanding | 0.7× | Andrew Chen; Saxifrage |
| essentially unrealistic | k > 1.0 is essentially unrealistic for sustained periods | Andrew Chen; Saxifrage |
What it is
Viral coefficient (k-factor) measures how many new users each existing user generates through organic referral or invitation. It is the product of invites sent per user multiplied by the conversion rate of those invites.
How to calculate it
Multiply the average number of invitations sent by an existing user by the fraction of those invitations that convert to new active users. For example, if each user invites 5 people and 10% join, k = 0.5. Track this on a cohort basis and over time, as virality commonly decays as early adopters exhaust their high-affinity networks.
Why it matters
The viral coefficient determines whether organic word-of-mouth amplifies paid acquisition (k < 1) or can sustain growth independently (k approaching 1). Even a k of 0.25 materially lowers effective CAC by extending each acquired user into additional sign-ups. A k above 1 would imply exponential user growth from a fixed seed — mathematically possible in short bursts but not sustainable at scale.
Benchmarks & pitfalls
Per Andrew Chen and Saxifrage, a k-factor of 0.15–0.25 is considered good, ~0.4 great, and ~0.7 outstanding. A k above 1.0 is described as essentially unrealistic for sustained periods. These are directional practitioner heuristics, not figures from a rigorous empirical study. Measurement ambiguity is significant: ensure invitations are tracked at the invite-delivery level and conversions attributed correctly to referral vs. organic search. Also distinguish between genuine referral virality and incentivized referral programs — the latter can inflate k temporarily while masking weaker organic signals.