# K-factor

An estimate of how many additional users each existing user brings through referrals.

An estimate of how many additional users each existing user brings through referrals.

## An example

If each user sends two invitations and 10% convert, the modeled K-factor is 0.2.

## Why it matters

A simplified model can miss timing, overlap and retention. It is not proof of sustained viral growth.


## Related terms
- [Active user](https://jolts.xyz/resources/glossary/active-user)
- [Cohort](https://jolts.xyz/resources/glossary/cohort)
- [Lifetime value (LTV)](https://jolts.xyz/resources/glossary/lifetime-value)
- [Retention rate](https://jolts.xyz/resources/glossary/retention-rate)

## Next step

[Explore advertising](https://jolts.xyz/advertisers) or [monetize your AI app](https://jolts.xyz/publishers).