K-factor

K-factor is a measure of how many new users each new signup brings in through invites. Reforge defines it as invites sent per new user multiplied by the percentage of those invites that convert to new customers. It is also called the viral coefficient, and it shows how efficient an invite loop is and where people drop out.

How it is measured

Reforge gives the formula: "The K-Factor is simply the number of invites sent per new user multiplied by the percentage of the invites that convert to new customers."

K = (invites sent ÷ new users in the cohort) × (invites that converted ÷ invites sent)

Reading it is simple. As Reforge puts it, "every new signup will produce a K number of new users." The term is "commonly referred to as the viral co-efficient," so expect both names in dashboards and decks.

A worked case

Take a team scheduling tool where each new signup can invite coworkers.

1. Pick a cohort, such as everyone who signed up last week.
2. Count the invites that cohort sent. Divide by the cohort size to get invites per new user.
3. Count how many of those invites became new customers. Divide by invites sent to get the conversion share.
4. Multiply the two results.

You now have two levers. If few invites go out, work on the prompt, its placement or its timing. If invites go out but few convert, work on the invite message and the landing page the invitee sees. Reforge notes that the number "highlights the loop's efficiency and potential areas of loss, from the invitation process to the final conversion."

Usual mistakes

• Reporting one blended number. Keep the two inputs separate so you know which step is leaking.
• Mixing cohorts. Match invites and conversions to the same signup group, or the ratio drifts.
• Counting signups as conversions when you mean customers. Pick one definition and keep it.
• Ignoring time. Invites keep converting for a while, so compare cohorts over the same window.

When it matters

K-factor matters most if your product gets better when coworkers or friends join, and you are deciding whether to invest in an invite flow this quarter. Run each change to the prompt or message as a test. Size it with the A/B test sample size calculator, log it in the growth experiment backlog template, and see the referral program playbook and growth loops explained for loop design.