The viral coefficient (k-factor) is how many new users each user brings. The formula, a worked example, what k below 1 adds and how to measure it honestly.
By Sergey BruhPublished 8 min read
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Start the free courseThe viral coefficient, or k-factor, is the average number of new users each user brings in through invitations: invitations sent per user × the share of invitations that turn into new users. If every user sends 2 invitations and 15% of them become new accounts, . Above 1, each generation of users is bigger than the last and the product grows on its own. Below 1, which is where almost every product lives, invitations can't drive growth alone, but they multiply every other channel by . The number is only meaningful with a fixed time window, and only when it counts new people. Here's how to calculate it, a worked example on real-looking data, and the four ways teams overstate it.
The k-factor depends on three choices:
The two factors are worth tracking separately: invitations per user tell you whether people want to share; invitation conversion tells you whether the invitation experience works for the person receiving it.
Halves is a bill-splitting app. Every group needs members, so sharing is built into the product: you create a group for the flat or the trip and invite the others. Here are the 2026 cohorts, everything counted within 30 days of each user's sign-up:
The working for January:
A dashboard that divides all accepted invitations by users gets for January. It's a third too high, because 487 of those accepts came from people who already had the app and joined another group.
With k = 0.31, Halves isn't viral in the strict sense: each generation of invited users is under a third the size of the one before, and the chain fades out. But it doesn't fade to nothing. Pool the five cohorts from January to May: . Now follow a real seed: the 2,169 users that Halves bought through its two paid channels in May 2026 (1,478 from paid social and 691 from store search ads).
Each row is the one above times 0.3109: , , and so on. That's a geometric series, and it has a short formula:
So the May seed grows by about 978 invited users: a multiplier of roughly 1.45 on every user a channel brings in. Before you turn that into a bigger ad budget, check it: don't credit invitations in your bids until a holdout shows they bring people who wouldn't have come anyway. Once one does, invited users can lower your effective acquisition cost; the post on the LTV to CAC ratio shows where that fits in judging a channel.
Take a hypothetical k of 1.2 and 100 seed users. Generations grow instead of shrinking: 120, 144, 173, 207 and so on. That's the exponential curve people mean by "going viral". In practice it rarely lasts: invitations reach friends of friends who already have the product or don't need it, conversion falls, and k drifts below 1. Treat a k above 1 as a phase to measure closely, not a permanent state to plan a budget around.
The second number is viral cycle time: how long from a user joining to the people they invited joining. At Halves the median was 6 days for January–May cohorts and 3 days in June.
The January example: 0.42 instead of 0.31. Existing users joining more groups keep groups alive, so report them next to k as an engagement metric, not inside it.
A cohort from last year has had twelve months to invite; this month's has had days. Fix the window, and use the same one for every cohort.
June's k is 0.55: trip groups form for a holiday and pull in friends within 3 days. January to May give 0.29–0.33. An investor deck that quotes 0.55 as "our k" promises growth that autumn won't deliver. Report k by cohort and season.
An invitation link gets the credit for a sign-up, but some invited friends would have found the app anyway, from a store search or a conversation. That's the gap between attribution and incrementality. To measure it, hold invitations back for a small random share of new groups (say, hide the invite prompt for 5%) and compare sign-ups. If half of the invited users would have come anyway, invitations are worth half of what k suggests. It's the same logic as any experiment: correlation isn't causation until you've compared against a group without the treatment.
k isn't only an investor number. It feeds two everyday decisions:
Invited users also need to find value quickly, or they leave as fast as they came; the post on activation rate shows how to check whether newcomers get to that moment.
Anything above 0 helps, because it lowers your effective acquisition cost, and most products measured honestly sit well below 1. A sustained k above 1 is rare and usually temporary; don't build a plan that needs it.
Work on the two factors separately. More invitations per user come from making sharing part of the job the product does (a group that needs its members). Better conversion comes from what the invited person sees: a clear reason to join and a quick path to value without a long sign-up.
No. k counts sign-ups you can trace to an invitation within a window. Word of mouth without a link isn't in it, which is one reason a holdout test is the fairer way to measure the full effect of sharing.
Per user is standard and fits the formula. For a group product it also helps to watch how many members a new group reaches in its first weeks: that's where invitations turn into value.
The free course Product Analytics: From Data to Decisions covers this in Module 8: measure k honestly on the Halves invite data, forecast active users from cohorts including the invited users a smaller paid budget would also cost, and judge channels, attribution and incrementality. For the bigger picture, start with what product analytics is.
