Find your K-factor (invites per user × invite conversion rate) or measure it from real referral data, then see how your viral cycle time turns it into user growth, cycle by cycle.
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Virality
I know
Average across all new users, including those who invite nobody
%
Share of invites that become active users
Users active during the measured period
Splits K into i and c
Projection
days
Time from a user joining to their invitees joining
Optional: users from other channels
Enter your values to see the result.
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How to use the Viral Coefficient Calculator
Choose your inputs: average invites per user and invite conversion rate, or measured users, invites and referral sign-ups.
Enter your starting users and the viral cycle time in days.
Add organic users per cycle if other channels keep bringing people in.
Set a target and start date to see when you would reach it.
Press Calculate K-factor and review the projection table and chart.
Viral coefficient formula
K = i × c, where i is the average invites sent per user and c is the invite conversion rate.
Measured K = new users from invites ÷ users who could invite in the same period.
New users in cycle n = new users in cycle n − 1 × K (+ any organic users).
Amplification when K < 1 = 1 ÷ (1 − K), the total users each acquired user becomes.
Example: users send 4 invites on average and 20% convert, so K = 4 × 0.20 = 0.8. Starting from 1,000 users, the next cycle brings 800, then 640, then 512, and the chain levels off near 1,000 ÷ (1 − 0.8) = 5,000 users. Raise invites to 6 and K becomes 1.2: cohorts now grow 20% every cycle instead of shrinking.
How K-factor and cycle time combine
K-factor
Users per acquired user
Cohort growth per cycle
What it means
0.2
1.25
−80%
Small boost to paid and organic channels
0.5
2
−50%
Every user acquired brings one more over time
0.8
5
−20%
Strong word of mouth, still needs other channels
1.0
Unlimited (linear)
0%
Each cohort exactly replaces itself
1.2
Unlimited (exponential)
+20%
Self-sustaining growth until the market saturates
Cycle time decides how quickly these effects play out. At K = 1.2, cohorts double about every 3.8 cycles: that is under 4 weeks with a 7-day cycle but over 3 months with a 30-day cycle.
Ways to raise your K-factor
More invites per user: prompt sharing at moments of success, make invites part of the core workflow (shared docs, team spaces) and support several channels.
Higher conversion: personalised invite messages, a landing page that shows who invited them, and a short sign-up flow.
Shorter cycle time: ask for invites early in onboarding and send reminders for unaccepted invites.
Two-sided rewards: referral credits for both the inviter and the new user often lift both i and c.
Frequently asked questions
What is the viral coefficient (K-factor)?
The viral coefficient, or K-factor, is the number of new users each existing user brings in. It equals the average number of invites each user sends multiplied by the share of those invites that convert. A K-factor of 0.5 means every 10 users bring 5 more.
What is a good K-factor?
A K-factor above 1 means growth feeds itself: each group of new users recruits a larger group, so you grow without paying for every user. Very few products stay above 1 for long. A K-factor between about 0.2 and 0.9 is still valuable because it multiplies every user you acquire through other channels.
What does K below 1 mean for growth?
With K below 1 the invite chain fades out, but it still adds users. The total each acquired user eventually becomes is 1 ÷ (1 − K). At K = 0.5 every user turns into 2, and at K = 0.8 into 5, which lowers your effective acquisition cost by the same factor.
What is viral cycle time and why does it matter?
Viral cycle time is how long it takes from a user joining to the people they invite joining. Two products with the same K-factor grow very differently if one has a 2-day cycle and the other a 30-day cycle, because the shorter cycle compounds many more times in the same period.
How do I measure my K-factor from real data?
Pick a period, count the users who could invite during it, and count the new users who signed up through their invites or referral links. K = referral sign-ups ÷ inviting users. If you also track invites sent, you can split K into invites per user and conversion rate to see which one to improve.
Why does a projection with K above 1 grow so fast?
Because each cohort is K times the previous one, the model is exponential. In reality K drops as you saturate your audience and early adopters run out of friends to invite, so treat long projections as an upper bound, not a forecast.
Are all the tools on FreeToolCabin tested for quality and accuracy?
Yes! Every tool on our site undergoes automated Q/A testing in real browser environments to ensure high precision, correct output, and zero errors. If you happen to spot a bug or have a suggestion, please contact us at [email protected] and our team will review it right away.