Viral coefficient calculator
The viral coefficient, or K-factor, is how many new users each user brings in. Above 1, every wave of new users brings a bigger wave and the product grows on its own; below 1, word of mouth fades out. Enter your starting users, invites sent per user and the share of invites that turn into users, then the number of cycles and how long one takes.
Runs in your browser: nothing you enter is sent anywhere.
How to use it
- Enter your starting users.
- Enter how many invites a user sends on average and the conversion of an invite.
- Enter the number of cycles to project and the cycle length in days.
- K = invites per user × invite conversion
- New users in cycle n = starting users × Kⁿ
- Total after n cycles = U × (1 − K^(n+1)) ÷ (1 − K), or U × (n + 1) when K = 1
- Limit when K < 1 = U ÷ (1 − K)
Worked example
1,000 users, 5 invites each, 10 % conversion, 5 cycles of 7 days:
- K = 5 × 0.10 = 0.5: fading.
- The first cycle adds 500 users, the fifth only 31.3.
- Total after 5 cycles = 1,000 × (1 − 0.5⁶) ÷ 0.5 = 1,968.8, close to the limit of 2,000, in 35 days.
With 4 invites and 30 % conversion, K = 1.2: after 3 cycles the base is 5,368 and the last cycle alone adds 1,728.
Tips
- Measure K per cohort: early users often invite more than later ones.
- Raising conversion of invites is usually easier than getting people to send more of them.
- Even K below 1 lowers acquisition cost: each paid user brings 1 ÷ (1 − K) users in total.
Frequently asked questions
How do you calculate the viral coefficient?
K = invites sent per user × conversion rate of an invite. Users sending 5 invites each, of which 10 % sign up, give K = 5 × 0.10 = 0.5.
What does a K-factor below 1 mean?
Each wave of new users is smaller than the one before, so growth from invites stops near starting users ÷ (1 − K). With 1,000 users and K = 0.5 it levels off at 2,000.
What is the viral cycle time?
The time from a user joining to the users they invite joining. With the same K, a shorter cycle grows the base much faster, because more cycles fit in the same time.
Does the model include other growth?
No. It counts only users who arrive through invites, starting from your current base, and assumes K stays the same every cycle. Paid and organic sign-ups come on top.