Calculate your product's K-factor and model exponential user growth over viral cycles. Find out whether you're viral (K>1), linear (K=1), or sub-viral (K<1) — free, no sign-up.
Calculator
Viral coefficient
0.75K-factor
Sub-viral (K < 1) Sub-viral growth — 3.5K total users after 6 cycles (42 days).
Cumulative users
3.5K
New users (last cycle)
178
Total time
42 days
How the viral coefficient works
The viral coefficient K (also called K-factor) measures how many new users each existing user generates in a single viral cycle. It's calculated as: K = invites per user × conversion rate. A K greater than 1 means each generation of users produces a bigger generation — classic exponential growth. K equal to 1 is linear (each cycle adds the same number). K less than 1 means growth is decelerating.
Cumulative users after n cycles is the sum of a geometric series: starting users × (K^(n+1) − 1) / (K − 1) for K ≠ 1, or simply starting users × (n+1) when K = 1. The cycle time tells you how quickly compounding happens — a 7-day cycle can lead to dramatic growth within months, while a 30-day cycle spreads the same K across a year.
Even a small improvement in invites per user or conversion rate can push K from sub-viral to viral. Increasing conversion from 30% to 35% while sending 3 invites moves K from 0.9 (shrinking) to 1.05 (growing). That threshold between sub-viral and viral is where growth teams focus their experimentation.
Frequently asked questions
What is a good viral coefficient?
Any K above 1.0 means your product grows on its own — each cohort of users produces a larger cohort. K between 0.5 and 1.0 is sub-viral but acquisition can still work with paid channels on top. K above 2.0 is exceptional and usually short-lived in practice. Most successful consumer apps sustain K between 1.0 and 1.5 during peak growth phases.
What counts as one viral cycle?
A cycle is the time from when a new user joins to when their invited friends complete sign-up. For a mobile app this might be 7–14 days; for a B2B referral program it could be 30–60 days. Shorter cycles compound faster — the same K of 1.2 produces very different user counts at 7 days vs 30 days per cycle.
How do I increase my K-factor?
There are only two levers: invites per user and conversion rate. To increase invites, make sharing a natural part of the product (e.g. invite to collaborate, share a result, unlock a feature). To increase conversion, reduce friction in the invite link — a personalised landing page from a trusted friend converts far better than a generic sign-up page.
Is viral growth sustainable long-term?
Pure viral growth always slows as the addressable market saturates — you run out of new people to invite. In practice, virality is most powerful early, when network effects are still expanding. Mature products combine a K-factor close to 1 (retention + referral) with paid or content acquisition to sustain overall growth.
Exponential reach curve
Cumulative users: 3.5K
Show data table
Exponential reach curve
Cumulative users
Cycle 0
1K
Cycle 1
1.8K
Cycle 2
2.3K
Cycle 3
2.7K
Cycle 4
3.1K
Cycle 5
3.3K
Cycle 6
3.5K
Results are estimates. Verify with a professional for important decisions.
About this calculator
This calculator measures how fast your product or campaign spreads on its own. Enter how many users you start with, how many invites each sends, and what share of invitees actually sign up — and the tool projects your total user count across multiple viral cycles.
How to read your results
The headline figure is your total cumulative users after all the cycles you modelled. The verdict label tells you at a glance whether growth is viral (K > 1 — each cohort is larger than the last, producing exponential growth), linear (K = 1 — each cycle adds exactly the same number of users), or sub-viral (K < 1 — each wave is smaller than the one before). Watch the cycle-by-cycle chart: a K above 1 produces a curve that bends sharply upward.
Worked example
Start with 1,000 users. Each user sends 5 invites, and 30% of invitees convert. Run 4 cycles of 7 days each.
K = 5 × 0.30 = 1.5, so the product is viral. Cumulative users reach roughly 13,188 over 28 days — growing from 1,000 to 2,500 after cycle 1, 4,750 after cycle 2, 8,125 after cycle 3, and 13,188 after cycle 4.
Frequently asked questions
What is the viral coefficient (K-factor)?
The viral coefficient K is the average number of new users each existing user generates in one cycle. It equals invites per user multiplied by the conversion rate. A K above 1 means every cohort is larger than the last, producing compounding, exponential growth without additional ad spend.
What counts as a viral cycle?
A viral cycle is the time it takes for a new user to sign up, use the product, and send their own invites. Typical cycles range from a few days (social apps) to a few weeks (enterprise tools). Shorter cycles compress the timeline and make K above 1 far more powerful.
How do I improve my K-factor?
You can raise K by increasing either invites per user or the conversion rate. Tactics include frictionless in-product share flows, referral incentives, and a strong landing page that converts invite clicks into sign-ups. Even moving conversion rate from 20% to 30% with three invites per user pushes K from 0.6 to 0.9 — a large jump toward viral.
Does this model account for churn or market saturation?
No — this is a pure K-factor projection. It does not deduct churned users and does not cap growth at total addressable market size. Real-world growth decelerates as you reach saturation, so treat large later-cycle numbers as an upper bound rather than a forecast.
Can K stay above 1 forever?
In theory, yes; in practice, no. Conversion rates fall as you exhaust easy-to-reach audiences, and existing users become less likely to invite people they know once most of their network has already signed up. Sustainable viral loops are rare — most products land somewhere between sub-viral and 1.2.
How it's calculated
The viral coefficient is K = invitesPerUser × (conversionRate / 100). Cumulative users after n cycles is the geometric series sum: startingUsers × (K^(n+1) − 1) / (K − 1) when K ≠ 1, or startingUsers × (n + 1) when K = 1 exactly. Each term startingUsers × K^i represents the new cohort added in cycle i. When K > 1 this series grows exponentially; when K < 1 it converges to a finite total. Formula derivation follows standard viral-loop literature (Elman, Ellis, and references cited in the source code).
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