The Difference Between 891 Users and 3.2 Million Is a Decimal Point
The viral coefficient, or K-factor, is how many new users each user brings with them. A few decimals decide whether your product compounds into millions or quietly fizzles out.
Ruben Christoffer Damsgaard
Aug 29, 2026
What Is the Viral Coefficient?
The viral coefficient is the average number of users a new user brings on after they convert, e.g. after they have bought your product, or perhaps signed up on your platform. Essentially: how many friends does a new customer bring to your business. You want this number as high as possible.
The stories and examples below can help you understand the implications of different viral coefficients on a deeper level, and hopefully induce some reflection around this concept.
How many friends does a new customer bring to your business? That single number is the K-factor.
The Vape Cult of Houston
I have a friend in Houston who is infatuated with these fruity and colourful vapes. Let us call him John. He likes them so much that he has gotten all his friends and his friends' wives to buy them. After John bought his first vape, he converted 10 people from his network to the vape cult.
And if every one of his 10 friends converts 10 new customers, the viral coefficient (K-factor) will be 10. Meaning the total number of customers is now 111. If the K-factor remains at 10 for another cycle, the total customers will amount to 1,111. This compounding effect creates an exponential growth curve.
How TikTok Compounds
Let us now run through TikTok as an example. A guy named Marc decides to download TikTok. He loves it so much that he is telling his friends about the app. He gets 3 of his friends to create an account. These 3 friends share videos to their other friends that have yet to download the app.
Out of all the people the 3 users have shared videos to, 9 have gone through with a download and have created an account. In this case the average number of new users that each converted user has brought onto the app is 3, therefore the viral coefficient is 3.
A cousin in epidemiology
A similar factor called the reproduction number is used in epidemiology. In this world it is used to estimate and track how many new people an infected person infects on average, where Râ‚€ is how many people a newly infected person will transmit the disease to in a fully susceptible population.
If a person infects 2 people, and they in turn infect another 2 people each, the reproduction number is 2. It is very similar to the viral coefficient we are talking about in business and marketing.
The Threshold Is 1. The True Cause Is in the Decimals.
If the viral coefficient is K < 1, your growth will fizzle out over time. If it is K > 1, your user base will grow exponentially. And decimals matter a lot here. The math in this example will showcase this perfectly.
I will demonstrate the differences in results with a set of varying K-factors. Let us say we have a seed of 100 users that grows over 20 cycles. The first K-factor is 0.9, the second is 0.99, and the following are: 1.1, 1.3, 1.5, 1.6. Try to hold onto your chair while looking at the graph below.
The K-factor ladder
Cumulative users from 100 seeds over 20 cycles
- K = 1.60→ 3.22M users
- K = 1.50→ 997.4K users
- K = 1.30→ 82.0K users
- K = 1.10→ 6.4K users
- K = 0.99→ 1.9K users
- K = 0.90→ 891 users
Reading the Ladder
We see that a K-factor of 0.9 ends at 891 users after 20 cycles. A K-factor of 0.99 ends at 1.9K users. With a K-factor of 1.1 the user base will still end up small relative to the other K-factors, but the user count is still 7 times as large as the user base produced by K = 0.9, and the total users end up being 6.4K.
And the interesting part revealed by this K-factor ladder is that each small increase in K causes disproportionately large differences after 20 cycles. The K-factor of 1.3 gives us 82K users, and if we increment the K-factor from 1.3 to 1.5 we get close to a MILLION USERS! That took us from under 100K and almost up to 1 million.
And if we increment once again from 1.5 to 1.6, the K-factor of 1.6 over 20 cycles will give us 3.22 million users. The difference between the highest and lowest K-factor is just 0.7, yet over 20 cycles, the difference in the resulting user base is a staggering ~3.22 million! Hopefully you did not blow up from this shocking illustration of the colossal impact that small differences in the K-factor can have.
Same seed. Same 20 cycles.
891
K = 0.9
users after 20 cycles
82K
K = 1.3
users after 20 cycles
3.22M
K = 1.6
users after 20 cycles
Now you understand the viral coefficient better and you might also understand its importance in creating a viral product. The difference between a viral loop that scales exponentially and one that fizzles out and dies is a minuscule difference in the K-factor, and tiny changes in K become massive differences in result after repeated cycles.
The difference between a loop that scales exponentially and one that fizzles out is a decimal.