K-factor coefficient

Measuring virality, according to Mixpanel (2014), still comes down to answering one simple but very important question: how many of your users send invites, and what percentage of those invites become new users.

The most popular method to measure viral growth of a site/app is among various blogger (Intercom, 2014; Blissdrive, 2014; ForEntrepreneurs, 2014 ) the so called “K-Factor coefficient”. The K-factor is used by companies to understand the number of new registrations created by viral channels and the associated growth in user base. The K-factor is calculated by the following formula:

Bothgunzblazing (2013) gives the following example: Let’s imagine I release a game and want to acquire new players through viral growth. Through in-game functions and features, it’s possible to add friends and send requests to players who have not yet played the game. By adding these features, if my game is well designed, the goal is that some players (we’ll refer to this set of players as Set A players) will like the game enough to use these features and will send invites to several of their real-life friends. Some of these people will try the game for themselves (Set B) and if they too like the game, will also start inviting their friends to join, some of which will be people that were not part of Set A. This cycle can potentially go on ad-infinitum, bringing lots of new players into the game.


Assuming, that each player of Set A has 10 friends which he invites to join and that 20% of the invited friends will actually sign up for the came, the K-Factor is calculated as followed:

K = 10*0,2 = 2

This means that the initial customer base of 10 people will successfully convert 2 out of 10 invitees as new users. As a result, the company has 10 starting users, plus the 20 new customers, which equals a customer base of 30.


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