K factor can be complicated if you let it. We want to keep this metric very simple in this discussion but we will expand on it later. To define the concept of k factor - it’s the viral growth rate of users converting other non users into users. A k factor of .5 means every 2 users will convert another 1 user. Conceptually you can see this as an offset for the churn rate. A portion of users will be leaving on a monthly basis, but the k factor offsets this number by adding new users. I always urge people to understand that k factor will only in .01% of projects provide the viral growth to sustain a project without further marketing. Experiencing an exponential K factor is nearly always out of your control and it leads to poor expectations to expect one.
K Factor is a very old metric from traditional marketing theory. The formula is;
number of invites by users * percent conversion of each invite = k factor
Based on the formula you can see the optimization points that I’ll cover here****. Having seen social gaming performance metrics you could expect the following stats from an unoptimized k factor. You can expect the average user to (actively or passively) send from 1 – 15 invites with conversion rates at around 3% – 5%. These simple numbers can create a k factor for a specific user as low as 3% (1 invite * 3% conversion rate) and as high as 60% (12 invites *5% conversion rate).
One important note to mention about invites sent by users – many of the “invites” may be passive functions that aren’t deliberate. How often do you see your friends playing Candy Crush on Facebook with their score automatically shared? This is an example of a passive user invitation. I actually consider any reference to your product to be an invitation. Don’t think that a user has to be a zealous advocate for your product in order to have them have a wide reaching testimonial.