Limitations of the K-coefficient

The K-factor is based on the following assumptions which limit, according to Data Community DC  its reliability to measure viral growth: 1.) The market is infinite: Since viral growth can be so explosive, the market for a product can become saturated very quickly. As the market becomes saturated, fewer potential customers will respond to invitations, effectively reducing the “viral coefficient”. Since market saturation could occur in a matter of days or weeks, the effect of a finite market size cannot be ignored. 2.) Once a customer, always a customer. The rate of customers which stop using the product is ignored. … Continue reading Limitations of the K-coefficient

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 … Continue reading K-factor coefficient