Time to Go Viral : K-factor

Do you remember how Angela Merkel explained coronavirus spread by pointing out a virus’s actual transmission rate at a given time? She told: “ If the spread rate edges up further to 1.2, everyone is infecting 20% more.” I know you are wondering why I started with Merkel’s quote to this post? Because the ratio Merkel mentioned is also very popular in app marketing, and we call this ratio “K-factor”.

Let’s say your cohort size is 1000 on Day 0 .When you check your active users on Day 7, the total number is now 1200. You haven’t gotten in touch with those 200 people directly, they contacted by your existing users. That makes those 200 people your viral growth and your K factor 1.2 .

So K-factor is…

K-factor is a metric that shows how many additional users each of your existing users brings along to the app without you paying for those additional users. K-factor is practically the level of mobile game distribution and its spread.

K-factor may have different values, we can categorize them as below:

K < 0 No viral. User base is declining.

K = 0 No viral. User base is steady.

K > 0 Viral. User base is growing.

Even though the example above misleads you about the easiness of K-factor calculations, in reality it is not. Because virality has many factors both in online and offline environments. What mainly triggers viral growth for your apps are:

  • App invites
  • Word of Mouth(friend recommendation, chat groups…)
  • Social Media(social post, influencer …)
  • Self-Discovery(top charts, featured games…)

Measuring K-factor

There are multiple ways for calculating K-factor. One of the most common ones is calculating it based on invitation metrics:

K-factor = Nr. of invites sent to each customer(i) x conversion rate of each invite(c)

Let’s assume that each user sends the invitation to 10 friends (i = 5), and on average, 30% of people only who received an invitation are successfully registered (c = 30%). In this case, the k-factor = 5 * 0.3 = 1.5. Let’s compare different i’s and c’s in the below chart.

Check out the table above, K-factor is based on solo invitations. For instance, according to case 1 if the product has 100 active users, then in the next period they would be 250 in a perfect world. Even though this model is suitable for comparing different campaigns under a channel, it has limitations if you consider the model for a bigger scale. Although you are able to track all invitations and registration through them properly, you can’t tell 100% of new unpaid registrations came through invitations. As I mentioned above, there are other factors that trigger virality such as word of mouth, social media, self discovery. But how can you track virality for all factors?

Following formula scales k factor more comprehensively:

K-factor = O(n) (organic downloads per period n) / T(n-1) (active users per period n-1)

This formula takes into account all kinds of invitations whether it sourced by an online invitation or chatting. K- factor is the proportion of users came through organic(unpaid) channels in period n to total active users in the previous period. It is useful for comparing larger scales such as locations and channels. Yet, it requires a cautious mind in this calculation. Because unpaid uplift on the period n may include re-installs or the installs couldn’t be tracked in the period n-1.

Important footnote is, this model doesn’t have that “plus one”, so, if the project has no organic downloads, k-factor will be equal to zero. Some developers and marketers compare this K-factor with churn. The main logic is that the virality of your app should cover the churn of users. Otherwise, more users leave than users come and user base will decline by the time.

The shorter viral cycle, the higher k-factor

I talked about how to improve K-factor in my previous post under the title “Improving LTV”. Therefore, you can check solutions for better K-factor there. The main point is: design your game and retain your user by reducing your viral cycle. The earlier your users share your game, the more viral audience grows in the same period.

Conclusion:

K-factor is important. It lowers your overall real CPI. When viral users converts IAP or they are spending more than the regular players, your LTV will increase as well. In overall, understanding your K-factor also gives you greater insight about the effectiveness of the sharing capabilities of your app. This insight helps you find out what kind of reward your existing users expect in order to share it with others and what kind of incentives others need to install it. When you are able to spread your app through unpaid channels and learn to retain these new users, your revenue metrics will grow.

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