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Think Big. Move Fast.

Last week Andrew Chen wrote an excellent post about the growth and potential decay of viral apps. Rather than just focusing on the elements of viral growth, Andrew also took into account the declining likelihood of an accepted invitation as you saturate a population, and the impact of churn. He provided a useful model to social media founders who are trying to estimate their growth, and what can go wrong when a viral app “jumps the shark”:

shark fin

He notes:

* Early on, the growth of the curve is carried by the invitations
* However, over time the invitations start to slow down as you hit network saturation
* The retention coefficient affects your system by creating a “lagging indicator” on your acquisition – if you have good retention, even as your invites slow down, you won’t feel it as much
* If your retention sucks, then look out: The new invites can’t sustain the growth, and you end up with a rather dire “shark fin.”

I think this is a very useful model, but that it doesn’t quite predict what we typically see in real life. Rather than dropping to zero, failed viral apps typically hover at a steady level much lower than their peak. Since Andrew made the model available under “copyleft”, I made a small edit to his model. Rather than treating churn as a constant percentage of users in each time period, I treated it on a cohort level, with a higher churn rate in the early periods and lower churn as time goes on. This is similar to the churn profiles seen for subscriptions businesses such as AOL’s ISP business. (I was at AOL from 2002-2005 as SVP of Corporate Development, and then as GM of Netscape.) This model better matches active user graphs that we typically see for failed viral apps.

churn by cohort

If you’re interested, the model is available for download here. Viral growth assumptions are in the yellow cells on the “viral acquisition” tab and churn assumptions and output are on the “user retention” tab.