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Median # of tweets = 1 June 3, 2009

Posted by jeremyliew in retention, twitter, viral.

Fascinating study in the Harvard Business Review about twitter. It looks at 300,000 users and covers differences in behavior between men and women, # of followers and # following. But most interestingly, it looks at usage:

Twitter’s usage patterns are also very different from a typical on-line social network. A typical Twitter user contributes very rarely. Among Twitter users, the median number of lifetime tweets per user is one. This translates into over half of Twitter users tweeting less than once every 74 days.

At the same time there is a small contingent of users who are very active. Specifically, the top 10% of prolific Twitter users accounted for over 90% of tweets. On a typical online social network, the top 10% of users account for 30% of all production… This implies that Twitter’s resembles more of a one-way, one-to-many publishing service more than a two-way, peer-to-peer communication network.

The fact that half of twitterers have tweeted once or less, and that 75% of twitterers have tweeted four times or less is quite astonishing. It is consistent with Nielsen’s finding that 60% of Twitter users don’t come back the next month.

With Facebook apps we have sometimes seen amazing growth driven by virality, followed by a dip towards a more sustainable level of usage. When you are viral, a good portion of unique users are going to the site to sign up for the first time. But if they don’t stick, then you can see a “shark fin” shaped curve, as Andrew Chen has posted about in the past.

Twitter is not just another Facebook app. Unlike many of the “flash in the pan” apps, Twitter is a verb, and has entered the popular consciousness. The very high usage of the top users (90% of tweets from 10% of users) also suggests a different model. But it will be interesting to see how twitter usage continues to grow over the next few months

An excellent excel model of viral growth March 10, 2008

Posted by jeremyliew in business models, churn, models, retention, social media, viral, viral marketing.

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.