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MikeW

Lithium Alumni (Retired)

03-18-2010
05:28 PM

Dr. Michael Wu, Ph.D. is Lithium's Principal Scientist of Analytics, digging into the complex dynamics of social interaction and online communities.

He's a regular blogger on the Lithosphere and previously wrote in the Analytic Science blog.

You can follow him on Twitter at mich8elwu.

If you've ever managed a community you've probably heard of the "90-9-1 rule". If you have observed a community closely, you have probably seen it in action.

Soon after a community launches, users begin to participate, but each user participates at a different rate. The minute difference in participation levels is accentuated over time, leading to a small number of hyper-contributors in the community who produce most of the community content.

The 90-9-1 rule simply states that:

- 90% of all users are lurkers. They read, search, navigate, and observe, but don't contribute
- 9% of all users contribute occasionally
- 1% of all users participate a lot and account for most of the content in the community

But how real is this rule? Do all communities follow this rule consistently? If not, how far off is the deviation? Is the proportion really 90:9:1, or is it more like 70:25:5, or 80:19.99:0.01? Let's find out...

Lithium has accumulated over 10 years of user participation data across 200+ communities, so we can address this question empirically with rigorous statistics. Rather than complicating the issue with the lurkers, I choose to analyze only the contributors (i.e. the 9% occasional-contributors and the 1% hyper-contributors). The proportion between these two groups of participants should be 9:1 or equivalently 90:10 according to the 90-9-1 rule.

__The 9-1 Part of the 90-9-1 Rule__

So the 90-9-1 rule ** excluding the lurkers** says that:

- 90% of the
(which is 9% of all users) are occasional-contributors.*contributors* - 10% of the
(which is 1% of all users) are hyper-contributors, who generate most of the community content.*contributors*

What does the data tell us? On average, the top 10% of contributors (the hyper-contributors) generate 55.95% of the community content, and the rest of the 90% (the occasional-contributors) produces the remaining 44.05% of the content.

With my statistician hat, you know I can't possibly be satisfied with just the average! So I plotted the distribution of content contributed by occasional-contributors versus the hyper-contributors across all communities. The standard deviation is 13.02%.

Please note: The reason you only see 143 communities here, is because I've excluded communities that are less than 3 month old (these communities are too young that their participation dynamics are not stable enough for the analysis).

As you can see from the data, the hyper-contributors can contribute anywhere from about 30% to nearly 90% of the community content with an average of 55.95%. This is certainly a substantial percentage (considering the fact that it is generated by only 10% of the contributors), so the 90-9-1 rule "sort of" holds. But, to be rigorous, it depends on what do you mean by "**most**" of the community content.

If "most" meant at least 30% of the community content, then the 9-1 part of the 90-9-1 rule holds for **99.30%** of our communities. If you meant at least 40% of the community content, then **89.51%** of our communities satisfy this rule. But if "most" meant at least 50% of the community content, then only **65.73%** of our communities are described by this rule.

__Turning the Problem Around__

This gives us a convenient spot to turn the problem around and look at the 90-9-1 rule from another perspective. We can define rigorously what "most" means (e.g. at least 30% of the community content), then calculate the fraction of contributors who generated these content and treat them as the hyper-contributors. We can then compare and see how far off we are from the expected ratio of 9:1.

Averaging across 143 communities, we see that if we define "most of the community content" to be "at least 30% of the total content," then the fraction of participants who contributed this amount ranges from 0.32% to 5.14% with an average of 2.73%. That means, on average, hyper-contributors consist of roughly 2.73% of the contributing population, so the remaining 97.27% of the participants are occasional contributors. And the ratio of hyper- to occasional-contributors is about 97:3, far from the expected value of 9:1.

If instead, we define "most" to be "at least 40%" of total content, then we get roughly 5.07% hyper-contributors on average across 143 communities. Now the ratio of hyper- to occasional-contributors is about 19:1, which is closer but still quite far off the expected ratio of 9:1.

If we defined "most" to be "at least 50%" of the total content, then the group that contributed this amount (which qualifies them to be hyper-contributors) is about 9.35% of the participants. This gives us a ratio that is much closer to the expected value of 9:1 on average. However, the variability is also very large. Even under this simple criterion of contributing at least 50%, the fraction of participants who contributed this amount may vary from less than 1% to ~18% of the participants. That means the ratio between hyper- and occasional-contributors may be anywhere from 99:1 to about 5:1.

So is 90-9-1 a hard and fast rule? Definitely not! Not even the 9-1 part of it. But it is certainly a great rule of thumb, when looking at or explaining community data. And it tells us that participation in communities is highly skewed and unequal, and there is a small fraction of hyper-contributors who produce a substantial amount of the community contents.

Next time I am going to start to dive deeper into the contribution level of the hyper-contributors, your community's real superusers.

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About the Author

Dr. Michael Wu was the Chief Scientist at Lithium Technologies from 2008 until 2018, where he applied data-driven methodologies to investigate and understand the social web. Michael developed many predictive social analytics with actionable insights. His R&D work won him the recognition as a 2010 Influential Leader by CRM Magazine. His insights are made accessible through “The Science of Social,” and “The Science of Social 2”—two easy-reading e-books for business audience. Prior to industry, Michael received his Ph.D. from UC Berkeley’s Biophysics program, where he also received his triple major undergraduate degree in Applied Math, Physics, and Molecular & Cell Biology.

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