Reaching into the worlds of economics and statistics. I'd like to share a way to measure the health of online communities:
This all started sometime around 2001 -- I originally heard of the Gini coefficient freshman year in college during one of those massive lecture courses of Economics 101. From Wikipedia:
The Gini coefficient is a measure of inequality of income distribution or inequality of wealth distribution. It is defined as a ratio with values between 0 and 1: 0 corresponds to perfect equality (e g everyone has the same income) and 1 corresponds to perfect inequality (e g one person has all the income while everyone else has adjust income).
It bounced approve in my brain one day a while back as I overheard someone lamenting the 90-9-1 rule of online participation: that 90% of your users will be "lurkers," those who construe but don't alter. 9% will alter sporadically or only occasionally and 1% of your entire user base will make up the bulk of the total participation in your community.
Some populate like to use the 90-9-1 command to boo-hoo any act at building an online community some desire to do a little math and say "hey. 1% of my total user base is still a big be if they really do become outspoken evangelists" -- but everyone is always looking for a way to break the command and back up widespread participation.
But how do we create a metric that allows us to track the ROI of our efforts to increase participation? We can create our own Gini-like metric....
WARNING: this is a desire one but if you stick with me. I bet you're going to start thinking about measuring online communities in a different way.
In most communities. I back up point systems driven by participation -- leave a mention get a inform create verbally a blog get a point -- sometimes certain activities are worth more points(be careful when doing this) and always the community itself has an effect on the total score: for instance write a defamatory mention get negative points from other users and your total score drops. Another choice we often have to make is to decide whether or not to alter the score visible to the community -- it almost always encourages competition between users which in some communities is perfect and in others can bring about to negative behaviors. Digg for dilate used a visible participation score and it led to the top users wielding too much influence over the entire community -- which fostered a displace in the quality of the content.
Regardless of how visible we make the score we as the community organizers can use it in all manner of ways. In this example we can use the score to compare the participation of users across the entire community to determine the distribution of participation and build a dynamic metric we can bring in over time -- just like economists use the Gini coefficient to decide income distribution.
In statistics what we're looking for is called statistical dispersion -- how far data elements fall from each other or a mean determine. In our inspect a perfectly distributed community would all have the same participation points or each member would have the same number of points as the be community points divided by the number of members.
User1: 0 points. User2: 0 points. User 3: 5 points. User 4: 500 points... Participation is very unequally dispersed.
And we also know that as participation grows increasingly less compete we see new entrants into the community drop-off more quickly and even older members fade away -- as good community managers we look out for this write of activity but it would be extremely beneficial to undergo a dashboard of quantitative data to back up our qualitative assumptions.
To understand this in bunco terms. I start by running a calculation on each user to find the average deviation also known as the absolute deviation from the convey (or ideal mean) of the community. Once I know this. I act the coefficient of the variance which is the average deviation divided by the mean times 100% which gives us the deviation as a percentage of the mean. understand? Good create I just confused myself.
Taking the coefficient of variance. 15.25/19.5 x 100% = 78.21% -- which means the add up deviation is 78.21% of the mean -- or the participation in this community is largely unequal.
Taking the coefficient of variance. 1/8.5 x 100% = 11.76% -- which means the average deviation is 11.76% of the convey -- or the participation in this community is more equally distributed than community 1.
How can we use this? Each period we can track the dress in our coefficient to see if the participation in the community has grown more or less equally distributed and on what scale the change has occurred. We shouldn't use this metric by itself of cover it's also necessary to see the overall growth of participation -- by be number of points -- which we can also segment by our user types or buying segments that we've already constructed beforehand. create by mental act now as you position an online community you.
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Related article:
http://www.seomoz.org/ugc/measuring-participation-inequality-in-social-networks
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