Cool Framework for Community Health Index
When I first read the description of Michael Wu’s presentation, I thought he was going to show human health indicators and/or “fit scores” for various cities around the country. I mean, didn’t some of you think that when you read the title of this post: “Community Health Index”?
Wu was actually offering a point of view on how online communities should be measured to be able to diagnose their health and predict future outcomes. We were both on a panel at the Persuasive Technology 2009 conference yesterday at Claremont College.
Lithium, company Wu works for, apparently has been around quite a while creating communities for numerous companies. The smart framework Wu presented for measurement explains why I think they’ll keep growing.
Basically, their Community Health Index measures the ability of an online community to meet the needs of its members.
First, they cover three basic diagnostic measures:
(1) Traffic
(2) Content — useful and interesting (passive vs. active)
(3) Members — registration, conversion rate
Then, they roll up three predictive factors:
(4) Liveliness — posts per board
(5) Interaction — unique user per topic X number of topic conversations
(6) Responsiveness — average response time
I like the simplicity and breadth of coverage these metrics combine–they generate a composite index across these six. And you can see how you could take the same predictive factors and generate different metrics but still under the same more broad buckets, depending on the dynamics of your community.
Nicely done.
Only piece that jumps out right away as missing is longevity or retention. I haven’t studied in detail the technique, but I wonder how that is incorporated–how much churn is expected versus toxic for a community, for example.