There’s a theory in the community industry, the 1-9-90 theory of engagement. The theory is that 1% of your community is super active (posting, asking questions, commenting, etc.), 9% will be somewhat active (reacting with emojis, maybe commenting here or there), and 90% of your community will be passive (reading questions and comments, maybe reacting with emojis). Note that each community defines these levels differently, so one community’s definition of “active” members might not suit another’s. These definitions are also often determined by the platform that the community lives on. Some platforms give different definitions, and some track different metrics altogether.
At CMX, we have two online community spaces – the Slack Community and our Facebook Group. Due to the nature of the platforms we are using, the metrics we track for each space are determined partly by what’s available to us from the platform. Keep in mind, the metrics you track might be different than the metrics you report on, and the metrics you track to prove member value are probably different from the metrics you track to prove business value. To learn more about these different strategies, check out The Three Level Strategy Framework.
CMX’s Slack Community
I was so curious to know how CMX sizes up to this theory, and it’s March Metrics Madness, so I got inspired! I decided to do a little experiment! For this study, I looked at the CMX Slack Community. Started in 2017 by a few passionate members of CMX, the Slack Community now has over 4000 members, and incredible engagement (if I do say so myself..).
CMX currently uses the free version of Slack. Their “per person per month” payment plan doesn’t work within our budget, with over 4000 members. As such, the metrics we track for our Slack Community are those that the free plan on Slack makes available. These metrics are specifically the ones we use to track member value and community engagement. These are not the metrics we track to prove the business value of our community.
Please note: Slack defines someone as active if “they posted a message or read at least one channel or direct message.”
- Daily Active Users (DAUs)
- Weekly Active Users (WAUs)
- Daily members posting messages
- Total members (available via Dots)
- New members (available via Dots)
- Average Replies per Thread (available via Dots)
- Number of members who replied to a post (available via Dots)
- Stickiness (DAU/WAU) (Stickiness is the ratio of daily active users to weekly active users, which indicates the proportion of weekly active users who engage in CMX Slack in a single-day window; i.e., how “sticky” a community is for a member.)
- New Users (available via Dots)
- Stickiness (DAU/WAU) (Stickiness is the ratio of daily active users to weekly active users, which indicates the proportion of weekly active users who engage in CMX Slack in a single-day window; i.e. How “sticky” a community is for a member.)
How does the CMX Slack Community fit into 1-9-90?
In the CMX Slack Community, we use the following definitions:
- Super active = posted in a channel or direct message
- Active = read at least one channel or direct message
- Passive = did none of the above
Slack doesn’t give the above metrics as they are written, so we must do a calculation to determine each number:
- Super active = Daily members posting messages
- Active = Daily active members minus Daily members posting messages
- Passive = Total number of members minus total Daily active members.
Here’s how the CMX Slack Community measures up to the 1-9-90 theory of engagement:
- Super active = 2%
- Active = 5%
- Passive = 93%
So, in the CMX Slack Community, over the last three quarters, we averaged 2% of our community is super active, 5% are active, and 93% are passive. At this time, we can’t actually separate the passive group into our lurkers who logged in and viewed content, to the folks who didn’t login at all. These are two very different groups, and I’d love to be able to separate the (#weloveourlurkers). I’m hoping we will be able to get more granular with our definitions as we work more with the team at Dots.
There are so many metrics to choose from, and all have different purposes, different perceived values, and reasons for tracking. What fits one community might not fit another, and so figuring out what is right for yours is the real trick. I hope this experiment provides an example of how one calculates their metrics using the 1-9-90 theory. I’m so curious to hear how other communities fall within this theory as well. If you do your own experiment, or you already know, please email me with your 1-9-90 numbers!
Looking for more opportunities to nerd out about metrics? Check out what happened during March Metrics Madness!