With the new infrastructure (A1—A3), as well as the improved algorithms (B1—B4), there is no doubt that the final CHI score will also change. There are literally hundreds of intermediary calculations (possibly more) between a single user’s action within the community and the final CHI score. Any changes in any one of those intermediary steps could change the final CHI score.
In my last post, we discussed some of the infrastructural and algorithmic changes behind the new Community Health Index (CHI) shipped at the end of October. But that was just the beginning. Today, let’s talk about some of the forthcoming features we’ve planned with the new CHI score. Please note that these features are not yet available, but they will be soon.
First, an apology for going radio silence for a month. Sorry. But I’m back.
It’s been a crazy October for me. Most of October seems like a blur, because it’s been a mixture of sleep deprivation and 80+ hour work weeks. Besides all the traveling for speaking events during day times and the lost luggage on the way (which I’m happy to share with you later if you are interested), I’ve been working with our data platform team to implement the new community health index at night (CHI, denote by the Greek letter chi).
Just as in the past, I will continue to cover analytic science and research here at Lithium. Today, I will talk about a refinement in the community health report that slightly altered the information displayed on the CHI compass that many community managers are receiving today.
However, some health factors have very different ranges of values, for example, weekly values for members may range in the hundreds, where as content (posts weighted by views) and traffic (views) may have values over 10K or 100K. On the other hand, liveliness, interaction and responsiveness, have much smaller values in the tens or, in some cases, less than one. With such large variation in the scale of the data, it means we have to normalize these values before we display the CHI compass.
In this era of information, there is no shortage of quality information, and because this information lives on the internet, it is nearly free to the end users. However, if this information is not delivered in a timely manner, it will lose its value. Therefore, time is a critical resource that is going to influence a user's experience when he/she is engaging with your community. A community that responds promptly will give the visitors a much better user experience than one that takes a long time to respond.
When you are able to create a lively community, the hard work is half done because by definition a lively community has solved a difficult conundrum of participatory media: How do you get people to participate? However, in a healthy community, it is not enough just to participate. There must be interaction with other users. Otherwise, where is the social of social media?
There are two important dimensions to interaction:
The amount of conversation you have with a particular user.
The number of different users you've communicated with
In the next three post, we will explore the predictive health factors. Base on all of your valuable feedbacks, I got the impression that Liveliness is the factor that has raised the most questions. So I will begin with this health factor.
The Liveliness Health Factor
A number of people have asked: what is liveliness?
Last time we talk about the content health factor and how we plan to reformulate it to enable drill down capability for the health factors and CHI. This time, I will explore the third health factor: Members.
The Members Health Factor
After achieving a steady stream of traffic and copious amount of high-value content, a healthy community should accumulate registered members, grow, reach critical mass, and then become self-sufficient and self-sustained. The members health factor is intended to measure the growth rate of the community.