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Data Science

Health Factor 3: Members

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.


Members.jpgThe 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.


Recall that traffic is a measure of passive engagement, and content is a measure of active participation. In Lithium-powered communities, login is not required for passive engagement, but it is required for active participation. This means that users must register to become members of the community and must sign in before they can post. However, because people tend not to register  or log in unless they have to, the members health factor is good estimate for the conversion rate from passive engagement to active participation.


What about Member Churns and Lurkers

Although growth is a sign of a healthy community, member attrition is inevitable. Some users register merely to post a question, and they will leave as soon as they have the solution to their problem. Therefore, even healthy communities will have some natural member churn rate. But a high attrition rate is indicative of poor community health because the community is not able to retain members and grow its active core of user base. This issue has been brought to my attention at the Persuasive2009 conference.


Another question I often hear is that "what should we do about lurkers?" If you are not familiar with social media you might think that lurking is an undesirable behavior. But in a series of studies, Prof. Blair Nonnecke and Prof. Jenny Preece have shown that lurkers do provide value, and they are important to the success of community. So let your lurkers lurk! When the time comes--when they have a specific question to ask or some information to contribute--some of them will de-lurk and become members.


The Current and Future of Members

Currently, the members health factor is the same as the weekly registration metric, so it only measures the passive to active conversion rate. To get a complete picture of community health, it is important to track member attrition in addition to member accumulation. So in our next iteration of CHI, the members health factor will account for member churn.


Diagnostic.jpgThis covers the three diagnostic health factors: Traffic, Content, and Members. They are called diagnostic because they describe the result of user behaviors and tell you the current health of the community. As such, they are lagging indicators of community health. Next time, we will start with the predictive health factors: Liveliness, Interaction, and Responsiveness. They are referred to as predictive because they describe the intrinsic social dynamics within the community, so they tend to be leading indicators of community health.


Let me close by reaffirming that all comments are welcome, so let me know what's on your mind. We do take your comments seriously, and you can help us make CHI more accurate and better suited to everyone's needs.




Hi Mike,


This is an interesting post.  How do you track member attrition? Does it have to do with the amount of time a user is inactive for a certain period of time?  I often try to take into account only my active members when doing data analysis on our community, but I have not found an easy way to do so. 

Data Science

Hello Laura,


Thank you for your reply. Concerning attrition: we have to make an assumption that members who have not login for some period of time are considered lost. We can pick a reasonable time frame, say 4 weeks for example (the exact number of days for the window, will have to be set after we examine more data). Then member attrition is just the number of user who has not login for at 4 weeks or more. Since out app tracks the time of every login, this attrition rate can be computed.


Of course, you would want to compare the weekly differential of the above measure of member attrition to the number of registration (member accumulation) in the past to come up with a percentage of loss over the 1 week period. For that you will need the number of registration between 4 weeks ago and 5 weeks ago.


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