After more than two months, we have finally reached the 6th and final blog on the current and future development of the community health index (CHI) and its health factors. I want to thank all of you readers out there for all the feedback; the analytics team at Lithium takes them seriously in prioritizing what we deliver. I guess this mini-series is getting too long and you are probably a little tired of it by now. So, let's nail it this time! Alright! On with the final health factor: Responsiveness. If you missed any of the previous blog in this series, they are: Traffic, Content, Members, Liveliness, and Interaction.

The Responsiveness Health Factor

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.

The responsiveness health factor is a measure of the average elapsed time between responses. It is very similar to the traditional time-to-response metric, which is generally defined as the elapsed time between the first message in a thread and the first response to that message. However, this traditional metric does not take into account the elapsed time between subsequent responses. So, you can think of this health factor as a more accurate version of the time-to-response metric.

Responsiveness also has a significant impact on user retention because visitors will abandon an irresponsive community altogether and spend their valuable time elsewhere. Therefore, this health factor can be interpreted as the perceived quality of engagement for potential visitors.

The Current and Future of Responsiveness

The current formula for computing responsiveness ignores threads that have no response, and the fraction of responded threads (or the response ratio) has no effect on this health factor. The response ratio was originally part of the responsiveness calculation, but it was removed when I found that it is highly correlated to the interaction health factor for the community. Since threads that have no responses will have zero interactions, these unresponsive threads are already penalized by their interaction health factor. However, people seem to inherently expect the response ratio to affect the community's responsiveness. To make CHI and the health factors accessible to a wider audience, I may need to formulate the responsiveness calculation in the future. Besides this minor point, we have not received much troubling feedback concerning this health factor.

Finally, I like to draw your attention to an important point about the three predictive health factors: Liveliness, Interaction, and Responsiveness. Recall that these health factors are predictive because they describe the intrinsic social dynamics within the community, and they tend to be leading indicators of community health. In practice, this means the predictive factors can often serve as an early warning sign to imminent problems. By analyzing these predictive health factors, you can take corrective actions against potential problems before they occur. So the predictive factors are also called actionable analytics.

Well, this concludes our coverage on all the health factors of CHI. Next time we will talk a bit more about how these health factors are combined. Stay tuned at mich8elwu.

This is the fifth blog in the miniseries on the Health Factors of CHI. Other blogs already in this miniseries are:

1. Traffic
2. Content
3. Members
4. Liveliness

Last time we talked about the most troublesome predictive health factor: Liveliness. This time we will discuss Interaction.

The Interaction Health Factor

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:

1. The amount of conversation you have with a particular user.
2. The number of different users you've communicated with.

Clearly, the more you talk to a user, the more interaction you have with that user, but talking to different users also increases the level of interactions within your community. In fact, the latter has a greater effect on the overall health of the community. The interaction health factor measures the average number of unique participants per topic weighted by the amount of conversation between them. Thus, this health factor provides an estimate of the average number of members a potential user is likely to interact with and the estimated amount of messages that will be exchanged between them. Accordingly, this health factor may be interpreted as the expected scope of the engagement for potential visitors.

Different Modes of Interactions on the Lithium Platform

Because different communities have vastly different kinds of interactions, the interaction health factors also vary greatly across communities. For example, the dominant mode of interaction of a support community consists of a troubled user asking a question and a knowledgeable user answering the question. This kind of interaction usually involves few users with relatively short diagnostic dialogs before arriving at the solution, so support communities tend to have a steady and modest value for the interaction health factor. In contrast, the dominant mode of interaction for an enthusiast community often involves extended discussions among many users. Also, because the topic and amount of discussion is usually event-driven, enthusiast communities tend to have a higher, but more volatile value for their interaction health factor. B2B and internal communities also tend to have lower level of interactions than that of enthusiast communities.

The Current and Future of Interaction

Besides the difference in community purpose, the different web applications, such as forums, blogs, ideas, and tribal knowledge base (TKB), also have drastically different modalities of interaction. However, the current health factors and CHI were developed based on analyses of primarily forum interactions. Blogs, Ideas, and TKB are relatively new products in our platform that do not have 10 years of historical data. Consequently, the expected level of healthy interaction in the current algorithm may be too stringent for these new interaction modalities. As a result, the interaction health factor for communities that use these new applications may appear slightly lower than expected. We have made note of this issue and we will address it in the next revision of our formulae.

I hope this blog gave you a better understanding of interaction and addressed the concerns you may have concerning this health factor. Next time, we will move on to the last health factor, responsiveness. Watch for updates at mich8elwu.

After a month of digression, let's come back to the topic of health factors. Previously, we have covered the three diagnostic health factors:

1. Traffic
2. Content
3. Members

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? The liveliness health factor is a perception of the amount of activity within a given space, so it is a measure for the concentration of activities. This health factor is extremely important, because it strongly influences a user's propensity to participate. In fact, liveliness gives an estimate of the likelihood of active participation for community users. The more lively the community appears to be, the more likely an individual is to participate. It's logical to think that a lively community is more likely to respond to the user's question or comment.

Concentration of Activity = Perception of Liveliness

Although modern communities furnish users with many activities, posting messages is still the most visible action that is going to influence a user's perception of the community's liveliness. Since other activities (such as rating contents, tagging messages, chatting with other members, etc.) are less noticeable, they have much less effect on the perceived liveliness of the community.

Look at the above picture and ask yourself: what make it lively? This picture looks lively to a spectator because the children are concentrated, and there are a lot of visible actions. This playground would be much less lively if the same group of children were dispersed throughout the park.

Since liveliness is a concentration measure, the absolute amount of post is not very important. For example, if your community has 200 posts per week and there are 4 boards, then each board gets 50 posts per week on average. That is more than 7 posts a day! Users would perceive this as a pretty lively community. In contrary, if there are 50 boards (same 200 posts per week), then each board will only get 4 posts per week. That is less than 1 post a day! Even though the absolute amount of posting activity is the same (200 per week), the former example has a much higher concentration of post, and therefore gives the perception of greater liveliness than the latter example.

The Current and Future of Liveliness

The question that is often raised is "Do you excluded private boards, archived boards, read-only boards, etc, when computing the liveliness health factor?" In the current version of CHI, the liveliness calculation only excludes boards that are hidden or under a hidden category. These hidden boards may contain some private or archived boards, but they probably do not encompass all of them, and it certainly does not include read-only boards or announcements boards. Consequently, communities with many of these "non-public" boards may appear to have a lower liveliness, because our current calculation may not excluded all boards that are not intended for public participation.

Like the content health factor, liveliness is currently computed at the community level, which prohibits drilling down to identify of categories or boards that are not lively. These two items are already on our roadmaps of to-do items. If you can think of others, please let me know. Also, if there are any questions about liveliness that I did not cover, please bring it up . Let's try to make this a lively discussion.

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.

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.

This 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.

My previous post focused on the "traffic" health factor - where it is today and where it's headed in the near future in terms of reformulation. Today I'll be talking about another health factor: Content.

The Content Health Factor

Once critical mass is reached in terms of human traffic, the next thing to focus on is building content within the community. Visitors won't return time and time again without an abundance of interesting, useful and highly desirable content. The content health factor is a measure of both the quantity and the quality of the messages posted within your community. Contrary to traffic, which measures the passive engagement of visitors, content measures the active participation of your community members. Because posting (whether it is a message, a reply, or a comment) adds consumable information that is persistent within the community. This is a form of active participation.

Measuring the Quantity and Quality of the Posts

The post count metric provides a straightforward measure of the quantity of posts, but how do you measure their quality? We leave this decision to the readers. Using a marketplace metaphor, when the number of consumers (readers) in a community is large, the "economics" of the community can give an accurate estimate of the relative demand (whether they are useful or interesting) for the posts. Since the demand for a post strongly correlate with its viewership, the demand for a post must be reflected in the page view metric. However, highly viewed pages tend to draw more random views. This snowball effect will inflate the estimate of consumer demand. Therefore, the post quality can be approximated by a dampened version of the page view metric, which we call viewership.

The Current and Future of Content

In the current implementation of CHI, post counts and page views are aggregated over the weekly window, and then computed at the community level. However, this computation prohibits any drill down capability for this health factor. Yet, drilling down to a category or a board and seeing the content health factor at different hierarchy of the community can provide actionable intelligence for the community manager. Because this is a common use-case, and personally I've been asked this specific question at our customer conference earlier this year, I will make sure that we address this issue in the next reformulation of CHI. This drill-down view can be achieved by computing the product before the aggregation.

Can you guess what is coming? Yes, we will talk about the member health factor next. In the mean time, please do tell me if there is a feature you want with regards to measuring community health. Stay tuned at mich8elwu.

Welcome back. I hope everyone had a relaxing Fourth of July holiday. Last week, Lithium delivered a new report, the community health report, to our clients. This report contains two important pieces of new information: 

1. The community health index (CHI): a score that reflects the "health" of your community.
2. The community health compass: a radar chart that shows the relative value of the six health factors (traffic, content, members, liveliness, interaction, and responsiveness) that went into the computation of CHI.

Earlier this year we published a whitepaper on CHI. Since then, however, we've refined the CHI algorithm based on data from real-world use cases along with customer feedback. So for this post, I'd like to talk about the current state and future direction of CHI.

The Traffic Health Factor

Let me begin with the health factor you are most familiar with: Traffic. This health factor is most important when you are launching a community. If no one comes to your new community, the community will fail. As the community matures and acquires a large core of active users, the effect of traffic may become less important. This is because traffic reflects only the passive engagement of the visitors with the community. In a public community, visitors can accomplish quite a bit without any active contribution. For example, they can read messages, search for answers, navigate the community, checkout other members' public profiles, etc. These activities are considered "passive" because the visitors merely consume existing community content, but do not add content of their own. 

Measuring Human Traffic is Not That Simple

In short, the traffic health factor is intended to measure the amount of human visits to the community. However, traditional page-view metrics also counts non-human visits by web robots and crawlers. Moreover, counting page views on a modern AJAX-filled website is not trivial. Because Web 2.0 technology furnishes users with a wide variety of interactions on a very dynamic webpage, users can potentially visit many places and perform many activities without refreshing the page. As a result, behind-the-scene REST API calls, RSS feeds, or any server-rendered pages go unnoticed to conventional page-view trackers such as Google Analytics.

The Current and Future of Traffic

In the current formulation of CHI, traffic is measured by Lithium's own PageView metric, which includes REST calls and any server-rendered pages. Although our preliminary study suggested that robots and crawlers do not significantly affect the final CHI score of the community, we intend to remove their contribution to our PageView metric in our next formulation of CHI. However, since robots do not always declare their identity (in fact some crawlers may even intentionally disguise themselves as human visitors), it is not possible to completely remove the effect of robots and crawlers. Despite this, through iterative reformulation, we hope to derive a traffic health factor that can track the passive engagement of human visitors more accurately.

I hope this post gave you a better understanding of the traffic health factor. Next time, let's talk about the content health factor. In the meantime, feel free to continue this conversation in the comments section below.