Community Health Index

This article is about the health index for online communities. For NHS Scotland's patient identification system, see Community Health Index (Scotland).

The Community Health Index^[1] is a numerical index developed by Lithium Technologies to measures the "health" of online communities. It is often abbreviated as CHI, and sometimes denoted by the Greek letter χ. The value of CHI ranges from 0 to 1,000, where 0 represents communities with poor health and 1,000 represents communities with excellent health. The health of an online community, in this case, is defined to be its ability to meet the needs of its members irrespective of the business objective of the community.

In late 2008, Lithium Technologies completed a detailed, time-series analysis of up to a decade's worth of proprietary data that represents billions of interactions, millions of users, and scores of communities. This research, coupled with the company's expertise in planning, deploying, and managing customer communities, enabled Lithium to identify and calculate key factors that contribute to a new standard for measuring community health.

By analyzing hundreds of metrics from communities of varying types, sizes, and ages, Lithium identified the diagnostic and predictive metrics that most accurately represent key attributes of a healthy community: growth, useful content, popularity, responsiveness, interactivity, and liveliness. Although Lithium uncovered other metrics that proved to be even more predictive of community health, the ones the company selected as the basis for calculating the Community Health Index are readily available for most online communities across the industry.

The Health Factors

The characteristics of healthy communities and their corresponding health factors are:

Members. After an initial surge of registrations characteristic of a newly-launched community, membership in a healthy community continues to grow. Although mature communities typically experience a slower rate of growth, they still add new members as the company’s customer base grows. The traditional method for measuring membership is the registration count.^[2]

Content. A critical mass of content posted on an online community is clearly one of its strongest attractions to both members and casual visitors. In support communities, the content enables participants to arrive at a general understanding or get answers to specific questions. In engagement (enthusiast or marketing) communities, it serves as a magnet to attract and engage members. In listening communities, the content posted by community members gives the company valuable input from the customers who use their products or services.^[3]

A steady infusion of useful content, then, is essential to the health of a community. The traditional metric for measuring content is number of posts. This metric alone, however, gives no indication of the usefulness of the content, especially in communities that do not use content rating or tagging. In order to model content usefulness instead of sheer bulk, we consider page views as a surrogate for marketplace demand, but then dampen their effect to reduce the likelihood of spurious inflation.

Traffic. Like membership, traffic in a community—page views or eyes on content—is one of the most frequently cited metrics for community health. In deriving the Traffic health factor, we started with the standard page view metric, but then mitigated the effect of robot crawlers in order to diminish their impact.

Responsiveness. The speed with which community members respond to each other's posts is another key metric for determining community health. Participants in support communities, for example, are only willing to wait for answers for a limited amount of time. The same is true for engagement and other types of communities. If there is too much of a lag between posts and responses, conversations peter off and members start looking elsewhere. The traditional response time metric counts the number of minutes between the first post and the first reply. That first post might be anything—a question, a blog article, an idea, a status update. Because our analysis of community-member behavior has revealed the importance of subsequent responses, we have enhanced the traditional response time metric to account for all of the responses in a topic.

Interaction. Interaction between participants is one of the key reasons that online communities exist. The traditional metric for measuring interaction is thread depth, where threads are topics of discussion and their depth is the average number of posts they contain. This way of looking at interaction, however, does not consider the number of individuals who are participating. As a result, a topic with six posts by the same participant would have the same depth as one with six different contributors. Because our experience with online communities has led us to understand that the number of participants in an interaction is even more important than the number of posts, we have added the dimension of unique contributors to our calculation of Topic Interaction.

Liveliness. Although most people would be hard-pressed to define it, they recognize and respond to liveliness or buzz when they encounter it. Research has shown that participants are not only attracted to but are also motivated to return and contribute in communities that feel animated and vibrant.^[4]

References

1. ^ Community Health Index for Online Communities Lithium Technologies Whitepaper
2. ^ Butler, B. S. (2001). Membership Size, Communication Activity, and Sustainability: A Resource-Based Model of Online Social Structures. INFORMATION SYSTEMS RESEARCH, 12(4), 346-362.
3. ^ Soroka,V., & Rafaeli,S (2006). Invisible Participants: How Cultural Capital Relates to Lurking Behavior. Proceedings of the 15th international conference on World Wide Web (pp. 163-172).
4. ^ Ackerman, M. S., & Starr, B. (1995). Social activity indicators: interface components for CSCW systems. In Proceedings of the 8th annual ACM symposium on User interface and software technology (pp. 159–168).