The Future of Connection: Metrics for Digital Communities
How do we know our online communities are healthy and functional?
What are Digital Communities?
While historically, communities were mobilized around geographical proximity, modern society has a potent tool at its fingertips: the internet. The powerful computing abilities and deregulation of the internet has reduced the barriers to entry making the internet affordable and accessible to large groups of people around the world¹; these leaps in our collective technological prowess have created tools for social mobilization: digital communities.
Digital communities can be formed at a local, national, or even on a global level and can mobilize people at a faster rate than we’ve ever witnessed prior to the internet.
The first example of a social network that enabled the formation of digital communities around personal networks was in 1997. SixDegrees.com had many of the features of a modern social media platform: finding friends, surfing friend groups, and connecting over shared interests². This was the first digital community hosted on the internet and paved the way for the present-day Facebook, Twitter, and other social media platforms.
Digital Communities are the Future: The Internet is Still Young
Digital communities have evolved since then. While some of the largest and most well-known digital communities are currently established on social media sites, ‘forums’ have always been and still are one of the most prevalent forms of ‘community’ on the internet. The coronavirus (Covid-19) pandemic has unearthed and popularized this ignored industry: new communities like Animal Cafe, Virus.cafe, and many others foster online connections outside of social media. In 2020, the Reddit-user-created digital forum ‘r/wallstreetbets’ became so popular that it toppled the stock market³. Another such incident in 2021 was when a BTS (K-Pop) fan group collaborated with Gen-Z Tik Tok communities to sabotage the US Former President’s Kickoff rally in Tulsa, Oklahoma³. This is the power of community. Despite the rise in new digital communities such as Clubhouse, existing social media platforms such as Facebook, Youtube, and Instagram have an indisputable majority making them a great space for creating and engaging digital communities.
Figure 1 outlines the growth of social media platforms between 2017 and projects into the year 2025. In 2021, there are a projected 3.78 billion users of social media platforms⁴. Another statistic indicates that Facebook alone has 2.78 billion unique, monthly active users⁵. To put this in perspective, the sum of the two most populated countries in the world: China (1.39 billion) and India (1.36 billion) represents fewer people than the number of unique monthly active users on Facebook. Almost 37% of the world's population uses Facebook every month. This number doesn’t even account for the total number of Facebook account holders, but merely active users. With the largest digital communities currently being formed on social media platforms such as Facebook, there’s a growing emphasis on the need to understand indicators of whether these digital communities are contributing positively or negatively to their communities themselves, members, platforms, and society as a whole.
Engagement Metrics
Social media sites include engagement metrics that measure how effective each post is. These are often used by marketers to measure whether they have successfully created engaging content to keep the attention of their followers.
Examples of engagement metrics are:
- Pageviews
- Likes
- Shares
- Subscriptions
- Time spent on each page
- Bounce rates
- Top exit pages
Over time, these engagement metrics determine whether a community is created or not. However, these engagement metrics are often misleading: they don’t tell you the quality of the community you have created or whether the information that is creating the engagement is legitimate; engagement doesn’t necessarily equate to community health. Oftentimes, engagement is a better indicator of popularity and has the potential to come at the expense of community health.
Community Health Index (CHI)
While the current engagement metrics are not sufficient to measure whether a digital community is effective, there is a lack of extensive literature on alternative digital community health metrics. The two main reports around community health metrics are from Khoros (formerly known as Lithium) and Higher Logic.
The Khoros Whitepaper is one of the main papers that address the concept of ‘community health’. They describe community health as a ‘spectrum’ of how healthy the community is and identifies parameters that reflect this notion⁶. Additionally, Higher Logic, an SaaS online community platform, also suggests 3 formulas to measure Activity, Value, and Reach. These six main indicators for the Community Health Index by Khoros and the three indicators within the Higher Logic’s Engagement Benchmark score⁷ are tabulated below.
Khoros Whitepaper Metrics
To calculate each of these metrics, refer to the Lithium Whitepaper.
Higher Logic Report on Community Metrics
To calculate the EBS metrics, visit the Higher Logic website here.
Due to the lack of existing literature, we contacted Vincent Boon, an expert on Online Communities. He is the Co-Founder of Standing on Giants and one of the leading experts in the burgeoning world of community management. Previously giffgaff’s Chief of Community, Vincent has turned his attention and talents to Standing on Giants & various other projects.
In addition to those discussed above, we suggest considering the following metrics that expose user behavior on a more detailed level.
- Community integration and Engagement efficiency in communities centered around specific products measure the percentage of customers who have a community account.
- % of users/members posting
- % of content that is help related
- % of new ideas (+ #comments on idea)
2. Highly Active Users: These are users who spend a minimum of 4 hours a week on the platform and post at least 3 times each week
3. Quality of Interactions: These are topics that receive more than 3 replies from different unique users.
4. Diversity of Users: % of New vs Existing Users, Anonymous vs Users who have a good reputation, etc.
5. User Turnover: How quickly are users who leave a community replaced by new members?
6. Richness of Content: This indicator measures the number of characters, links, pictures, videos, reactions. These are indicators of how interesting the content is to the users.
7. Reported content:
- %Content Reported: This is the percentage of all content reported by the community, based on the total content created. A healthy community will have at least 1% of its content reported by the community.
- % Abusive Content: This is the percentage of Abusive content reported, based on the total content created. A healthy community will have a maximum of 0.3% of its content reported by the community being abusive.
Choosing the Right Metrics: More Research Needed
These indicators add another dimension of community engagement that measures how lively, highly active, responsive, and self-sustaining a community is. However, unfortunately, these are very limited indicators that measure community health. Each community needs to create personalized metrics that impact their communities. Some potential metrics could include accuracy of information being shared within the community, socially positive outcomes such as improvement in public health, level of innovation, influence on policy (either within companies, societies, countries, or on a global level).
However, each metric that is included within a community will include the bias of the people creating the infrastructure underlying the community. If I value accurate and complete information, then I will include a metric on transparency within the community that I create. However, businesses and marketing professionals need to be mindful that these metrics aren’t generalizable to every member of a community. Therefore, more research needs to be conducted on how to update the current method of measuring engagement and digital community health.
Some factors that can affect the generalizability of these metrics are:
- Homogeneity of the Community: The more homogenous a group in terms of demographics, the easier it is to create metrics that represent the entire group. For example: Within a community of social entrepreneurs, if all members are from a similar gender, nationality, and region that speaks the same language, they might have similar values regarding social entrepreneurship. These values can be more easily converted into metrics than within a diverse group with diverse opinions. As new members who have different preferences join a group, the community might need to revise its metrics to accommodate newcomers.
- Broad vs Narrow Communities: Communities that are created around a specific topic might have similar underlying values or goals which can be translated into metrics. For example: A community of new moms might value family time while a gaming community might value innovation in gaming technology. However, communities that incorporate broad topics such as politics might have to include metrics that measure community health for all members involved; these metrics need to function irrespective of a member’s political, social, or economic leaning.
Conclusions
The current engagement metrics used on websites, social media platforms, and other online communities seem insufficient towards identifying whether a digital community is healthy or not; the thresholds for determining health using existing metrics is an area that needs work. While Khoros and Higher Logic have started the process of addressing this gap, more research needs to be done on whether and how we can create and incorporate metrics for heterogeneous groups. A key issue to discuss is whether the current metrics are reflective of digital community health and if not, we need to create and establish new metrics that measure the health of large digital communities.
References
- Plant, R. (2004). Online communities. Technology in Society. 26(1): 51–65.
- Boyd, D. M., & Ellison, N. B. (2007). Social Network Sites: Definition, History, and Scholarship. J Computer-Mediated Communication. 13(1): 210–230.
- Digital Communities & the Future of the Internet: EPAM Continuum.
- Tankovska, H. (2021). Most used social media 2021. Statista.
- Tankovska, H. (2021). Number of social media users 2025. Statista.
- Lithium. (2011). Community Health Index for Online Communities.
- Higher Logic. (2020). The 2020 Engagement Trends Report
This article is part of a series on digital community health, find out more about digital communities in Healthy Online Communities: What Tech Can Learn from Biological Ecosystems
