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Data Science

Health Factor 2: Content


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.




Michael, thanks for talking through the CHI factors. Can you tell me if Accepted Solutions ranking and Kudos factor into the Content rating? If Content is made up of quantity and quality, then these two factors are very public demonstrations that a user believes the content is of use - therefore it has a perceived higher quality rating to that person. Or do these attributes factor into another of the 6 metrics?

Bladefrog, I asked the same question some time ago since it seemed to make so much sense. The answer was no, however - and the reason why was that CHI was developed to be used across many environments, not just Lithium's, so Michael had to look for some least common denominator mesurements that every community shares. We didn't want to highlight our own features in the formula to unfairly weight the scales in our communities' favor. Though you could argue that accepted solutions, kudos leaderboards and other similar features tend to drive more views, which will positively affect your community health.

Data Science

Hi Bladefrog,


Thank you for the question. Scott was right. CHI was developed with the goal that everyone who like to compute it should have sufficient data to compute it. So we excluded metrics that are specific to Lithium, such as kudos and accepted solutions. But don’t get the impression that encouraging your members to give kudos and mark solutions has no effect on your community health. It does! But it does so indirectly.


Because rated and validated content are more useful, communities that have many kudos and accepted solution tend to drive more traffic and viewership. So even though kudos and accepted solutions do not directly affect CHI, they certainly can drive traffic and affect your community health through the traffic health factor and the content health factor by increasing viewership.


Thanks for the info. With that in mind, what is the biggest weighted or determining factor in changing the Content metric itself then? Is there one thing that has a major impact to the score?


Data Science

The post count is still the biggest factor that will influcence the Content health factor. Because the viewership is a dampen version of page view. It's affect will also be dampened. However, page view generally is a much larger number than post count. If you have a huge boost in Traffic, you can also expect a significant increase in Content. But it would have to be a very huge increase, like orders of magnitudes difference.

Is it not possible to devise a formula such that the dampening of views is a sliding scale depending on whether or not kudos and accepted solutions data is available?  One simplistic implementation could be to use a dampening factor of 0.5 if Kudos are not available and 0.4 if Kuds are available (and of course add in Kudos with some reasonable multipler of views?  Just a thought...

Data Science

Hello KaushalS,


It is possible! However, this require Lithium specific data, such as kudos and accepted solutions. And if we do that, CHI would no longer be a general metric that is applicable to other community vendors.


Hi Mike - I think the drill down view will be very useful to us as the performance across the boards in our community varies greatly! I know our scores must be strongly affected by one particular board and I would like to see what the metrics look like for the remaining boards.

Data Science

Hello Laura,


Thanks for the reply. Your affirmation will ensure that this feature gets the attention it deserves. This is very good and I sincerely appreciate you taking the time to confirm that this is a good feature to have and that it is useful for you.


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