To surface useful community insights you need to dive below surface level metrics.
In one client project, our top-bar navigation survey showed the community maintained an average of a 4.1 (out of 5) helpfulness score.
But that metric alone doesn’t tell us much. To gather useful insights we need to see which parts of the community are more or less popular than others.
We did this by segmenting the community data by category. You can see this chart below:
This chart gives us a wealth of great data. We can see that helpfulness varies a lot by category of discussion. Most importantly, by looking at response rate and time to first response, we can set specific goals and interventions for each category.
Specifically, we set up three clear interventions:
- Reduce the time to first response in the Product 2 category by assigning virtual agents to support.
- Increase the response rate in the Product 1 category by surfacing unanswered questions on the homepage.
- Improve the quality of response in Developer and Partner categories by recruiting experts to answer questions.
You can see the result below:
It took a lot of work, but you can see the targeted interventions had their effect. Product 2 has an improved time to first response, product 1 has a much better response rate, and the community is a little more helpful to partners and developers.
None of this would’ve been possible if we had simply looked at the overall score.
To make really targeted interventions to improve a community, you have to dig beneath the surface. If you need help getting this data, you only have to ask.
Note: Full case study here
