Qualitative Vs. Quantitative Community Metrics

Communities are awash with qualitative data. It’s everywhere.

We often seek it out with focus groups, surveys, polls, interviews, and discussions. But it also teems through community channels unprompted, in forum posts, in group chats, and DMs.

Because it’s so abundant, we can overlook its value.

Because it’s so fiendishly hard to capture and action, we can underutilize it.

We might also be hesitant to use it because it’s seen as fluffy. A label often applied to community in business making us keen to avoid furthering that perception.

It’s critical, though, that we leverage it. The qualitative data we can collect and the learnings we can glean from it are key to the value community delivers. Few other teams within a business have the same direct access to customers and at the scale community does. That means we can provide unique insights you can’t get elsewhere.

Qualitative insights enable an understanding of the why behind behaviors and experiences that quantitative data cannot unpack. They also help to determine focus areas, prioritize activities, and enrich quantitative feedback. In short, qualitative data tells us the story behind the numbers.

When it comes to reporting, data alone isn’t enough. You need to be able to tell the data’s story, give it meaning, provide the context to understand it and enable connection with the consumers of it.

But when and how do community teams best use qualitative data? Here are some practical examples of when to use qualitative data when working with other teams that are anything but fluffy.

Early indicators

When your community is new, or you’re just getting started with something, often you won’t have enough quantitative data to make meaningful decisions or to produce reports.

A failure here is to not report anything at all. That’s when execs get antsy and create their own narratives, assuming no thought has been given to measurement.

It’s better then to get ahead of this and record what anecdotes and feedback you can, so you can at least get a sense of whether your strategy is working (and reassure management that you’re on top of it).

You should be careful to avoid extrapolating those insights into judgments about things as a whole. Your qualitative results won’t be statistically significant. Meaning, the results aren’t representative of the entire population because the sample size collected is unlikely to be large enough.

Instead, you can use those insights to form hypotheses to test via quantitative methods. So it gives you the means to get the answers you want, rather than the answer directly.

Trends and themes

Qualitative research helps us to understand trends (patterns in the data) and identify themes (grouping similar data). Both are useful when understanding and acting on product feedback, for example.

The number of ideas or votes on an Ideas board only gives you part of the picture. Overarching trends in the feedback, such as common areas of friction, or emerging use cases, can help a product team understand what customers need better than individual solution suggestions.

Insights on when a customer needs a solution and what they’re currently doing to work around or solve that need help uncover how important a feature really is.

Plus, not all feedback is equal. Who suggested or voted on an idea is key to prioritizing it on the roadmap. It provides the missing info needed to spot alignment with your ideal customer profile and the wider company strategy.

That can prove similarly important when reporting on data, like community-qualified leads, for instance. Which organizations are on the list and why can in some cases be more important than the number.

Sentiment

How strongly someone feels about something can also help determine the priority we should give to it. Working with Support to resolve an issue a few people hate is better done ahead of something a larger group finds mildly annoying. That only emerges from the language used and signals like when it comes up and the severity of its impact.

Knowing the exact words people use in their feedback can also be hugely valuable to marketers, copywriters, and those working on your help docs. Using that same language in copy can help people find that content more easily, connect more authentically, and better explain the value of a product or feature in marketing communications.

Providing context

In reporting, most folks won’t know whether a metric is good or bad, or how much of an increase or decrease represents success. But with the context of what that metric means in real terms they can start to understand it. Examples or anecdotes from customers about how a product or service solved a problem and what that meant to them on a personal or professional level gives this clarity.

This applies to analysis, too. User journeys, for example, either within your community or product show you where folks drop off but don’t explain why. Qualitative feedback is essential to know what caused a user to stop and the right approach to remedy it.

Involving people

This is perhaps obvious to many community people, but people like to share their opinions on the things they care about. Whether via a survey, a 1:1 call, a poll etc., involving people helps to strengthen relationships. While you could get similar insights through automated means, such as click tracking, taking the time to hear from someone is valuable in itself.

#MakingMemories

Lastly, stats and figures are easily forgotten. What often sticks in someone’s mind is a story or even a feeling. Quantitative data alone fails to bring the emotional element that a good story can stir up. If you want to make a point use a statistic, but if you want them to remember it, tell them a story.

Solutions like Talkbase make collecting and reporting on quantitative data easier and less time-consuming, making days spent wrangling spreadsheets a thing of the past. In turn, we can use that time to make better use of one of the core strengths of community work: extracting qualitative insights.

By knowing when and how to use qualitative data we can maximize the value generated by our efforts and help teams across the business to work more effectively.

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