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Truly Being A Data-Driven Community Leader (Two Examples)

Richard Millington
Richard Millington

Founder of FeverBee

Everyone is data-driven until they see data they don’t like.

Then they seek (and inevitably find) flaws in the data.

Sure, they’re “just being thorough”. But they weren’t anywhere near as thorough when they like the data.

Two examples spring to mind.

Example One: What Members Really Want

The first is what members want. I’ve undertaken over a hundred client surveys over the past decade. The results usually show a variation of the following.

Asking useful questions and getting useful answers are at the top, feeling a sense of belonging and making friends ranks at the bottom.

Put simply, the overwhelming majority of people visiting a brand community today aren’t interested in making friends or feeling a sense of belonging with others.

That might sound unsettling given the nature of community work, but it’s where the data naturally leads (p.s. Emotional support/belonging is just one value a community provides).


Example Two: What activities have an impact

The second area where people ignore data is measuring which activities they’re engaging in have an impact.

For example, if you stop doing a lot of the tasks you’re doing today, do members notice (not just the noisy few, but the majority).

If we look at this slide from an upcoming case study, we can see that when we removed the tasks which were taking up 60% of the community manager’s time, it had almost no impact upon the number of visitors or the satisfaction of members.

This freed up time to focus on the areas which our surveys and data results indicated would have a big impact. These areas were in major resources, the superuser program, better navigation, and more access to engineers.


Being Data-Driven Means Accepting Data You Really Dislike

If you dismiss data you dislike (or don’t collect data you might dislike), you’re neither data-driven nor acting in the best interests in the community.

There is a goldmine of valuable insights in your data which can direct you to what to work on and what members care most about.

The key is taking the steps to gather the data and accepting the data you find (especially when it contradicts your beliefs).

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