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Three Very Different Ways To Analyse An Online Community

Richard Millington
Richard Millington

Founder of FeverBee

Most people work from a simple assumption (e.g. “higher levels of activity per member, an increase in retention rates”). This means you measure activity per member and design your engagement activity to maximise activity per member.

The downside is you haven’t proved if the relationship is true (i.e. does it correlate well?), nor the influence levels of activity have on an individual’s retention rate (does it account for 100% or 5% of retention rate increase?), nor whether the relationship is linear (does it drop off after a certain level?).

You could blindly be pursuing more activity when data might show you that 3 posts per member, per month, is enough.

A better approach is to test a falsifiable hypothesis (e.g. sample members by levels of activity and compare this with customer retention rates to prove if the relationship exists and how influential the level of activity is in that relationship). You could then focus on increasing the level of activity from a specific segment to see if retention rate among that segment rises (this isn’t a natural experiment, but it’s still good). You might find that there is a relationship between increased levels of activity and retention, it’s nonlinear. After 5 posts per month, there is little impact.

Now instead of trying to get every member super active, you focus on ensuring they make 5 good contributions per month. This changes how you work a little.

An even better approach is to run a regression analysis to identify which variables correlate with increased levels of member retention. You might find that increased activity accounts for a 27% increase in higher retention rates. This also highlights other key variables. You might find direct messages between members have a 25% influence, opening newsletters have an 18% influence, and adding a profile picture have 10%. You can now test these relationships and build a mechanistic model.

Now instead of trying to maximise member activity at all costs, you might spend more time on newsletter subject lines and content, persuading members to add their profile picture, and ensuring members befriend each other to increase the number of direct messages.

This isn’t easy to do, but that’s exactly what makes it valuable. You can build a specific, tested, model that will show you exactly what you need to spend your time on to achieve. This lies beyond the endless hunt for more engagement. It’s where you can take your work to a more advanced, strategic, level.

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