Zero In On The Drop-Off Points

Run a cohort analysis (Google Analytics/Community-Analytics can help) and identify the most common drop-off points.

Be aware that the biggest will always be between week 0 and 1. There are ways to tackle this, but ignore this group for now. This group skews your data too badly.

Look at where the remaining 20% or so of survivors begin to drop-off.

Is it after week 3? 4? 8? 12? Focus on these points. Now interview some members who did drop off at this point and ask them why (the last visit date often works).

You will usually get answers along the lines of:

“I just forgot about the community really”
“I didn’t really see anything I could help with”
“It didn’t quite click with me”
“I became too busy”
“To be honest, I didn’t like feeling dumb”

(all genuine quotes)

You might stumble upon an outlier, but assume these reasons are reflective of broad groups of individuals. Find the ones which appear most often and run a short survey for the drop-outs to answer to find out which resonates most.

Now set up an automated reminder specifically timed to tackle these objections. Don’t use facts at this level, you need emotions. Focus on an amazing story that will surprise or dazzle someone. Highlight an action they can take so they won’t forget about you. Focus on how they can help even if they don’t have an expertise. Focus on someone who was in their position and overcame it – with a convincing case study.

You can have fun with it if you like.

Repeat this for different drop-off points. If you get it right, you should see a small bounce in the retention rates. A small bounce might not seem important, but as with all things retention – it builds up over time.


  1. Nick Emmett says:

    This is great, and something I really need to start getting my head around now I’m doing this gig full time - cohort analysis is a new thing for me but I need to get on it! Love the linked YouTube talk too, very useful.

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