Some colleagues and I were discussing ways of driving better engagement with our readers of a specific newsletter and I suggested eliminating people who haven't opened the email in a certain amount of time. Obviously this would remove unengaged readers and improve our open rates.

My plan was to search for non-openers within 365 days, 200 days, 100 days, and 30 days. Once we knew what those numbers looked like we could make a decision. I figured each narrowing of time would result in fewer unengaged readers.

These are the numbers I got:

365 days: 6,101 non-openers
200 days: 3,891 non-openers
100 days: 4,575 non-openers
30 days: 5,761 non-openers

That wasn't what I expected at all so I reached out to our email platform support and they replied:

The numbers will be different as some people have still not opened an email in 200 days, but they have in 365 and so on. I exported a 30, 100, 200, and 365 day list and compared a handful of the emails and the ones on the 30 day list, do not appear on the 365 day list. I verified with a colleague that this data is correct.

I'm still having some trouble wrapping my head around this but I can't really argue otherwise.

So it's left me wondering what the best approach for this situation might be. I considered exporting the email addresses for all four queries and comparing them to find out who is consistent within them all. That should give a true list of unengaged recipients, right?

Does anyone have any suggestions?