Hi @JessFosnough!

I’m going to check on this internally and I’ll update the thread ASAP!

Best,

Brian

@Brian Turcotte I’m also very curious to hear what you find out about this. Thanks for bringing the question @JessFosnough!

~ Gabrielle 

Hey @JessFosnough, I’m stepping in for Brian here.

I also asked around and what I learned is all Apple Privacy Opens (MPP) are machine opens, but not all machine opens are Apple Privacy Opens. Machine open just means a bot opened the email whereas Apple Privacy Open is just specific to Apple themselves. The reason not all machine opens are Apple Privacy Opens is because inbox providers, network providers, firewalls, etc. all can employ bots to open and scan the emails for validation of the email links to ensure they aren’t malicious. 

Now, as far as parsing MPP from other machine opens, I don’t believe there is a way to separate the two for segmentation or reporting purposes. True machine opens aren’t typically reported as such. We usually just see that as a duplicate open which makes it hard to distinguish it from MPP. It is possible to create custom reports in Klaviyo to measure MPP, which I’ll link here for future readers of this thread:

• How to identify iOS15 Mail Privacy Protection opens
• How to configure custom reports to track Apple Mail Privacy Protection (MPP) opens

I know that’s not the answer you were hoping for, but I hope that helps shed some light! Maybe with some fancy footwork you can riddle out something close using the MPP custom report. 

Thank you, @kaila.lawrence - I appreciate your response to my question. It helps to know that a machine open CAN be different from an MPP. 

I think my main concern is - do I need to be concerned about subscribers who *seem* to be engaged, but actually aren’t, since their opens are coming from bots? For example, we have a sunset flow that targets customers who haven’t clicked OR opened (within a certain time frame), but if machine opens count as an open (and I belive they do), then we actually have some unengaged customers who are being missed, and continue to stay on our list. In addition, our open rates are most likely inflated and can’t really be used as an accurate measure of subscriber activity.

I don’t expect all of this to be “fixed” overnight, as this is a complicated issue, but wanted to bring it up as something Klaviyo developers might want to look into for the future.

I definitely follow your logic. I’d like to pull in some experts to share how they handle this in their reporting: @Omar @retention @bluesnapper @Ashley I. @Spark Bridge Digital LLC 

Hi @JessFosnough this is a really great question. Since we can’t tell 100% by the opens whether someone is engaged not, my next step would be to look at clicks. You can separate out MPP folks who clicked and anyone else who clicked (regardless of open). This would be a stronger, but very engaged group of people. 

If you create a segment of people who have clicked but not opened, I wonder if this would be helpful in determining engagement as well?

Thanks for the response, @KatherineB, I appreciate the suggestion!
I probably should explain that our business is a little unorothdox, compared to other users on Klaviyo. We sell through our website, but we also have brick & mortar retail stores, and a lot of our subscribers visit our retail stores only. When we send a coupon, all a retail customer has to do is open the email and show it at the register to receive the discount. So while I agree that clicks would get me a more engaged segment, it would exclude the retail store customers who are engaged and purchasing, but never actually clicked on the email.
I’m constantly getting “warned” that my click through rate is poor - and I have to keep reminding myself that it’s okay, our retail customers don’t have to click! :-)

@kaila.lawrence @KatherineB 

I would like to ask the question the other way around:

If machine opens are false does it mean that MPP are false too?

I have the following conditional split in my Flow:
 

and the person goes to NO branch: