Never 100% inbox

No matter how great an email program deliverability is, no one can guarantee that 100% of the email sent will reach the recipient’s inbox. Why? Recipients can make decisions about where mail goes in their own inbox. Every mail client has a way for users to control where mail is delivered.

This is good for delivery, when the mail means so much to people that they override spam filters and put mail in their inbox. This is problematic for delivery when the final recipient throws mail away or filters it into spam.

Of course, there’s no way to know what individual recipients are doing with mail in general. Sure, there’s currently panel data but that is only for the subset of users that installs a 3rd party app into their mailbox. There’s no way to know where 100% of email is delivered.

For me, I consider any email program with a >95% inbox delivery rate to be an excellent program. I also don’t think there’s much the sender can do in order to get that last 5% to the inbox. That 5% is just not reachable. Not by improving data, not by double opt-in, not by any of the things we do to improve delivery. Some small percentage of mail is just never going to get in front of the user.

The primary reason for these delivery failure is the end user. End users can, and do, create their own filters. While ISPs do curate the inbox, end users have the ability to filter email. If an enduser sets up a filter for a particular email, the ISP isn’t going to overrule that. ISPs want the end users to have a pleasant inbox experience. Thus, the end user’s wants and needs rule. Nothing is clearer to the ISP whether a particular user wants an email than that user directly setting up a filter.

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Thinking about deliverability

I was chatting with folks over on one of the email slack channels today. The discussion was about an ESP not wanting to implement a particular change as it would hurt deliverability. It led me down a path of thinking about how we think of deliverability and how that informs how we approach email.
The biggest problem I see is the black and white thinking.
There’s an underlying belief in the deliverability, receiving, and filtering communities  that the only way to affect sending behavior is to block (or threaten to block) mail.

This was true back in the ancient times (the late 90’s). We didn’t have sophisticated tools and fast CPUs. There weren’t a lot of ways to handle bad mail other than to block. Now the landscape is different. We have many more tools and the computing capacity to quickly sort large streams of data.
At most places these days, blocking is an escalation, not a warning shot. Many places rate limit and bulk folder questionable mail as a first strike against problem mail. Sometimes the mail is bad enough to result in a block. Other times, it’s not bad enough to block, so it disappears into the bulk folder.
There’s a corresponding belief in the sending community that if their behavior doesn’t result in blocking then they’re acting acceptably. This isn’t true either. There are a lot of things you can do (or not do) that don’t help delivery, but will actively harm delivery. Likewise, there are things you can do that don’t actively harm delivery, but will help. All of these things add up to reaching the inbox.

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Following CAN SPAM isn't enough to reach the inbox

One of the top entries on the list of things deliverability folks hear all the time is, “But my mail is all CAN SPAM compliant!” The thing is… no one handling inbound mail really cares. Seriously. CAN SPAM is a law that is little more than don’t lie, don’t hide, and heed the no. Even more importantly, the law itself states that there is no obligation for ISPs to deliver CAN SPAM compliant mail.

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Did the algorithm change?

When faced with unexplained deliverability changes one of the first questions many folks ask is “Did the algorithm change.” In many ways this is an meaningless question. Why? Because there are two obvious answers to the question.
A1: Of course it didn’t.
A2: Of course it did.
Both answers are correct, but they’re answering different underlying questions. When we understand how two diametrically opposed answers are both correct, we understand much more about filtering.

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