A decade or so ago I was helping a client troubleshoot a Spamhaus listing. They, as many companies do, had a database with addresses from a number of different sources. Spamhaus was asking for them to reconfirm the entire database, which they didn’t want to do. I came up with the idea that if we had some sign of activity on the email address, like an open or a click and some other corresponding activity related to that open or click then we could assume that the address was likely a real user and was interested in the emails.
The reason Spamhaus asks for confirmed opt-in is because they want to make sure that the actual recipient of the email wants to receive it. The confirmed opt-in process wraps the grant of permission and the identification check together. But, I reasoned, if we can if we can meet both checks through a different process, then the addresses are confirmed.
In that case, and in the other cases where I’ve used open rates as part of the heuristics to fix Spamhaus, and other delivery, problems things worked out. Over time, I’ve improved my recommendations based on feedback from clients and filters. The confirmation criteria is more sophisticated and more accurate.
Much of my refinement is an effort to compensate for how inaccurate and unreliable open data is. We all think of open rates as a measure of when a user opens and email. But that’s not what is being measured. Instead, open tracking relies on an invisible pixel that is loaded if and when a user loads images in their email. Some, perhaps even most, people load images by default. But not everyone does. There are even some mail clients that can’t load images.
In addition to the mail client issue, there are cases where an image will be loaded without the user ever seeing the mail. In fact, I dealt with a client issue a few months ago where the client was seeing opens before the ESP delivered the mail. In this case, best we can tell, the filter rejected the message after collecting all of the data. A copy of the message was stored on the appliance and the links were checked by the filter. The links were clean, so when the sending server retried the mail, it was accepted and delivered to the user. I think things like these are going to become more common as filtering gets more sophisticated.
While I’ve been making my criteria for using open rates stricter I’ve watched more and more marketers and deliverability experts try and use open rates as a sign of permission. The underlying belief is that if recipients are opening our mail, they must want it. And, statistically and in general that may be the case. But there are cases where an open doesn’t actually translate to the recipient wants the mail.
Every email in the spam folder hurts your overall reputation. Continuing to send mail ending up in spam will decrease your overall delivery in the long term.
When dealing with deliverability issues, including Spamhaus listings, open rates can tell us a little bit about our data. We can use open rates, along with other data, to make sensible decisions about which email addresses belong to folks who want that mail.
What we can’t do is say that every email address that has opened an email has given us permission to send to them. We also can’t say that because we have decent open rates our overall data is good. That’s just not how email works. Yet, every day I see folks incorrectly drawing very specific correlations between opens and permission. I’ve even seen some companies on the SBL try and argue that they should be removed because their open rates are high. (Hint: it’s not a good argument and it doesn’t get them delisted.)
Open rates are an inexact and occasionally inaccurate measurement. When we use open rates we must remember these two facts. They are what we have and we’d be fools to ignore them as information. But we cannot continue to use them as more than what they are: estimates of how recipients are interacting with our mail.