Dead addresses tell us things

There was confirmation this week that the increase in “user unknown” messages from Yahoo is actually Yahoo cleaning out abandoned accounts. At the same time a Yahoo is sending out notifications to folks to log into mail.

The first thing every sender should do is remove all these Yahoo addresses from their lists. They’re done, kaput. Gone.

There are some other things worth doing with them, though. Some of these things are informative, they will help help you understand your subscribers and list lifecycle better. Others are protective, they will improve your data hygiene over the long term.

We know that Yahoo disabled a bunch of email addresses that have not been in use for at least a year. There are some other bits of information we have that give us a broader picture of what is happening.

  1. About a year ago, this same thing happened. There was an increase in the number of user unknowns at Yahoo (Thanks, Tara, for noticing I wrote about it last year). It’s possible that they’re scheduling address purges on a yearly basis.
  2. I mentioned reports of an increase in user unknowns from Yahoo in April 2013.
  3. Yahoo is sending mail to users alerting them that if they don’t log into their mail accounts they’ll lose access to mail – I got one of these to the address tied to my flickr account.

Based on this information, I presuppose the following.

  1. Yahoo has an process for reviewing and disabling accounts that happens in the early spring and has done for at least 5 years.
  2. The addresses that started bouncing recently are accounts that have been not logged into for between 12 and 23 months. 12 because this is what the re-engagement campaigns are indicating. 23 because we can assume that some addresses were at 11 months for the disabling a year ago.

We have a known population of yahoo.com addresses that we can assume were abandoned between April 2017 and March 2018. Now you know how many Yahoo addresses go bad in a 11 – 12 month period.

We can ask questions about those addresses that will give us more insight into our subscriber list and how we should handle expiring addresses and data hygiene.

  1. When did those addresses join your list?
  2. When was the last open? click?
  3. Is that address associated with an active login or purchasing account?

With a known population of freemail addresses and some certainty on when the recipients stopped logging into their accounts we can develop data hygiene rules that make sense for our business. It’s not just picking a certain period of time to stop mailing. We can model the behaviour of freemail users knowing when they abandoned their accounts and make sensible policies.

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Thoughts on Data Hygiene

zombieemailOne of the big deliverability vs. marketing arguments has to do with data hygiene and dropping inactive users. Marketers hate that deliverability people tell them to let subscribers go after a long time of no activity from the subscriber.
Data hygiene is good. Email is not permanent and not forever, and the requirements for data hygiene in the email space are very different than the requirements in the postal mail space. There is no such thing as “dear occupant” in email. I mean, you can sent to occupant, but the occupant can then hit the this is spam button. Too many emails to “occupant” and mail goes to bulk instead of the inbox. These are real risks.
With that being said, there are a lot of things to consider when putting together a data hygiene program. You’re looking to remove people who are no longer interested in your brand as much as they are no longer interested in your mail. You’re trying to suss out who might have abandoned the email address you have for them. It’s complicated.
I’ve worked with a lot of clients over the years to implement data hygiene programs. Sometimes those programs were to deal with a bulk foldering issue. Other times clients have been trying to address a SBL listing. Still other clients were just looking for better control over their email and delivery. In all cases, my goal is to identify and classify their recipients into 3 groups: addresses we know are good, addresses we know are bad, and then addresses we don’t know about.
Good addresses get mailed. Bad addresses get dumped. The challenging bit is what do we do with the unknown addresses? That’s when we start looking at other data the client may have. Purchases? Website visits? What do we have to work with and what else do we know about the people behind the addresses. Once we’ve looked at the data we design a program to take the addresses we don’t know about and drop them into either the good or the bad bucket. How we do that really depends on the specifics of the company, their program and their data. But we’ve had good success overall.
There’s been a lot of discussion on hygiene this week, after Mailchimp published a blog post looking at the value of inactive subscribers. They found something that I don’t find very surprising, based on my observations across hundreds of clients over the years.

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What kind of mail do filters target?

All to often we think of filters as a linear scale. There’s blocking on one end, and there’s an inbox on the other. Every email falls somewhere on that line.
Makes sense, right? Bad mail is blocked, good mail goes to the inbox. The bulk folder exists for mail that’s not bad enough to block, but isn’t good enough to go to the inbox.
Once we get to that model, we can think of filters as just different tolerances for what is bad and good. Using the same model, we can see aggressive filters block more mail and send more mail to bulk, while letting less into the inbox. There are also permissive filters that block very little mail and send most mail to the inbox.
That’s a somewhat useful model, but it doesn’t really capture the full complexity of filters. There isn’t just good mail and bad mail. Mail isn’t simply solicited or unsolicited. Filters take into account any number of factors before deciding what to do with mail.

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Whitelisting is dead

A decade or so ago I was offering whitelisting services to clients. It was pretty simple. I’d collect a bunch of information and do an audit on the customer’s sending. They’d get a report back identifying any issues that would limit their chances at acceptance. Then I’d go and fill in the forms on behalf of the client. Simple enough work, and it made clients feel better knowing their mail was whitelisted at the various ISPs.
When email filters were less complex and more binary, whitelists were a great way for receivers to identify which senders were willing to stand up and be held accountable for their mail. Over time, whitelists became much less useful. Filtering technology progressed. Manual whitelisting wasn’t necessary for ISPs to sort out good mail from bad.
The era of whitelisting is over.
In fact, three of the major whitelist providing ISPs were AOL, Yahoo, and Verizon; all three are now a part of OATH. The Verizon whitelist page now redirects to postmaster.aol.com. New requests to signup for the AOL whitelist are rejected with the message that AOL whitelisting is no longer available or necessary. Yahoo has a “new IP review” form rather than a whitelisting form.
Whitelisting is dead.
Even the various certification and whitelisting services have mostly gone away. Both Habeas and Goodmail failed to achieve a profitable exit event. Of course, Return Path is still around, but they have built a platform of tools and services unrelated to whitelisting or certification.
Now senders are going to have to focus on sending mail that people ask for and want in order to make it to the inbox.
 

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