DOD breaks links in .mil clients

DataSecurity_IllustrationThe Department of Defense is breaking HTML links in mail to .mil domains. This is part of the DoD’s attempt to curtail phishing.

a great majority of intrusions into Pentagon networks are the result of the kind of human error that is exploited in phishing attacks, in which seemingly trustworthy e-mail links are used as attack vectors to hijack user computers, install malware or steal credentials.

Instead of being able to click on links, .mil recipients will have to cut and paste links into a browser in order to visit the website. This will also affect open tracking and break images in emails.
If you’re sending to .mil domains, plain text is going to be best. The DoD has had a policy of not rendering HTML, but some mail clients still did. Now the DoD is taking extra steps to break links.
My suggestions for senders who need to send mail to .mil domains:

  1. Use plain text.
  2. Make links as short as possible so that they’re easier to cut and paste.
  3. Call to actions are even more important as you’re asking for an extra step.
  4. For those of you who can, try and get an address that’s not .mil

For mailers who might sometimes get .mil addresses on your lists, think about whether or not you really want to allow them. Try to get a different address for them. Deliverability will be easier and your pretty HTML can be displayed.
 

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Pattern matching primates

Why do we see faces where there are none? Paradolia
Why do we look at random noise and see patterns? Patternicity
Why do we think we have discovered what’s causing filtering if we change one thing and email gets through?
It’s all because we’re pattern matching primates, or as Michael Shermer puts it “people believe weird things because of our evolved need to believe nonweird things.”
Our brains are amazing and complex and filter a lot of information so we don’t have to think of it. Our brains also fill in a lot of holes. We’re primed at seeing patterns, even when there’s no real pattern. Our brains can, and do, lie to us all the time. For me, some of the important part of my Ph.D. work was learning to NOT trust what I thought I saw, and rather to effectively observe and test. Testing means setting up experiments in different ways to make it easier to not draw false conclusions.
Humans are also prone to confirmation bias: where we assign more weight to things that agree with our preconceived notions.
Take the email marketer who makes a number of changes to a campaign. They change some of the recipient targeting, they add in a couple URLs, they restructure the mail to change the text to image ratio and they add the word free to the subject line. The mail gets filtered to the bulk folder and they immediately jump to the word free as the proximate cause of the filtering. They changed a lot of things but they focus on the word free. 
Then they remove the word free from the subject line and all of a sudden the emails are delivering. Clearly the filter in question is blocking mail with free in the subject line.
Well, no. Not really. Filters are bigger and more complex than any of us can really understand. I remember a couple years ago, when a few of my close friends were working at AOL on their filter team. A couple times they related stories where the filters were doing things that not even the developers really understood.
That was a good 5 or 6 years ago, and filters have only gotten more complex and more autonomous. Google uses an artificial neural network as their spam filter.  I don’t really believe that anything this complex just looks at free in the subject line and filters based on that.
It may be that one thing used to be responsible for filtering, but those days are long gone. Modern email filters evaluate dozens or hundreds of factors. There’s rarely one thing that causes mail to go to the bulk folder. So many variables are evaluated by filters that there’s really no way to pinpoint the EXACT thing that caused a filter to trigger. In fact, it’s usually not one thing. It could be any number of things all adding up to mean this may not be mail that should go to the inbox.
There are, of course, some filters that are one factor. Filters that listen to p=reject requests can and do discard mail that fails authentication. Virus filters will often discard mail if they detect a virus in the mail. Filters that use blocklists will discard mail simply due to a listing on the blocklist.
Those filters address the easy mail. They leave the hard decisions to the more complex filters. Most of those filters are a lot more accurate than we are at matching patterns. Us pattern matching primates want to see patterns and so we find them.
 

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Gmail has some extremely complex filters. They’re machine learning based and measure hundreds of things about incoming mail. The filters are continually adjusting to changes and updating how they treat specific mail.
One consequence of continually adjusting machine learning filters is that filtering is not static. What passes to the inbox now, may not pass in a couple hours.
One of the other challenges with Gmail filters is that they look at all the mail mentioning a particular domain and so affiliate mail and 3rd party mail can affect delivery of corporate mail.
The good news is that continually adjusting filters adapt to positive changes as well as negative ones. In fact, I recently made a segmentation suggestion to a client and they saw a significant increase in inbox delivery at Gmail the next day.
Gmail can be a challenge for delivery, but send mail users want and mail does go to the inbox.

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I’ve never met a spammer that claims to be a spammer. Most that I’ve met claim to be legitimate marketers (or high volume email deployers). But there are things spammers do that I never expect to see a legitimate marketer doing.
I’ve written about these things throughout the blog (tag: TWSD), but it’s probably time to actually pull them together into a single post.

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