Google takes on intrusive interstitials

Starting next January, Google will be modifying its mobile search results to lower the ranking of sites that use interstitials that interfere with the users experience. In a blog post announcing the change they explain:

Pages that show intrusive interstitials provide a poorer experience to users than other pages where content is immediately accessible. This can be problematic on mobile devices where screens are often smaller. To improve the mobile search experience, after January 10, 2017, pages where content is not easily accessible to a user on the transition from the mobile search results may not rank as highly.

Search
While this doesn’t have any effect on email delivery, I think it’s noteworthy to mention here for 2 reasons.
First, many interstitials are subscription boxes. If subscription boxes are considered an “intrusive interstitial” then websites may suffer lower visitation due to lower Google ranking. This will result in fewer signups from mobile devices. Removing the interstitial will reduce signup rates, another unwelcome consequence to this change. I don’t have a good solution, although it may be as simple as not showing interstitials to users coming directly from Google. Folks who use interstitials for signups should be looking at this issue now.
Second, it clearly demonstrates the priority Google puts on user experience. Many users get frustrated when they go to a site and there is immediately something blocking the information they’re looking for. Google has heard this and is trying to make their results less frustrating for users. This attitude is also a part of their filtering and blocking decisions. Mail that is deemed annoying or frustrating for users may go to the bulk folder, even when they’re lacking overt spam signs. We’ve certainly seen cases where mail gets filtered with no clear reason other than “people have reported mail like this as spam.”
Overall, I think consumers will appreciate the new search ranking algorithm. I think marketers are going to have to adapt in many ways, not the least of which is figuring out how to collect email addresses without compromising search engine rankings.

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Things to read: March 9, 2016

It’s sometimes hard for me to keep up with what other people are saying and discussing about email marketing. I’ve been trying to be more active on LinkedIn, but there are just so many good marketing and delivery blogs out there I can’t keep up with all of them.
talkingforblog
Here are a couple interesting things I’ve read in the last week.
Five Steps to Stay Out of the Spam Folder. Conceptually easy, sometimes hard to pull off in practice, these recommendations mirror many things I say here and tell my clients about delivery. The audience is in charge and your recipients are the best ally you can have when it comes to getting into the inbox.
Which states are the biggest sources of spam?. California and New York top the list, but the next two states are a little surprising. Over on Spamresource, Al points out the two next states have some unique laws that may affect the data. I just remember back in the day there were a lot of spammers in Michigan, I’m surprised there’s still a significant volume from there.
CASL didn’t destroy Canadian email. Despite concerns that CASL would destroy the Canadian email marketing industry, the industry is going strong and expanding. In fact, spending on email marketing in Canada was up more than 14% in 2015 and is on track to be up another 10% this year. Additionally, according to eMarketer lists are performing better because they’re cleaner.
A brief history of email. Part of the Guardian’s tribute to Ray Tomlinson, the person who sent the first email. Ray’s work literally changed lives. I know my life would be significantly different if there wasn’t email. Can you imagine trying to be a deliverability consultant without email? 🙂

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Port25 blocking

biohazardmailA number of hosting providers are blocking outgoing port25. This has implications for a lot of smaller senders who either want to run their own mail server or who use SMTP to send mail to their ESP.

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Trawling through the junk folder

As a break from writing unit tests this morning I took a few minutes to go through my Mail.app junk folder, looking for false positives for mail delivered over the past six weeks.
trashcans
We don’t do any connection level rejection here, so any mail sent to me gets delivered somewhere. Anything that looks like malware gets dumped in one folder and never read, anything that scores a ridiculously high spamassassin score gets dumped in another folder and never read, mailing lists get handled specially and everything else gets delivered to Mail.app to deal with. That means that Mail.app sees less of the ridiculously obvious spam and is mostly left to do bayesian filtering, and whatever other magic Apple implemented.
There were about thirty false positives, and they were all B2C bulk advertising mail. I receive a lot of 1:1 mail, transactional mail and B2B marketing mail and there were no false positives at all for any of those.
All the false positives were authenticated with both SPF and DKIM. All of them were for marketing lists I’d signed up for while making a purchase. All of them were “greymail” – mail that I’d agreed to receive, and that was inoffensive but not compelling. While I easily spotted all of them as false positives via the from address and subject, none of them were content I’d particularly missed.
Almost all of the false positives were sent through ESPs I recognized the name of, and about 80% of them were sent through just two ESPs (though that wasn’t immediately obvious, as one of them not only uses random four character domain names, it uses several different ones – stop doing that).
If you’d asked me to name two large, legitimate ESPs from whom I recalled receiving blatant, blatant spam recently, it would be those same two ESPs. Is Mail.app is picking up on my opinions of the mail those ESPs are sending? It’s possible – details specific to a particular ESPs mail composition and delivery pipelines are details that a bayesian learning filter may well recognize as efficient tokens.

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