Outrunning the Bear

bear
You’ve started to notice that your campaigns aren’t working as well as they used to. Your metrics suggest fewer people are clicking through, perhaps because more of your mail is ending up in junk folders. Maybe your outbound queues are bigger than they used to be.
You’ve not changed anything – you’re doing what’s worked well for years – and it’s not like you’ve suddenly had an influx of spamming customers (or, if you have, you’ve dealt with them much the same as you have in the past).
So what changed?
Everything else did. The email ecosystem is in a perpetual state of change.
There’s not a bright line that says “email must be this good to be delivered“.
rideInstead, most email filtering practice is based on trying to identify mail that users want, or don’t want, and delivering based on that. There’s some easy stuff – mail that can be easily identified as unwanted (malware, phishing, botnet spew) and mail that can easily be identified as wanted (SPF/DKIM authenticated mail from senders with clean content and a consistent history of sending mail that customers interact with and never mark as spam).
The hard bit is the greyer mail in the middle. Quite a lot of it may be wanted, but not easily identified as wanted mail. And a lot of it isn’t wanted, but not easily identified as spam. That’s where postmasters, filter vendors and reputation providers spend a lot of their effort on mitigation, monitoring recipient response to that mail and adapting their mail filtering to improve it.
Postmasters, and other filter operators, don’t really care about your political views or the products you’re trying to sell, nor do they make moral judgements about your legal content (some of the earliest adopters of best practices have been in the gambling and pornography space…). What they care about is making their recipients happy, making the best predictions they can about each incoming mail, based on the information they have. And one of the the most efficient ways to do that is to look at the grey area to see what mail is at the back of the pack, the least wanted, and focusing on blocking “mail like that”.
If you’re sending mail in that grey area – and as an ESP you probably are – you want to stay near the front or at least the middle of the grey area mailers, and definitely out of that “least wanted” back of the pack. Even if your mail isn’t great, competitors who are sending worse mail than you will probably feel more filtering pain and feel it sooner.
Some of those competitors are updating their practices for 2015, buying in to authentication, responding rapidly to complaints and feedback loop data, and preemptively terminating spammy customers – and by doing so they’re both sending mail that recipients want and making it easy for ISPs (and their postmasters and their machine learning systems) to recognize that they’re doing that.
Other competitors aren’t following this years best practices, have been lazy about providing customer-specific authentication, are letting new customers send spam with little oversight, and aren’t monitoring feedback and delivery to make sure they’re a good mail stream. They end up in the spam folder, their good customers migrate elsewhere because of “delivery issues” and bad actors move to them because they have a reputation for “not being picky about acquisition practices“. They risk spiraling into wholesale bulk foldering and becoming just a “bulletproof spam-friendly ESP”.
If you’re not improving your practices you’re probably being passed by your competitors who are, and you risk falling behind to the back of the pack.
And your competitors don’t need to outrun the bear, they just need to outrun you.

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Hunting the Human Representative

Yesterday’s post was inspired by a number of questions I’ve fielded recently from people in the email industry. Some were clients, some were colleagues on mailing lists, but in most cases they’d found a delivery issue that they couldn’t solve and were looking for the elusive Human Representative of an ISP.
There was a time when having a contact inside an ISP was almost required to have good delivery. ISPs didn’t have very transparent systems and SMTP rejection messages weren’t very helpful to a sender. Only a very few ISPs even had postmaster pages, and the information there wasn’t always helpful.
More recently that’s changed. It’s no longer required to have a good relationship at the ISPs to get inbox delivery. I can point to a number of reasons this is the case.
ISPs have figured out that providing postmaster pages and more information in rejection messages lowers the cost of dealing with senders. As the economy has struggled ISPs have had to cut back on staff, much like every other business out there. Supporting senders turned into a money and personnel sink that they just couldn’t afford any longer.
Another big issue is the improvement in filters and processing power. Filters that relied on IP addresses and IP reputation did so for mostly technical reasons. IP addresses are the one thing that spammers couldn’t forge (mostly) and checking them could be done quickly so as not to bottleneck mail delivery. But modern fast processors allow more complex information analysis in short periods of time. Not only does this mean more granular filters, but filters can also be more dynamic. Filters block mail, but also self resolve in some set period of time. People don’t need to babysit the filters because if sender behaviour improves, then the filters automatically notice and fall off.
Then we have authentication and the protocols now being layered on top of that. This is a technology that is benefiting everyone, but has been strongly influenced by the ISPs and employees of the ISPs. This permits ISPs to filter on more than just IP reputation, but to include specific domain reputations as well.
Another factor in the removal of the human is that there are a lot of dishonest people out there. Some of those dishonest people send mail. Some of them even found contacts inside the ISPs. Yes, there are some bad people who lied and cheated their way into filtering exceptions. These people were bad enough and caused enough problems for the ISPs and the ISP employees who were lied to that systems started to have fewer and fewer places a human could override the automatic decisions.
All of this contributes to the fact that the Human Representative is becoming a more and more elusive target. In a way that’s good, though; it levels the playing field and doesn’t give con artists and scammers better access to the inbox than honest people. It means that smaller senders have a chance to get mail to the inbox, and it means that fewer people have to make judgement calls about the filters and what mail is worthy or not. All mail is subject to the same conditions.
The Human Representative is endangered. And I think this is a good thing for email.

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Reputation is more complex than a single number

I checked our SenderScore earlier this month, as quite a few people mentioned that they’d seen SenderScore changes – likely due to changed algorithms  and new data sources.

It sure looks like something changed. Our SenderScore was, for a while, zero out of a hundred. That’s as bad as it’s possible to get. I didn’t get a screenshot of the zero score, but I grabbed this a couple of days later:

Are ReturnPath wrong? No. Given what I know about the traffic from our server (very low traffic, particularly to major consumer domains, and a negligible amount of unavoidable backscatter due to our forwarding role addresses for a non-profit to final recipients on AOL) that’s not an unreasonable rating. And I’m fairly sure that as they get their new algorithms dialed in, and get more history, it’ll get closer. (Though I’m a bit surprised that less than 60 mails a day is considered a moderate volume.)
But all our mail is delivered fine. I’ve seen none of my mail bounce. It’s very rare someone mentions that our mail has ended up in a bulk folder. I’ve received the replies I’ve expected from all the mail I’ve sent. Recipient ISPs don’t seem to see any problems with our mail stream.
A low reputation number doesn’t mean you actually have a problem, it’s just one data point. And a metric that’s geared to model one particular sort of sender (very high-volume senders, for example) isn’t going to be quite as useful in modeling very different senders. You need to understand where a particular measure is coming from, and use it in combination with all the other information you have rather than focusing solely on one particular number.
 

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