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The source of deliverability problems

Most deliverability problems don’t start where many people think they do. So very often people call looking for deliverability help and tell me all about the things they’re doing to reach the inbox. They’ll tell me about content, they’ll tell me about bounces, they’ll talk about complaints, engagement, opens and clicks. Rarely will they bring up their list source without some prompting on my part.

The reality is, though, that list source is to root of deliverability success and deliverability problems. Where did those addresses come from and what do the people who gave them think you’re going to do with them?
Outsourcing collection to a third party can cause significant issues with delivery. Letting other people collect addresses on your behalf means you lack control over the process. And if you’re paying per address, then there monetary incentive for that company to pad the list with bogus addresses.
Sometimes there are even issues with having your own employees collect addresses from customers. For instance, a retailer requires sales associates collect a minimum percentage of addresses from customers. The company even ties the associates’ evaluations to that percentage. Associates have an incentive to submit addresses from other customers. Or a retailer will offer a discount for an address and customers want the discount but not the mail, so they give a fake address.
All of these things can affect deliverability.
Address collection is the key to delivery, but too many companies just don’t put enough attention to how they’re collecting addresses and entering into the relationship with subscribers. This is OK for a while, and delivery of small lists collected like this can be great. But as lists grow in size, they come under greater scrutiny at the ISPs and what used to work doesn’t anymore.
The first step to diagnosing any delivery problem is to look at the list. All of the things ISP use to measure reputation measure how well you’re collecting addresses. Changing IPs or domains or content doesn’t change the reason mail is being filtered. It just means the filters have to figure out something new to key on.
Want great deliverability? Start with how you’re collecting addresses.
Want to fix deliverability? Start with how you’ve collected addresses, how you’ve stored them and how you’ve maintained them.
 

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Truths and Myths about email deliverability

virtualShow_forblogKen Magill will be interviewing me on the Truths and Myths of Email Deliverability, November 12 at the 2015 All About eMail Virtual Conference & Expo. Ken has a bunch of questions he wants to ask me, but he’s also expecting to take a lot of questions from the audience as well.
Speaking of myths, there has been discussion lately about recycled spamtraps. Apparently, there are people who believe (believed?) that every ISP uses recycled spamtraps. When Hotmail and Gmail said recently they didn’t use recycled traps people got very upset that they believed something that was not true.
It’s a mess. There is so much about email that is like a version of telephone. One person says “hotmail uses recycled spamtraps” someone else repeats “big ISPs use recycled spamtraps” then then third person says “all ISPs use recycled spamtraps.” People try and correct this type of misinformation all the time but sometimes it’s hard to clarify.
So show up to our session and let Ken lob questions at me, lob some of your own and we can see what myths we can clear up.

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Dueling data

One of the things I miss about being in science is the regular discussions (sometimes heated) about data and experimental results. To be fair, I get some of that when talking about email stuff with Steve. We each have some strong view points and aren’t afraid to share them with each other and with other people. In fact, one of the things we hear most when meeting folks for the first time is, “I love it when you two disagree with each other on that mailing list!” Both of us have engineering and science backgrounds, so we can argue in that vein.
ThatsFunny
One of the challenges of seemingly contradictory data is figuring out why it seems to disagree. Of course, in science the first step is always to look at your experimental design and data collection. Did I do the experiment right? (Do it again. Always do it again.) Did I record the data correctly? Is the design right? So what did I do differently from what you did? For instance, at one of my labs we discovered that mixing a reagent in plastic tubes created a different outcome from mixing the reagent in glass vials. So many variables that you don’t even think of being variables that affect the outcome of an experiment.

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