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.
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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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August 2015: The month in review

It’s been a busy blogging month and we’ve all written about challenges and best practices. I found myself advocating that any company that does email marketing really must have a well-defined delivery strategy. Email is such vital part of how most companies communicate with customers and potential customers, and the delivery landscape continues to increase in complexity (see my post on pattern matching for a more abstract look at how people tend to think about filters and getting to the inbox). Successful email marketers are proactive about delivery strategy and are able to respond quickly as issues arise. Stay tuned for more from us on this topic.
I also wrote up some deliverability advice for the DNC, which I think is valuable for anyone looking at how to maintain engagement with a list over time.  It’s also worth thinking about in the context of how to re-engage a list that may have been stagnant for a while. A comment on that post inspired a followup discussion about how delivery decisions get made, and whether an individual person in the process could impact something like an election through these delivery decisions. What do you think?
As we frequently point out, “best practices” in delivery evolve over time, and all too often, companies set up mail programs and never go back to check that things continue to run properly. We talked about how to check your tech, as well as what to monitor during and after a send. Josh wrote about utilizing all of your data across multiple mail streams, which is critical for understanding how you’re engaging with your recipients, as well as the importance of continuous testing to see what content and presentation strategies work best for those recipients.
Speaking of recipients, we wrote a bit about online identity and the implications of unverified email addresses in regards to the Ashley Madison hack and cautioned about false data and what might result from the release of that data.
Steve’s in-depth technical series for August was a two-part look at TXT records — what they are and how to use them — and he explains that the ways people use these, properly and improperly, can have a real impact on your sends.
In spam news, the self-proclaimed Spam King Sanford Wallace is still spamming, despite numerous judgments against him and his most recent guilty plea this month. For anyone else still confused about spam, the FTC answered some questions on the topic. It’s a good intro or refresher to share with colleagues. We also wrote about the impact of botnets on the inbox (TL;DR version: not much. The bulk of the problem for end users continues to be people making poor marketing decisions.) In other fraud news, we wrote about a significant spearphishing case and how DMARC may or may not help companies protect themselves.

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Growing your list carefully

Karl Murray wrote a great set of recommendations for growing an email marketing list. I really can’t think of anything I would have said differently. Touching customers and getting contact information from them is great, but there are situations where this gets bad addresses. Too many bad addresses can impact delivery.
So how do you grow your list without falling into a delivery trap? The specific recommendations, as always, depend on your specific situation. But knowing how bad addresses get onto your list will allow you to implement mitigation strategies that actually work.

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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.
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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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