Analyze your spam complaints, even if your deliverability is fine. Part two in a four-part series.
This is the second in a series about effectively handling spam complaints. If you haven't already signed up for feedback loops with the ISPs you send to most frequently, you're going to want to read Part 1 of this series and do so.
Most senders don't look at spam complaints in detail until their deliverability is affected. This is understandable, but it's much better to be proactive and take measures to protect your deliverability. One way to do this is to analyze your spam complaints, even if your deliverability is fine.
Every ISP and anti-spam organization uses different guidelines to determine when to blacklist or block your e-mail messages. That said, there are some benchmarks you can use. With my clients, I consider a 0.1 percent spam complaint rate (one complaint per thousand e-mail messages sent) to be where deliverability problems begin. So I work with them to keep their spam complaint rates below this level. Some ISPs have thresholds as high as 0.3 percent (three spam complaints per 1,000 e-mail messages sent); once you reach this level, you are very likely to have deliverability issues.
So the first thing you should do is figure out what the spam complaint rate is, both a) on each individual send and b) monthly on all your sends.
Once you get notifications of spam complaints through your feedback loops, you're going to need to organize them so you can analyze them. The easiest way to do this is with an Excel spreadsheet. The columns you'll need are:
The first thing I do with clients is to confirm that the unsubscribe rules in place for spam complaints are being followed. In my earlier column, I mentioned that any e-mail address that lodges a spam complaint against any of your e-mail messages should be suppressed from all future mailings. This means that each unique e-mail address should appear only once in your spam complaint spreadsheet - this is true whether you're looking at complaints from a single month or a few months.
A while back I had a client who I went through this process with. This first analysis of spam complaints, simple as it sounds, highlighted a huge but easily fixed issue.
They are a very large quantity mailer, sending to members of their organization. In the three months that I analyzed, they had received over 37,000 spam complaints. This amounted to more than three spam complaints for each thousand e-mail messages they sent, over the high threshold of 0.3 percent. When I sorted by the addresses making the complaints, I found multiple complaints per address, so I took 10 percent of those recipients and analyzed their complaint rates; here's what I found:
I confirmed that this 10 percent of the complaining addresses also accounted for 10 percent of the complaints; so this was a valid sample to look at. On average, a single e-mail address lodged spam complaints about 14 different e-mail messages in this three-month period. Nearly 10 percent of the people (the mode) that complained did so regarding 21 different messages in this three-month timeframe. That's seven complaints a month, or nearly two a week.
Houston, we have a problem.
This particular client was working with an ESP. The ESP was monitoring the feedback loop and its policy was to remove the e-mail addresses that complained only from the list they complained about - not to suppress them from all future sends.
Here's the rub: the sender was pulling lists from its internal database and sending "new" lists with different names (they added the date of the send to the list abbreviation) for each send. So people that lodged spam complaints were only suppressed from the list used to send the message they complained about, which would never be sent to again.
Their spam complaint rate for this three-month period was 0.31 percent; they had recently been blacklisted and the anti-spam organization was refusing to remove the block since they were repeat offenders. If the e-mail addresses that lodged complaints had been removed from all future sends, their spam complaint rate would have been just 0.02 percent, well-below the threshold for deliverability issues and blacklisting.
Moral of the story: analyze your spam complaints before the rate reaches 0.1 percent. If you're working with an ESP, talk to them and confirm that they are suppressing e-mail addresses that lodge complaints from all future sends to all lists. If you're using an in-house solution, make sure your IT group is monitoring spam complaints and suppressing those that lodge them from all future sends.
In two weeks, we'll look at analyzing the sources of the e-mail addresses that complained and how you can use that information to proactively protect your deliverability.
Until next time,
Jeanne Jennings is a recognized expert in the email marketing industry and managing director of digital marketing for Digital Prism Advisors. She has more than 20 years of experience in the email and online marketing and product development world. Jeanne's direct-response approach to digital strategy, tactics, and creative direction helps organizations make their online marketing initiatives more effective and more profitable. Digital Prism Advisors helps established businesses unlock significant growth and revenue opportunities in the digital marketplace; our clients learn to develop and implement successful digital strategies, leveraging data and technology to better meet bottom line goals. Want to learn more? Check out Jeanne's blog and Digital Prism Advisors.
2015 Holiday Email Guide
The holidays are just around the corner. Download this whitepaper to find out how to create successful holiday email campaigns that drive engagement and revenue.
Three Ways to Make Your Big Data More Valuable
Big data holds a lot of promise for marketers, but are marketers ready to make the most of it to drive better business decisions and improve ROI? This study looks at the hidden challenges modern marketers face when trying to put big data to use.
December 2, 2015
1pm ET/ 10am PT
Wednesday, December 9, 2015
5pm HKT / 5am ET