Improving your reputation
In the previous blogs (Part 1 : Part 2) we’ve looked at the basics of Email reputation and what the constituent parts of it are. I’m not about to teach you how to improve your Domain reputation (I’m not an SEO or PPC person – I work with emails). So that you have an understanding of what we’re going to look at next, I’d advise you to go back and read the previous two blog articles before progressing further. It’s not strictly speaking necessary, however you will understand why we’re looking into the details in this article.
Let’s assume a couple of things here. One, you’ve run at least one campaign to send a message to your subscribers. Two, you’re able to extract reporting or list information from the platform (don’t forget we not only have online help, we’ve got Account Managers to help if you truly get stuck).
Aside from the information you need to report back to your company on Opens, Clicks, Subscriber rates, etc, there are a number of factors that bear close observation if you want to maintain and/or improve your reputation yourself of course.
You’ve (hopefully) invested a lot of time and effort in getting your company’s market image to a nice place. Ensuring that that is maintained requires (like a garden) quite some effort on your part but the results (to maintain the gardening analogy and to line up a nice pun) will always bear fruit.
Here’s something you can do to check your data for quality. This exercise is a simplified version of what we can do for you with our Data Analysis service. Export your master list from within our Platform and load it into Excel. Let’s assume two things:
- Row 1 contains column headings (e.g. email, first name, last name, Title, Job Role, company, etc)
- Column A therefore (as visible above) contains email addresses.
Now let’s (temporarily) overwrite column C. Start at Cell C2 and put the following formula in there:
=LEFT(A2,FIND(“@”,A2,1)-1)
That should show you something like
And that’s fine because we know that that looks like a real name. Now fill that column downward to the bottom. Once you’ve done that, set up a pivot table based on Column C. You want that data in both the Rows and as a count in the Values. Got that? Good. Now look for addresses such as:
- Office
- Admin
- Manager
- Info
- Abuse
- Admin
- Webmaster
In short, what you’re looking for are addresses that if you checked them you would have no idea to whom the email was to be sent. They are bad things to try to target, particularly if you’re sending to overseas companies where culturally different value judgements can create undesirable results. These are the sorts of email addresses you really shouldn’t target (unless of course, you have explicit permission to do so). I’m going to hazard a guess that you’re likely to see several addresses like these where the value alongside it is significantly greater than 1, particularly if the data has been collated over a number of years and has a large number of recipients.
It’s also worth looking at Column B, which will tell you if an email address is valid or not. If not, no point in trying to target it.
Our advice? Opt these addresses out if you cannot see any interaction with them in the last six months. Then do some list hygiene, which we’ll get to in a later post.
By interaction, I mean a click on a link as a bare minimum. The problem with generic addresses is that they’re likely to be corporate entities. And if they’re corporate entities, then they are likely to take email security seriously.
On that basis, a recorded OPEN may not necessarily reflect an actual open. That is because security packages are required to scan the message before delivering it to the inbox of the intended recipient. This often appears as though an individual has opened the message, whereas in reality the message hasn’t even reached its destination yet. Remember, sending messages to recipients that have provided permission to you is always going to garner better results.
With this in mind, it’s probably worth checking to ensure that you have proof of opt-in for all the data. Targeting data that you don’t have proof of optin for is a very bad practice. It’s also worth checking to see where and when the data you can’t easily identify came from. If it’s more than 6 months old, it’s quite likely that unless you’ve got identifiable interactions with the recipient in the intervening period that this person is probably no longer interested in your emails. I’m sorry I had to be the one to break the bad news to you.
However, all is not lost. Speak to our Customer Success Team about sensible ways to re-engage these people and you never know, they may just convert to a valuable resource for you. Remember of course that ROI on email is significant, so you do have a degree of overhead that you can use to hopefully recoup some of this potential audience.
In the next article we’ll look at that scary part where people stop interacting with you and what can be done to get more bang for your buck, so to speak!
