One of the most important elements of communications is measurement. If you don't measure every output, you have no way of knowing what's working, what isn't, and where you need to improve. For e-mail marketing, there are some important statistics I keep track of, including Open Rate and Unique Clicks. The open rate tells you who is opening your e-mails, while the click-through-rate shows which links people are engaging with. However, sometimes the numbers don't line up the way we think they will. Thus, it's integral to understand what's behind the metrics.
To give you some context, e-mail marketing is a large part of my current position. In the past, it has always just been used, rather than used strategically. Over the last two months I've been tracking what we send, when we send it and who we send it to. I've been looking not only for ideal days and times, but a better way to connect with our audience.
Recently I was tasked with promoting tickets to a local event. Traditionally they'd send out a mass e-mail to over 8,000 people - including those 3,000 miles away. I couldn't see the benefit of this, so I tried something new: send out the same e-mail to three different groups. The first group (104 people) lived in the immediate area. The second group (3214 people) lived within the state but outside the immediate city. The final group (3560 people) live outside of the state.
A week later, I measured the results, expecting the highest number of opens and unique clicks to be in the first e-mail. But I was wrong, and here's why.
The first e-mail I sent not only had the lowest click-through-rate, it also had the lowest opens. The other two e-mails excelled in both areas. If I was looking only at the analytics, I would assume that group of 104 people had no interest in their home-town baseball team. I wouldn't send any future communication about The Red Sox, and would probably limit anything about baseball also. I would be incorrect (because who loves The Red Sox more than Bostonians?)
Instead, I had to look beyond those analytics. Why wouldn't a group of people from Boston want to read an e-mail about purchasing Red Sox tickets? Because they can get them anywhere. It's not because they're uninterested, it's because they are too interested and already have a place to get their information. Instead, the groups outside of Boston who didn't have direct access to tickets found more value in my information.
As you can see, looking at a single analytic (such as the open rates on one e-mail) isn't enough. You need to study analytics over a period of time, and understand the effects external factors may be having on your communication.
Do you study your e-mail marketing analytics? If so, which statistic is most important to you?