Most e-mail marketing tools will easily give you a report of open, click through, bounce rates conversions, etc. But most of the time these numbers are for the entire list that was sent it to. A tricky business, because it doesn’t say anything about the variance between different parts of your list.

Unless you separate statistics in groups, the general outcome can hide the most valuable, actionable, information.

No email alarm went off

If you send an e-mailing with a certain open rate, you might think your entire list has the same performance. You might even think that all metrics up to conversion and unsubscribe rates are average across your complete list. Small changes in the average aren’t likely to set off any alarm. But the underlying groups might say something different.

Here is an example. I am showing clickrate because most email marketers will be able to report these absolutes and / or percentages from their e-mail system. Preferably you want to see what is going on in the whole process. Send > opens > clicks > conversions. To keep it simple, here the example with clickrate.
emailmarketing statsistics example

Averages hide differences in groups

Suppose above were your 3 target groups. You might want to change something in that message in group C. And why is group A performing so much better? These metrics might fuel a great deal of additional testing and improvement. Another example, suppose group C contained a lot of addresses from a certain provider or just a lot of old addresses? You might have a delivery or rendering problem that needs fixing right now.

Averages hide huge changes

emailmarketing statistics example two

Now look at the  statistics of the next email above. We sent it to exactly the same groups and got exactly the same average performance. But whoa, what happened! Group A performed much worse. And C did  better. As we all can see this offers much more refined information can result into more targeted messaging. Imagine that this was the result of an optimization test. Only looking at averages, the performance would be the same as a control group, but it isn’t!

Catching grouped differences in email marketing

By only looking at the average performance, we would never know that there were differences per segment. Often marketers don’t even realize these groups exist! The results of your small group of best customers is overshadowed by the rest of that big list. There are some things you could look into right now to find these groups. Think about differences per domain, where the subscribers came from (the source) and click behavior.

Adding user profile information to your email list

But if you want to look at the people behind these e-mail addresses you need some more information about them. Age, products bought, interests, life-style, location, these could all be predicting the match with your messages and offers and the outcome of e-mail success. So be sure that you build user profiles and ask for this information.

Don’t settle for average (e-mail marketing statistics)

By digging deeper and running some statistical analysis, you will find those differences if they are present. And you might find that changes are needed in your current segmentation. Don’t be lured into thinking you are doing alright be only looking at the average statistics.