Which Metrics Matter — Understanding your winery's email marketing data
In marketing school, we are taught that you can’t improve what you can’t measure, and this principle has survived to guide marketing professionals around the globe every decade since the 1950’s.
But when it comes to email marketing, the numbers representing opens, clicks, and bounces all mean nothing without useful interpretation.
When it comes to email marketing, it's easy to find plenty of sources on the web that will tell you which metrics they think you should be tracking:
- Open Rate
- Click-through Rate
- Conversion Rate
- Unsubscribe Rate
- Forward Rate
- Bounce Rate
- Overall ROI
- List Growth Rate
But, while these numbers might be interesting to look at, they don’t tell us what to do. It’s tempting and easy to plot them on a chart so that we can gaze proudly at lines that go up and to the right, but still, that’s not telling us anything we don't already know.
Numbers are just numbers, and while we're often drawn to the bigger ones, we've all known someone who insists that it's how you use it that really makes the difference.
This article is about how to interpret the numbers, and most importantly, how to see the relationships that will then inform intelligent actions to create positive change.
Let me give you an example.
Suppose you’re looking at your email Open Rates, that is, the number of your emails that are opened divided by the number of emails you sent (multiplied by 100 to convert it into a percentage) and you discover that 10% of your marketing emails are being opened by their recipients. Is this a high, or low number? And in either case, what does this number tell us about how to move forward?
Upon reaching the realization that this number doesn’t tell us anything about what we should do, the clever ones (like you) start asking questions: Why is it so low? (or high?) Are my emails too long? or too short? Is it the wrong font? Is the subject line boring? Am I sending too many emails? Or not enough emails?
Asking questions like these can help us generate ideas about how we could test a particular dimension, say, for example, email frequency. If we’re sending an email every week and getting a 10% open rate, what might we discover if we divided our audience into three groups and emailed one group weekly, another group bi-weekly, and a third group monthly?
Let’s say that this experiment reveals that our control group, Group 1 (weekly emails) continued to have an open rate of 10%, Group 2 (bi-weekly emails) had an open rate of 15%, and Group 3 (monthly emails) had a whopping 20% open rate. Naturally, you declare that from this day forth, emails shall only be sent on a monthly basis, since a monthly frequency is the obvious “sweet spot”.
But wait, what if we notice that the click-through rate (the number of recipients that click on a link within your email, which takes them to your website) remains at a steady 3% for all groups?