In a perfect world, email copywriters could follow a very simple writing formula. They’d type in the same magic words for every single client and get huge sales week after week. Clients would be thrilled. Copywriters would be thrilled. Everyone would be thrilled.
In the real world, it doesn’t always work out that way.
Each client is different, and so is their market. While there are tried-and-true copywriting tactics that improve your chances of getting great results, there can also be a lot of trial and error along the way as you figure out what your audience responds to.
Assessing what works and what doesn’t can be as simple as making sense of the data you already have. Each email marketing platform will tell you data like open rate, click through rate, and unsubscribe rate for each email. By looking at the copy and some other details from over- and under-performing emails, we can get a better idea of what is and isn’t working.
I recently did an audit of our email performance for one of my clients, a very cool Austin-based product manufacturer. Here, I’m walking you through how I organized my data, and what I learned about how my copy and our strategy affected some key email marketing metrics. You can then recreate this process for yourself to analyze your own email marketing.
Setting Up the Data
To start, I charted out what I know. My client tracks some of this data through their email platform, and I pulled some other details like word count, whether the email was launching a new product, and number of links per email by reviewing our campaigns. I plotted all the following information in a Google Sheet.
- Date sent
- Day of week sent
- Time of day sent
- Number of recipients (this client has several segmented lists ranging in size from about 1,500 to 80,000+ subscribers)
- Email subject line
- Word count
- Whether the content included a new item launch or a giveaway
- The winning & losing calls to action (more on this in a minute)
- Number of links in the email
- Open rate
- Click-through rate (CTR)
- Unsubscribe rate
- Revenue generated
- Conversion rate
Confused about some of those terms? Here are a few quickie definitions:
Open Rate: Percentage of email recipients who open the email
Click-through Rate: Percentage of recipients who click on a link in your email
Unsubscribe Rate: Percentage of recipients who unsubscribe from your email list
Conversion Rate: Percentage of recipients who go on to make a purchase
When I had it all set up, I wanted to figure out our averages for each of the above metrics. This was for two reasons.
1) I want to know how we’re stacking up against the overall e-commerce industry; and
2) I want to see which subject lines, CTAs, and copy performed better or worse than our average.
I found that across 40 emails, we had achieved the following averages:
My Client’s Avg.
1% – 2%
My Client’s Open Rate: 22.99%
Industry Average Open Rate: 5.68%
My Client’s CTR: 3.64%
Industry Average CTR: 2.01%
My Clients’s Unsubscribe Rate: 0.24%
Industry Average Unsubscribe Rate: 0.27%
My Client’s Conversion Rate: 4.39%
Industry Average Conversion Rate: 1% – 2%
So compared to industry averages, my client is doing well! But we can always do better.
Making Sense of the Data
Once my spreadsheet was all set up, I needed to assess which emails performed well and which didn’t. It’s a lot of data, and it can get overwhelming. Just take one email marketing metric at a time.
Open rates are probably the easiest data point to start with.
When analyzing the click-through rate and conversion rate, there may be many factors that get a reader to take action. But with the open rate, the only real connection point is the subject line.
Remember that our average open rate was 22.99%. I figured anything between 20.99% and 24.99% was probably “normal.” So I examined any open rate outside of that range to see what to avoid or what to try more often in my copywriting.
I found a few interesting tidbits, like this one: the emails where we give away free stuff aren’t performing nearly as well as they should! In fact, out of 10 giveaways, the open rates for seven were below average, one was average, and only two were above average.
What does this tell me?
Here’s my hypothesis. I often use the words “free” or “giveaway” in the email subject lines for this type of freebie. I’m guessing that these words may be triggering spam filters and preventing our subscribers from seeing the emails at all.
So what will I do with that information?
I’ll need to test my hypothesis. Next time we have an email with a giveaway, I’ll write two subject lines. One will include a possible spam-filter trigger word, and one will not. We’ll test out the subject line on small sections of the subscriber list, and see which performs better! Then, we’ll send the email with the winning subject line to the rest of the list. This is called A/B testing, and it’s the best way to test a theory like this in your copywriting.
You also can look for other patterns, like open rates on certain days of the week or at certain times of day. But no tricks of timing will get your emails open if you have a boring subject line.
The next number for us to look at is the click-through rate. With this client, our emails are sales-oriented, trying to get readers to click through to the company website and make a purchase. So for the emails that have a high click-through rate, what seems to be working? And for those with a low rate, why might that be?
When you work on something like this, there are a lot of variables to consider, and it can get overwhelming. I knew I wanted to look at:
- Word count (aka, length of the email
- The email copy itself
- Whether the email was launching a new item
- The number of calls to action (CTAs)
- The copy in each call to action
Just take it one thing at a time. While each different variable works in tandem with the others, you can only test a single change in a single email. So come up with a few theories and test them out in the coming weeks and months.
What did I find? Sometimes there are useful takeaways, and sometimes there are no clear patterns. For example, I found that emails with fewer than 30 words seem to get fewer clicks than emails with 30 to 40 words. But above 40 words, there’s no obvious winner or loser.
I also looked for trends related to “action” words in my CTAs. We’ve been A/B testing our calls to action over the past two months, so I have both the “winning” CTAs and the “losing” CTAs to look at. I found that “browse” doesn’t get the click. And “shop” is all over the map, with some of the worst and the best CTRs. So the jury is still out on that one.
This type of analysis will help me figure out what speaks to our customers and what kind of messaging falls flat.
The unsubscribe rate for this client’s email marketing campaigns is great overall. They have an engaged audience who loves their products.
I did notice that their giveaway and new product emails have the lowest unsubscribe rates, which supports the theory that those emails are being caught by the spam filter!
This is the most important email marketing metric of all. Everything else is just support for this!
Remember, this is how many people go on to make a purchase. So ultimately, your email conversion rate will also be impacted by what your landing or sales page looks like, and how well-optimized it is. But you may find some interesting insights in your emails as well.
In this email audit, I found an interesting correlation between the number of CTAs in each email and the conversion rate. These emails are very short and image-heavy, and each usually has 1 to 3 CTAs linking to different products or product categories.
I found that when there are 1 or 2 CTAs in the email, the conversion rate hits an average of 4.69%. But when there are three, that drops to 3.34%! This percentage difference represents nearly $1,000 in revenue per email!
It looks like we’re overwhelming our subscribers with too many buttons. Including fewer links may help with our overall conversion rate. I’m proposing this change to my client. With the extra effort of this audit, we may be able to get thousands more in sales without spending another penny on marketing or advertising.
There are more KPIs and metrics that you can dive into as you optimize your marketing efforts. But if you start with a thorough analysis of these four, you may be able to improve your email marketing performance at no extra expense.
An audit like this doesn’t require any special education or tools. All you need is some time, a spreadsheet, and the ability to look for patterns. Keep careful notes and test out your ideas on your future marketing campaigns to see if your theories are correct!