Four Terms. Two Equations. One Test.
According to IBM, there are 2.5 quintillion bytes of data created every day. What’s a quintillion? It looks like this—1,000,000,000,000,000,000. Now that’s why they call it Big Data.
But, evaluating the performance of your email campaigns can be much simpler. The number you need looks like this—4. That’s right. You can significantly improve the performance of your email campaigns by knowing and understanding just four data points.
In this article, I’ll define each of these measurements, review what impacts them, and share some easy math tips that are guaranteed to make you smarter than most of the people around you who might be sticking their noses into your email performance data.
The Four Key Data Points
Open Rate: If you manage an email campaign of any size, then you’re familiar with an open rate. It measures the number of people who opened your email relative to the number of people who received it. For example, if 100 people received your email and 20 people opened it, then your open rate is 20%.
Click-through Rate: Once again, this is a term with which you’re likely familiar. It measures the number of people who clicked on a link in your email relative to the number of people who received it. For example, if 100 people received your email and 10 people clicked on a link, then your clickthrough rate is 10%.
Click-to-Open Rate: This might be a new one for you, but it’s incredibly important (and my favorite of the four). It measures the number of people who clicked on a link in your email relative to the number of people who opened it. For example, using the numbers we provided above, if 20 people opened your email and 10 people clicked a link, then your click-to-open rate is 50%.
The impacts for your click-to-open rate are the same as for the clickthrough rate. But, since this measurement only accounts for those who open your email, it’s much better than the clickthrough rate in truly measuring the strength of your email content.
Unsubscribe Rate: Don’t be afraid of this number. It’s an important tool for maintaining a healthy email file. It measures the number of people who clicked to unsubscribe relative to the number of people who received the email.
What Can Be Learned From Each Data Point
Knowing any of these rates is easy. After all, even the simplest email system will provide them to you. The key to improving your email program is understanding what impacts each of these rates and how you can improve them.
Impacting Open Rates
Open rates are about much more than just the subject line. Here are four things you can learn when analyzing your open rate data.
- Did I use the right sender? Sometimes a new sender name can provide a jolt to your open rate. But, it could also backfire if the name is unrecognized by your list. A one-time email from a celebrity endorser could also significantly impact the open rate
- Was my spam score low? As an email marketer, it’s your job to know the words that service providers are flagging as spam. A sudden drop in open rates could indicate your email was flagged as spam by one or multiple ISP’s. Considering using a spam alert service that will score your email before you send it, and identify factors that could increase the likelihood of your email being sent to the Junk Folder.
- Am I sending too many emails? We all know those organizations that send far too many emails. It becomes almost an instant delete when you receive them. On the other hand, you don’t want to wait so long to send your next email that your volunteers lose interest or forget they were involved with you. Know your audience (by using email performance data) to determine the proper frequency for your email sends. If open rates are dropping, you might consider changing your pacing.
Impacting Clickthrough and Click-To-Open Rates
There is no shortage of people who have proclaimed, “Content is king.” And, it’s still true today. If you’re using formatting gimmicks to overcome bad writing, then your focusing in the wrong place. Good writing is critical to strong engagement and high clickthrough and click-to-open rates. But, there are additional factors as well.
- Am I leveraging personalization opportunities? Today’s more advanced CRM systems and data appends allow email marketers to personalize emails far beyond just including the first name. Your use of these tools to recognize actions takers and donors, pinpoint locations and customize your content can boost your clickthrough rates.
- Did my formatting make it hard for people to read my email? Do you remember when emails used to have multiple font sizes, lots of font colors and individually hyperlinked words scattered throughout the email? It was as if the sender wanted you to print their email and hang it on the wall in a frame as artwork. Simple formatting using consistent templates is the best way to ensure poor formatting doesn’t hurt your email performance.
- Are my volunteers willing to complete this action? All asks are not created equal. Your email asking people to sign a petition will always have higher clickthrough rates than the message asking them to donate. That doesn’t mean your petition email was that much better. It was just an easier ask. When analyzing your email data, be sure to keep the ask in mind.
Impacting the Unsubscribe Rate
In short, see above. Many of the things that impact your open and clickthrough rates will drive up your unsubscribe rate if done poorly. Bad formatting, unfamiliar senders, irrelevant content and controversial issues can all drive your volunteers to the exits. Know your standard unsubscribe rate, and be sure to investigate emails that show a rise.
Also, be sure you’re sending messages to only the appropriate segment(s) of your volunteer list. Let’s be clear – that’s not the people whom you want to receive your email, but the people who have indicated (either directly or indirectly) that they want to receive it. At our organization, we learned that 80 percent of our volunteers aren’t passionate about fighting cancer generally, they’re passionate about fighting the cancer that impacted them. That means a volunteer who wants to increase funding for breast cancer research isn’t necessarily interested in improving access to colon cancer screenings. Be sure to understand how your volunteers’ interests break down.
You Can Be the Math Genius in Your Office
If you’re a classic right-brainer, this headline could send you into a cold sweat. Keep shivering, but also keep reading, because I’m going to show you two simple math tricks that will help you add a little left-brain to your portfolio of expertise.
Conducting a simple analysis of your email data requires you to understand the proper way to compare rates between two emails. Let’s start with simple numbers to explain the math equations, and then we’ll use a more realistic data example.
Email A has an open rate of 20%. Email B has an open rate of 30%.
If the question you’re asking yourself is what’s the difference between the two rates, then you’re asking the wrong question. You need to break that thought down into two questions: How much higher is the open rate for Email B and how much lower is the open rate for Email A. Each question has a different answer and neither is 10%.
Answering question 1: How much higher is the open rate for email B?
- The answer is 50%. Here’s the math equation: (30 – 20) / 20 = .50 which is 50%.
- The explanation: Subtract the two open rates and divide by the lower rate.
Answering question 2: How much lower is the open rate for email A?
- The answer is 33%. Here’s the math equation: (30 – 20) / 30 = .33 which is 33%.
- The explanation: Subtract the two open rates and divide by the higher rate.
The basic rule of thumb is that if you want to know how much higher the rate is, then divide by the lower number. If you want to know how much lower the rate is, divide by the higher number.
Confused? Lets’ make it even more simple. Imagine that you have four cookies and your friend has two cookies. You have double the amount of cookies she has, and thus 100% more. That math equation looks like this: (4 – 2) / 2 = 1.0 which is 100%.
When comparing big whole numbers like 30 vs 20, it can be very easy to see that one open rate was significantly higher than the other. Of course, real email data doesn’t work that way. Let’s look at a more realistic data result.
The clickthrough rate for Email A is 4.11%. The clickthrough rate for Email B is 4.57%.
Before reading this section, you might have seen those numbers and been disappointed that the difference is an inconsequential half a percent. But, now you know better. Using our simple math equation, you know the clickthrough rate for Email B is actually 11% higher than Email A – a very significant difference.
Here’s the equation: (4.57 – 4.11) / 4.11 = .11 which is 11%.
What Works Best for Optimizing My Email Performance?
While the answer is different for every organization, the path to that answer is always the same: Test! Test! Test!
Conducting A/B tests is the best and easiest way to understand how to optimize your program. It will improve all four rates we’ve discussed as well as, most importantly, your action rate.
There are two basic ways to run a test. I call them the Full Test and the Pre-Test. (Perhaps in the comments you can suggest better names.) To illustrate the differences between the two, let’s assume you want to test two different subject lines.
For the Full Test, you’ll send each version of your email to 50 percent of your list – half of your list receives subject line A and the other half receives subject line B.
Full Test Pros:
- It’s fast and easy, only requiring one send of your email
- Your universe doesn’t need to be as large as the Pre-Test
- You can apply the results to your future emails
Full Test Cons:
- Half of your list will receive a non-optimized subject line
- You don’t have an immediate opportunity to leverage what you learned
Conducting a Pre-Test is the ideal, but it does require more time and a larger list. How much time you need will depend on the typical behaviors of your volunteers.
For this test, you’ll begin by sending each version of your email to only 15 percent of your list (for a total 30 percent send). Now, you wait. For my organization, about 90 percent of those who will open an email and take action do so within the first 24 hours, so that’s how long we like to wait before determining which subject line performed best. Once we identify the winning the subject line, we send it to the remaining 70 percent of the list.
- It’s still easy
- You can apply your learnings immediately to that same email
- A far larger percentage of your list (85 percent in the above example) receives the optimized version of the email thus increasing overall performance rates
- You need time to determine the winner (this might not work if you just learned there will be a vote on your issue tomorrow)
- Since your initial test emails are going to a smaller percentage of your list, the overall universe must be larger to allow for statistically significant results
Yes. This isn’t just for polling, but is also very important for your email tests.
To put it simply, statistical significance means the results of your test weren’t likely to occur randomly or by chance. To achieve this, you need a proper size universe and enough difference between the test results. A big thanks to M&R who created an easy-to-use tool to help you determine statistical significance for your email tests.
Four Terms. Two Equations. One Test.
In a world of Big Data, it’s easy to get overwhelmed by all the information available to you. Knowing just these four terms, two simple math equations and how to run one test, you’re empowered to not only effectively analyze the performance of your email campaigns, but to make the necessary changes to optimize them.
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