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A Marketer’s Guide to A/B Testing Email Campaigns

A/B testing emails

There is no denying the fact that better click-through rates in emails lead to more website visitors. But in order to maximize CTRs, any marketer would first need to maximize the open rates. Add to the mix the complexities of modern-day emails and even veteran marketers would need breathers to recoup.

How do you then reach the most optimal version of your emails? The answer lies in A/B testing to reap better results from your marketing efforts.

In this guide, we discuss the what, why, and how of improving your email marketing campaigns with the help of A/B testing.

Table of Contents:

What is A/B Testing of Emails?

To put it simply, A/B testing or A/B Split testing is the process of sending out multiple versions of your emails with different variables to test their performance. This gives you more control over your email marketing campaigns and helps you see which version of the email performs the best.

The idea is to send one version of the email to one subset of your subscribers and another version to another subset. Then, you measure the performance (read rates, click-through rates) of both versions to see which one performs better. You then roll out the winner across all your other subscribers.

In other words, A/B testing of emails shows marketers how to evaluate, compare, and decide between multiple versions of the same email.

Why should you A/B Test Emails?

Email A/B testing uses statistical methods to prove which version of an email brings you the best results. As such, it allows marketers to test various variables in their emails and then see actual results before rolling out improvements across all their subscribers.

This naturally brings forth several benefits that boost your campaigns as well as improve your overall marketing efforts:

Key statistics about A/B testing of emails that you should know

Let’s take a look at some key statistics that depict the importance of A/B testing in emails. These have been curated and sourced from 99firms:

What are the key components of A/B Testing?  

A/B testing requires marketers to run campaigns with multiple variables. Each of these variants (versions) should be tested against each other – and this is where the science comes in.

By running A/B tests across all possible combinations of your email variations, you are likely to come up with an optimal version that would convert better than any other version. You can roll out the same across your entire subscriber list to establish a benchmark for future email campaigns.

With that being said, here are the key components that you should consider when carrying out A/B Testing in Emails:

In short, A/B testing provides measurable insights into what is working and what isn’t in your emails. It can help you identify which subject line has a higher open rate or which image would leave a greater impact on subscribers.

Which variables can you A/B test in your email campaigns?  

Email marketers have a lot of flexibility when it comes to testing different variables. In fact, any factor that might affect the success of an email campaign is open for testing:

Which variables should you NOT A/B test in your email campaigns?

While you can split test most variables related to an email campaign, there are some that cannot or should not be tested:

A/B testing is an excellent way for marketers to establish what works best for their audience and weed out ineffective email components. However, even though A/B tests can help determine which version performs better, it’s not always easy to identify the reason behind a higher CTR. That’s why it is crucial for marketers to establish an optimization approach that would allow them to improve their email campaigns systematically.

What are some factors to keep in mind while carrying out A/B Testing in Emails?

The more variables you test for, the longer it will take to carry out your tests. For instance, if you were testing 20 different email variables by splitting them into two alternative tests (2 x 20), each test may take 1-5 days to complete (for example). And with each passing day, the relevancy of your email’s subject line diminishes. This is why you should test for just two or three variables at a time so that your tests are completed in one or two days.

A/B testing requires you to send out different versions of the same email at the same time! This would mean that your A/B test will not run in isolation and can impact your deliverability rate. If you wish to run an A/B test without affecting your deliverability, you can send it to a separate email sending list.

What are some of the benefits of A/B Testing in Emails?

A/B testing is extremely useful when it comes to improving email click rates, open rates, and conversion rates. Much like scientific experiments where one formulates a hypothesis and tests it against another version of itself, marketers can test each variable of an email campaign to find out which one performed better. A/B testing can also be very beneficial in improving content engagement rates by understanding the preferences of your audience.

It can improve overall marketing productivity because you won’t have to wonder if any single component of your email is responsible for low click-through rates or high unsubscribe rates.

Important Terminologies to Know Before You A/B Test Emails

Key email A/B testing terms include:

What are the different methods to A/B Test Emails?

For best results, it is always advisable that your email marketing solution provides the functionality to A/B test emails. If it doesn’t, here are some methods through which you can A/B test your email by segmenting your email lists:

Key Steps or Practices to A/B Testing Email Campaigns

To the uninitiated, setting up a brand new email campaign for A/B testing can be a daunting task. Take a note of the following best practices and you should be clear of any major mistakes:

1. Begin with a Hypothesis: You must always begin by formulating a hypothesis and then presenting it as a simple statement or question.

For instance, if you wanted to test the impact of an email’s content on its conversion rate, your hypothesis may sound something like this: “An email with actionable tips around our product will have higher conversion rates than emails without”.

Define the elements that would need to be improved or edited in order to test this hypothesis. Accordingly, you can determine if the changes are contributing positively or negatively to your desired outcome and then select the best-performing version of the email.

2. Select the variable that you want to test: Your first step should be to pick the variable that you want to test. The best way to do this is to pinpoint the variable that is most likely to affect your email engagement and conversion rates. 

This could be your subject line, email body, or even a particular call-to-action. Some examples of these can be:

3. Test one variable at a time and keep logs: As we mentioned before, A/B testing is about gathering statistics and then making inferences based on that data. To get accurate results, it’s important to test one variable at a time and keep logs of the variables you’re testing against each other. For instance, if you were comparing two different images used in an email for determining its conversion rate, note the number of clicks, forwards, unsubscribe, etc. on both the images separately.

The best way to keep track of this data is through spreadsheets. They allow you to create charts and graphs that will help you analyze your collected data better.

4. Don’t compare two different variables: You must not, under any circumstances, try and compare the results of two completely different variables. For instance, you must never try to compare data that has been obtained through personalizing an email with messages that are sent out without any personalization at all. 

This is because it isn’t just one variable that is responsible for your email engagement. There are several elements that go into making your email template, which you must take into account if you want to make accurate A/B testing marketing inferences.

5. Know what you’re looking for: With A/B testing, before running the test, it’s important to always know exactly what you are expecting. This is because it is sometimes difficult to determine the value of variables after you’ve completed your entire testing cycle. For example, just because an email with a particular subject line had more clicks than one with any other subject line doesn’t mean that it will work better for your business goals. It may have simply gone with the trend or received more attention than the other emails simply because it was the newest in the lot.

6. Use a single code for all your variations: While you’re testing, ensure that everything is using a single code base and that only one variable is being tested at a given time. 

Making changes to your email templates while you’re still running tests can actually skew your results and cause you to lose valuable information. If this has already happened, ensure that you recreate your test and start fresh.

7. Determine the sample size: In order to execute an effective A/B test for your email marketing campaign, it is important to determine the ideal sample size.

Marketers often create 2 groups with 50% of subscribers in each (when testing 2 email versions). But for better engagement results, it is advisable that you create two email groups each with 10-15% of your subscribers (in case you have 1000+ entries in your email list). You can then roll out the best-performing email to the remaining subscriber base for maximum bang for the buck (read ROI).

8. Determine the duration: What should be the adequate time window to conduct an A/B test? It is important to answer this since the accuracy of your test results often depends on the timeframe of the test. 

For instance, consider that you send 2 versions of an email (version X and Y) with varying headlines to 1000 subscribers each. After 6 hours, the open rate is 0.5% for X and 0.6% for Y. But you may find that the accuracy of the test would be more if you wait a bit longer, making version X the actual winner with an open rate of 0.8%. 24-48 hours seems to be a comfortable sweet spot for most email campaigns (provided that you have sent the emails at the most engaging date and time for your target audience).

9. Create different variations: The next step should include creating different variations of the same email design/layout where you can insert different versions of the variable that you are testing. 

Remember, your goal here is to find out which version works best for your audience. In case you are tweaking the email design, it is important that you keep the main layout or design of your email intact. Also, it is best not to make more than 2-3 significant changes to your original template.

10. Get Your Numbers Right: Just confirming that your email is opening or being clicked upon does not indicate a successful test. You must work out the numbers right, i.e., you will have to measure each variable against a singular metric which either goes up or down (depending on how it performs) and directly affects the ROI. 

For the example in point 8, the conversion rate would be the metric to watch for as your desired result.

11. Keep track of results: A good testing platform should provide a dashboard where marketers can monitor the daily progress of their email campaigns and see which version of the email is leading the way. Keep a close watch on these results since they can help you better plan your email campaign calendar in the future.

Note: It is always best to test your email campaigns at regular intervals so that you can ensure higher engagement and conversion rates. A/B testing your email campaigns against your original template will also help you know whether changes in the design and layout of your emails are required or not. Implement these tips and track the progress of your next A/B test for better conversion rates.

12. Get Feedback From Your Target Audience: Once you have your results, don’t be too quick to decide on the winner. Remember that you are testing two (or more) variables against each other and it is entirely possible that both emails performed equally well or poorly depending on what you were trying to test. To get an unbiased perspective, share your results with a few unbiased individuals and get their feedback on which email performed better.

The Bottomline: A/B testing is an integral part of email marketing campaigns because it’s based on the key principle of statistical inference. This tests one variable at a time and then makes inferences based on that data. However, before you can get started with A/B testing, you must first have a clear understanding of all the elements that go into your email template and why they must be tested together. By keeping track of data and identifying results accurately, you will be able to make more accurate inferences in the end.

A/B testing is a fantastic way to enhance your email marketing campaigns and ensure better audience engagement. While it may sound intimidating at first, if you follow the best practices mentioned above, you will be able to conduct a successful test in no time!

How to Determine the sample size for an A/B Test of Emails?

In this section, let’s understand how to actually calculate the sample size and timing of your email A/B tests. Some prerequisites or assumptions here include that the emails can only be sent to a finite set of audiences. And that you will be taking a small chunk of your entire email list to statistically deduce the best performing version of the email.

Here’s the best way to calculate your sample size:

How Many Days Should You Run Your A/B Tests?

The number of days required for an A/B test will depend on the number of contacts in your email list. For example, let’s assume that you have 2000 email contacts and want to run 40 tests that can be carried out over 20 days. By running 4 tests per day, you can get statistically significant results by the 10th day.

Had the number of contacts been 4000, you would have got statistically significant results by the 5th day.

What role does the testing platform play in A/B Testing Email Marketing?

A good email marketing platform can help you run an A/B test on variables that usually affect open rates, click-through rates, conversion rates, bounce rates, and many other metrics. It can also help you in tracking and analyzing your test results without any coding or design knowledge – all that you need is a few clicks to get the job done! This way, email marketers can run tests while improving their overall productivity and experience.

If you have not yet decided which email A/B testing platform to go for, here are some leading options that you can opt for today:

So, what’s next?

So, there you have it! Now you know the basics of A/B split testing for your email marketing campaigns. You can start getting a few results right away. Just remember that the more relevant variables you test, the easier it will be for your results to become statistically significant. This way, you can achieve the best results from your tests.

If you’re still struggling with getting started in A/B testing or if you’re an experienced marketer and simply want to accelerate your performance with A/B testing, we at Email Uplers can help you with all your email marketing endeavors.  We have a team of email marketing experts who can help you achieve your email goals. With us, you don’t have to worry about wasting resources on sending unsuccessful emails anymore.

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