Every marketing team should look to A/B testing for emails to continually evolve and improve their email campaign strategy. Keep reading this article for a detailed guide on the subject, including:
Let’s get started and jump right into it.
A/B testing, or split testing, allows marketers to really understand the relationship between how recipients respond and specific elements of an email. It provides a framework to continually improve email performance rather than aimlessly sending out marketing material with the hope of getting some conversions.
To perform an A/B test you have to randomly split the test audience, or email database, into two two test groups. Each receives one of two variations on a marketing email with only a single element changed. By measuring which version performs better, you can learn what appeals to your audience. This process does involve having the tools and infrastructure to track recipient responses, such as open percentages, click-through rates, and conversion.
Marketers use A/B testing to optimize many variables related to email campaigns. This includes:
The concept behind A/B testing is finding out whether email A or email B performs better with your audience. To make this possible, you need the audience that receives each version to be virtually identical. That means you segregate the test audience to create test groups that would theoretically provide the same response if they received the same marketing email. While this is impossible, you want to get as close as possible to this idealized scenario.
If you vary only one aspect of an email and send it out to equivalent groups, the only reason one email would perform better than another is the variable you are testing for. It provides measurable behavior statistics on your target audience for feedback and research to improve email campaigns moving forward.
A/B testing is a broad technique that can be applied to many different marketing tasks. For email marketers they can use A/B testing when:
If you’re sending marketing emails, A/B testing can be used to help refine messages based on real feedback and get better results.
Every business wants to maximize engagement rates and drive more purchases and revenue when designing its marketing approach. To achieve this, you need to experiment, gather data, and test different hypotheses on what works for your audience.
Simplifying your test to just one variable and two variants of an email is the easiest way to learn about your audience. You can even perform A/B tests directly from email clients using CRM platforms like Hubspot. Suppose your business selects a tool with A/B testing functionality. In that case, you will likely get help managing your email leads, automating A/B testing processes, and directly changing the elements or objects in your emails for optimization.
Want to see a visual example of what A/B testing for emails can look like?
Check out our Google Docs online spreadsheet to start testing your own email marketing ideas and gain insights on what gets the best results.
Click here to access a sample A/B testing for emails.
Instructions, best practices, and details are included at the top of the page so you can input the numbers and start tracking the results of your own A/B email testing.
Below are steps and tips for executing effective email A/B testing.
Without goals and targets, your efforts will be aimless, and you won’t have a clue what you are trying to achieve when testing for different variables.
So what are you aiming to improve:
The right amount of variables to test is one. If you think it's smart to speed up the process by testing multiple variables in a single test, you are misunderstanding the concept of A/B testing. With only one change you can trace performance changes back to a single factor.
For example, if you change several variables between emails with no controls and notice one email group performing much better than the other, how will you manage and determine the reasons behind an uptick or downturn in performance?
You need to check one theory or idea at a time. So whether that is subject line length, CTA wording, the duration of the copy, or something completely different, keep your testing confined to one variable at a time.
The next step in A/B testing emails is segmenting your email list. Traditionally, marketers are very deliberate when they separate email lists. They use specific criteria to help tailor content. Examples could be age, job, demographic, or locations and places where prospects live.
In A/B testing, this idea is thrown out the window.
As we mentioned in the previous step, you want to test for a single variable at a time. However, we can’t send the same email, with one change, to the same person twice and expect to perform a meaningful test. Therefore, we have to divide email lists randomly or use advanced methods to ensure each test group is as similar as possible.
What exactly will you change in your two emails and why? Will you alter the subject line or change the font size for better readability?
The variants you choose to alter can be anything. Just remember there are only so many hours in the day for testing different variants or finding new leads to email. Therefore you should use your expertise to choose variables with a high possibility, probability, or opportunity to impact the success of your email campaign.
Ultimately, A/B testing for email marketing is all about the enhancement of revenue and the advancement of conversions. Therefore, don’t waste your valuable time and resources on factors that will make virtually no difference. Look for key transformation factors such as subject lines and CTAs.
How many recipients will there be in each testing group? Having only a few collectives in each group is unlikely to yield valuable results. But while statistically speaking, the bigger the sample size, the better. Having too large a test group can give you far too much data to sort through.
Plus, your email database is a finite resource and you can’t bombard your audience to test various theories. Finding more leads can be difficult and expensive, especially in B2B markets where you are targeting businesses and not an abundance of consumers as in D2C. However, there are plenty of tools out there to help you find company email addresses and develop your list of leads.
With over 500 million contacts, BookYourData is an industry leading Pay-As-You-Go Prospecting Platform. With a range of search filters and email verification, BookYourData helps you quickly identify new leads at a fraction of the cost with 10 free leads upon signing up.
Generally speaking, you should aim for 1000+ in each test group to get statistically meaningful results. However, if your email list is much smaller, you will have to do with a lot less.
Ensure you have accurately applied the relevant changes to test your chosen variable and send out your test emails to the two groups. Remember, the timing and date of the email are also variables. So, unless you are testing for this, it is important to ensure you are reaching both test groups simultaneously.
Post-sending, you need to pay close attention to consumer response. This is what will aid your marketing teams in developing future approaches. Monitor everything, nothing is too small to be neglected when trying to capture all of the engagement produced. Any assistance or support will likely be appreciated, whether it is something simple like screenshots of how emails appear in recipients' inboxes or something more in-depth like an extensive database of recipient responses broken down by time and date.
Consistently documenting and tracking the outcomes will ensure that you have all the necessary information when it comes time for data usage and examination.
Once the testing results have rolled in, it is time to analyze and validate the data you have collected. Take a close look at the performance of your emails and which one performs better.
In application:
You’ll likely have a vast amount of data to consume and analyze. Therefore, strong analytics is critical to discard irrelevant or incomplete data. Only work with the leftover valid numbers. Utilization of the remaining meaningful data allows you to identify the actual results of the test.
If one email shows a statistically significant improvement, it’s a strong indication that you should base the rest of your campaign’s emails on the higher-performing version.
If you notice significant differences in how recipients respond to your emails, you can tweak and apply these changes to future campaigns. For instance, if version B of your email includes a clickable button that leads to your website and results in higher traffic, you should consider incorporating this element into future emails and digital communications.
A/B testing is over after you find a single positive result. It is an extensive process of a long period of time, iterating and optimizing emails to get closer to peak performance. Even if you have a successful email campaign, you should regularly introduce relevant changes to collect fresh data and insights on click-through ratios and other key performance indicators.
Once you have finished your A/B testing, ensure that you are tracking and storing the data somewhere secure. Provide access to relevant staff at your business so that they can implement your findings when developing new email content.
Unsure which variables have the highest impact on email performance and where to start testing? Below are five good items to consider for A/B testing.
The moment your email arrives in the consumer’s inbox, the first thing they will see is the subject line. It is the primary factor in whether your email manages to get opened or not, generating attraction or repulsion in readers.
Therefore, the first place to focus your A/B testing attention is finding effective subject lines. Measuring the difference in open rates between two emails with vastly different subject lines will point you in the right direction and help you determine what appeals most to your target audience.
Another aspect of your emails to consider during A/B testing is personalizing your content. There are lots of different ways to personalize your emails, use A/B testing to try changing the length, tone, and content variations within personalized elements.
How your emails look, including images, layout, and design, are critical aspects to engaging customers. Often visuals generate more of a response than plain text. Using A/B testing to try out new layouts and email structures and embedding images are excellent ways to gauge what works and doesn’t work in your email marketing campaign.
If you are deciding what the single most important element of a marketing email is, it is likely a toss-up between the subject line and the call to action. The subject line opens the door, getting a reader to click on your email and read more. The CTA gets a reader to close the door behind them, wanting to continue hearing from your business or even make a purchase there and then.
You can research when the best time to send marketing emails is. But the answer you’ll get will be a generalized response. A/B testing the timing of your emails for your audience will let you know what works great for your specific campaigns. Plus, this might change depending on geographic regions.
Now you know what to do regarding A/B testing for emails, but what potential pitfalls and mistakes should you work hard to avoid?
A common mistake that comes up in A/B testing is changing too many variables simultaneously and being unable to directly connect user clicks to a specific factor. For example, group A receives one form of email, and the others receive entirely different content from email subject lines and CTAs to timing and date. In this instance, the data returned cannot be connected to a specific variable, and it is difficult to use it to enhance future emails.
It is impossible to create two identical test groups made up of the same characters and customer personas. There will always be some variation. This creates noise in the dataset with one test group possibly engaging more with your emails just because the people were more susceptible to begin with.
Therefore, you have to analyze your data to ensure that differences in A/B testing results are statistically significant to state that one email outperformed the other.
Similarly to be statically meaningful a result has to be demonstrated using a decent sample size. This ensures any results are accurate to what your audience finds moore appealing. That means, A/B testing requires a large a number leads in your email database.
Take into account any external factors that may also influence the results of your A/B tests. This could be factors that dampen overall engagement with consumers less likely to be checking their inboxes or willing to spend money. For example:
Remember timing is a variable. So unless it is the variable you are testing for you need to send out both sets of emails simultaneously. This ensure that the time you send your emails does not influence the final results.
Stopping your A/B test too early means you may miss out on significant data and your results not reflecting your audience accurately. You need to give recipients enough time to respond to ensure you track all of the engagement generated by both test emails.
If an initial A/B test points towards a certain variable needing to be changed, try re-testing to make sure your result is accurate. This will confirm whether or not this is a beneficial change before you move on to testing a new variable.
Overgenerlizing results refers to assuming something that works for a specific email campaign will work across other areas of your marketing.
For example, you may discover that a large call-to-action button in a specific button color embedded in your email was the most effective way to increase website traffic and sales. Do not use this result to assume you should replace all other links with something similar or redesign your website menu to match the same aesthetic.
A/B testing results are specific to the testing scenario and you shouldn’t assume this applies to all other aspects of your business.
You notice three more sales per week coming in via using an embedded call-to-action button. That’s not a lot, so should you go through the trouble of adding this button to other emails?
The truth is, A/B testing for emails is all about marginal gains that build up leading to further success. 3 extra sales can add up. Combine this with small improvements from testing for other variables and the likelihood of reaching your goals starts gain pace.
If you have decided on the parameters of your A/B test (eg, personalization level or image placement), stick with them throughout the entirety of the process. Changing parameters midway means your results lose their purpose.
More and more people only interact with their inboxes on their smartphones. Don’t forget to consider mobile users when it comes to your A/B testing. Ensure that:
A/B testing for email is not something you do at the start and forget about for the remainder of the campaign. It is a continual process that strives to keep improving results the more variables, and versions of each variable, you test for.
It is vital that you document your results when A/B testing. The data you gather from these tests is incredibly useful, so you want to ensure that you are keeping track of it and storing it safely.
A/B testing can be a time-consuming process and shouldn’t be used for every little thing. Trust your instincts and judgment, and reserve A/B testing for significant changes, those that will have the biggest impact on conversions.
While you don’t want to overly rely on an A/B testing approach, it can be equally harmful to not prioritize it. A/B testing is critical to building a successful email marketing campaign and should be conducted regularly.
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The frequency of A/B testing for emails largely depends on your objectives and timeline. Aim to conduct A/B tests at least once a month, and consider testing more frequently when launching a new campaign.
When it comes to A/B testing, the larger the sample, the better. While it is possible to see performance changes in 100 people, if you can get your sample size into the thousands, you will receive much more accurate results. To do this you need access to extensive and high quality email lists.
You can tell that your A/B testing results are reliable when they are statistically significant. This means there is a low chance of the difference in performance being caused by noise.
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