A/B testing is a powerful method to compare two versions of an email campaign and measure which one performs better on a specific goal. However, not all variables are equally important or relevant for your A/B test. In this article, you will learn what variables you should test in an A/B test for email campaigns, and how to choose them wisely.
Before you start testing any variable, you need to define your goal. What do you want to achieve with your email campaign? Is it to increase open rates, click-through rates, conversions, or something else? Your goal will determine what metric you will use to evaluate your A/B test results, and what variable you will optimize for. For example, if your goal is to increase open rates, you might want to test different subject lines or sender names.
Defining what you want to achieve through the campaign is a very important parameter to decide what to measure in a/b testing. For eg. If you want people to navigate your website, it's important to check in which version people are more likely to click the link in the email.
One of the key principles of A/B testing is to isolate one variable at a time and keep everything else constant. This way, you can attribute any difference in performance to the variable you are testing, and avoid confounding factors. For example, if you want to test the impact of personalization on click-through rates, you should only change the name or salutation of the recipients, and keep the subject line, content, layout, and timing of the email the same.
In our marketing class in LBS we did a/b testing in two ways. One where we changed only one parameter at a time and second where we changed many parameters at the same time. In the second scenario it became extremely difficult to gauge what was the real influencial parameter but in the first we could clearly see the minor differences in client interaction by a small change in font size therefore always change a parameter at a time in a/b testing It may seem like a cumbersome process but it will always be more helpful in the long run
Not all variables are worth testing in an A/B test for email campaigns. Some variables might have a negligible or unpredictable effect on your goal, or might be too costly or time-consuming to test. You should select variables that are relevant for your audience, your industry, your product, and your goal. For example, if you are selling a software product, you might want to test different features, benefits, or testimonials in your email content, rather than the color or font of your text.
Once you have a list of relevant variables, you should prioritize them based on their potential impact on your goal. You can use your intuition, experience, or data to estimate how much each variable can improve your performance. You should also consider the feasibility and scalability of each variable, and how easy or difficult it is to implement and test. For example, if you have a large and diverse email list, you might want to prioritize variables that can help you segment and target your audience more effectively, such as location, behavior, or preferences.
Sometimes, testing two versions of an email campaign is not enough to find the optimal solution. You might want to test multiple variations of the same variable, or combine different variables in a multivariate test, to explore more possibilities and find the best combination. However, you should be careful not to test too many variations at once, as this can reduce the statistical power and reliability of your A/B test. You should also make sure that you have enough sample size and time to run your test and reach a valid conclusion.
After you run your A/B test for email campaigns, you should analyze the results and see which variation performed better on your goal. You should use appropriate statistical methods and tools to compare the performance of the variations, and check if the difference is significant and meaningful. You should also look for any insights or patterns that can help you understand why one variation worked better than the other, and how you can improve your email campaign further. You should then optimize your email campaign based on the results of your A/B test, and repeat the process until you achieve your desired outcome.
In an A/B test for email campaigns, it's essential to test one variable at a time while keeping others constant to accurately measure its impact. For instance, variables like email subject lines, content, or call-to-action buttons can be tested individually. To make the test effective, define a specific goal, such as improving click-through rates or conversion rates. Avoid ambiguity between versions; for example, you shouldn't offer the same product with different prices in both versions. This systematic approach enhances your ability to assess the effectiveness of changes in your email campaigns.