Email marketers have now moved from making gut-based to data-backed decisions. In this competitive environment, they need to find the best elements for their templates to stand out in their subscriber’s inboxes. And, when it comes to data analytics tools for finding comparable results, A/B testing reigns at the top. Here are the best practices to help you effectively conduct A/B tests to improve your email campaigns.
What is A/B testing?
A/B testing, also known as split testing, is the process of sending different variations of your email campaigns to different subsets of subscribers to find the winner variation. Usually, the variation that gets higher engagement rates gets chosen as the winner. But, you can also set different goals like higher open or click-through rates.
The complexity of the A/B tests varies with the requirements of the marketers. For example, in a simple A/B test, the marketers can test multiple subject lines for the same email template to find the one that garners higher open rates. More advanced A/B tests include putting completely different email templates against each other.
Most email marketing software, like Mailchimp, come with built-in A/B testing capabilities. You can use their email builders to create different templates and test every single element on it with ease. If you are struggling to find the right tools on the platform, you can always hire Mailchimp experts to help you arrange and conduct the test.
5 best practices to make the most of A/B testing campaigns
- Set your goals
Setting your goals for A/B tests will help you focus on a specific metric and manipulate the elements to improve it. It is recommended to do this before you commence your tests as it will guide you through the process. You can even create a simple hypothesis about your expectations from the test campaigns. Having a goal or hypothesis significantly increases your chances of getting a positive result. For example, a marketer may hypothesize that adding exclamation increases the open rates. Then, the A/B tests will help you determine if the hypothesis is true or not, and how many exclamation marks you can use until the open rate plateaus at the top. Create a control version of your template using the hypothesis and record its baseline readings.
- Independently test the variables
Independently testing the variables will yield much more desirable results compared to testing them together. Isolating these variables during the test helps pinpoint the results they are generating. Testing multiple variables at the same time makes it difficult to track the variable that positively or negatively affects the A/B tests. Therefore, it will hinder the development process of email templates. Say, a marketer wants to increase the engagement rate of their emails. So, during the A/B tests, they make changes to the image, fonts, and email copy at the same time. Even if the test successfully achieves its goal, the marketer will be unable to find the variable that caused the rate to spike. Hence, isolating the variable and independently testing them will make it easier to develop the email in the long run.
- Conduct multiple tests
In the world of eCommerce email marketing, time is of the essence. Your competitors won’t be sitting idly while you conduct A/B tests and perfect your email templates. A/B tests can be time-consuming which is why marketers should consider running multiple test campaigns simultaneously. Parallel test campaigns can produce insightful and comparable results faster than consecutive single tests. As a result, marketers can find their template’s strengths without falling behind their competitors. Even though some email marketing software offers these features, amateur email marketers may find it challenging to manage all the campaigns. You can hire Mailchimp expert to manage the campaigns effortlessly.
- Use A/B testing tools to determine the winner
You need to focus on your hypothesis throughout the test. Of course, you will be recording multiple metrics, but you need to make a decision that complements your primary goal. Use the data collected from the test and plug it into the A/B testing tool to determine which variant performed the best. Compare the results of your tests with those of your control version. To further reduce biases, you can use sophisticated algorithms to randomize your email demographics. Or, you can directly collect feedback from your subscribers through survey emails. Lastly, you need to give ample time to your A/B tests to generate useful data.
- Make changes and retest
To complete the A/B test, disable the losing variation and plug in the winner. However, there might be instances where both the variations run poorly. In such cases, the test is deemed inconclusive, and the marketers continue with the original version. Nevertheless, they can learn from the test and pour these insights to create a better version in the future. Remember, with A/B testing, there is always more room for optimization. Continue pushing and changing various elements in your template to perfect it.
Conclusion
Every element and aspect of your email template can be tested and optimized. Starting from the subject line to the timing of your emails, everything can be optimized. Regardless of the negative results, you can always learn from your mistakes and improve your email campaigns for subscribers.
Author: Kevin George is the head of marketing at Email Uplers, that specializes in crafting Professional Email Templates, PSD to Email conversion, and Mailchimp Templates. Kevin loves gadgets, bikes & jazz, and he breathes email marketing. He enjoys sharing his insights and thoughts on email marketing best practices on email marketing blog.
