The ABCs of A/B Testing

Karen Enriquez-Wagner – Account Supervisor

Email marketing platforms provide a lot of data. Information such as open rates, call-to-action (CTA) clicks and bounce rates is useful in measuring the success of individual campaigns. However, these insights can go even further to helping marketers understand consumer behaviors with a little experimentation.

A/B testing is a common strategy in email marketing that randomly splits the list of recipients into two or more groups that each receive a slightly different email. While the core messaging remains the same, small details are tweaked, and analyzing how the data differs in each of the groups can help improve results in future campaigns.

All About Variables

Just like a science experiment, A/B testing in marketing relies on testing hypotheses and measuring outcomes. The independent variables will depend on the client, audience and campaign, but common testable factors include:

• Subject line

• CTAs

• Time of day and week when the email is sent

• Imagery

• General content

Small tweaks are recommended for any independent variables rather than drastic changes, as marketers want to avoid comparing apples to oranges.

Best Practices

Timing is a key component that separates A/B testing from other kinds of research. While experiments to test fundamental differences in the messaging and presentation should be completed before the launch of a campaign, A/B testing is done throughout its duration. Initial testing will also use different methods, such as focus groups and surveys, and answer bigger questions about the overall strategy. A/B testing will fill in tactical details for successful execution. With this purpose in mind, marketers are encouraged to continuously utilize A/B testing. Even if they have been working with the same audience for years, new data could still show up and surprise them.

How the data is collected is just as important as when. As in science experiments, reliable output from A/B testing relies on changing one variable at a time. If a subject line and call to action are both changed between group A and group B, marketers will not know for sure which caused one send to outperform the other. Random split is also critical here to avoid introducing any new variables. Dividing the groups by location, gender, age or other demographics may cause the differences from group to group to impact the data rather than what the marketer is trying to test. Many email platforms allow marketers to split the list by every other person alphabetically or other more randomized options.

Cultivating Conversions

A/B testing, like any research in marketing, is done with the goal of optimizing each effort to increase conversions. Sending an email at 2 p.m. on a Monday versus 2 p.m. on a Tuesday may not seem like a drastic change, but small details make a big difference. Part of the appeal of email marketing is the wide reach it has, directly contacting hundreds of target audience members at once. Even a 1 percent increase in link clicks means dozens more people are interacting with the brand in a more meaningful way and one step closer to converting into sales. 

With the right approach, variables and technique, A/B testing is an invaluable tool to help marketers gain deeper understanding of audience preferences. Implementing it in email marketing allows for continuous refinement of strategy to maximize effectiveness and drive tangible results.