A/B Testing: What Is It & A/B Testing Examples

A/B testing refers to the process of testing two versions of something to see which one performs the best.

In marketing, A/B testing can be used for many different types of campaign assets, including emails, SMS messages, in-app notifications, ads, and landing pages. As you find the variations that increase conversions, you can continue to optimize them to see if they perform better than the previous versions.

We’ll take a look at how to set up an A/B test and use the results to optimize your future marketing campaigns.

Designing an A/B Test

In A/B testing, you’ll begin with a “control”—your existing asset—and a “variant.” You can test multiple variants simultaneously, which is known as multivariate testing, or test out a single variation.

In your test, you’ll aim to gather an understanding of how well a certain element performs against your control. Several examples of elements you could test include:

• Email subject line
• Email body copy
• Design creative
• Sender name and address
• Message send time
• CTA
• Ad copy

Don’t try to change all of these elements in a single test, however. Otherwise, you won’t know which element is helping or hurting. You can develop multiple A/B tests simultaneously to see if, for instance, an email works better with one subject line than your control, and if it performs better with a new CTA (while using the control subject line). Once you’ve determined which element outperforms the other in your tests separately, you can combine the winners.

When conducting an A/B test, focus on a specific metric to measure your success. If you’re testing email subject lines, for example, the conversion might be opening the email. If you’re testing a CTA, the conversion should be clicking on the CTA. If you’re testing landing page copy, you might be measuring how long users spend on the page.

With each test, make sure that your control and test are being sent to the same audience segment, whether that’s a broad general audience or a more specific subset of your users. To test your variants, you’ll need to run the test for long enough to reach a statistically significant number of visitors. For some brands, this might be possible within a matter of hours, while others might take weeks to achieve the desired sample size.

When you analyze your results, determine whether there’s a clear difference in results between your control and your test. If there’s little difference between the results, the test may show that the audience had no real preference for one version over the other. However, if you see the test version consistently outperform the other, you can note the “lift” in conversion rate, whether that’s 3% or 30%. When conducting different forms of tests, Harvard Business Review suggests doing a cost-benefit analysis to determine whether the conversion lift is worth switching the rest of your campaign. If implementation costs and time requirements are low, there’s little reason not to switch to the more successful version, even for a moderate lift.

Why Use A/B Testing?

A/B testing is a simple and efficient way to gain better data insights to help you understand what your users respond to.

You can use this data to help you formulate customized marketing campaigns and approaches for each audience segment, engaging them with the content that’s been shown to perform best with users like them.

By continually testing different variations and optimizing based on your top performers, you’ll likely be able to achieve a significant lift in performance across conversions, engagement, open rates, and other metrics, driving up your revenue. And, because you’re optimizing based on what works for each type of customer, you’ll be able to give each segment a unique and best-in-class customer experience.

A/B Testing in Iterable

Designing and deploying A/B tests is a seamless experience when using a cross-channel marketing platform like Iterable.

Iterable uses a drag-and-drop experiment builder to enable marketers to easily change any element of their campaigns and select an audience segment and size to use in their experiment. You can select a winner based on opens, clicks, custom conversion events, or purchases.

Rather than needing to manually track your A/B tests, Iterable can automatically begin using the winning experiment variation for all remaining messages within a campaign once you’ve reached enough users to determine the top performing variant.

You can also experiment by running the same A/B test with different user segments, helping you identify the assets that are likely to perform best based on user criteria, such as demographics or purchase history.

Additionally, because Iterable provides an integrated approach to cross-channel marketing, you can set up experiments to determine which marketing channels are most successful for the same content (i.e., sending a promotional offer via email vs. SMS).

What to Do After an A/B Test

Once you’ve identified the winning elements that can increase performance by a statistically significant number, you can roll out the winner to your entire segment or user base.

Don’t stop there. Continue tweaking your results and optimizing accordingly. If you’ve identified a headline variation that achieves a 30% lift, you can adopt that headline as your new control, and begin testing out body copy.

Depending on your sample size, you can also run the same experiment on different user segments to understand whether they have the same results. For example, new customers may respond better to a straightforward headline, while your longtime customers may be more likely to engage with a tongue-in-cheek headline. You can use this segmented testing approach to develop optimized marketing campaigns for each audience segment you’re trying to reach and for each stage of your customer journey.

Iterable makes it simple to run countless experiments and automatically optimize your campaigns based on top performers, empowering your marketing team to continually iterate on their results to increase performance.

A/B testing represents a simple way to use data to increase performance, regardless of what your testing goals are. Start experimenting and you’ll see your results soar.