A/B testing is a method of comparing two versions of a webpage, app, or email campaign to determine which one performs better. It is also known as split testing.

Examples of how A/B testing can be used:

• A company might want to test two different versions of a product landing page to see which one leads to more sales.
• An app developer might want to test two different onboarding flows to see which one results in more users completing the process and becoming active users.
• A marketing team might want to test two different subject lines for an email campaign to see which one leads to a higher open rate.

Here are the steps involved in an A/B testing:

• Define the goal of the test (e.g. increase sales, improve user retention, boost email open rates)
• Identify the element of the webpage, app, or email campaign that will be tested (e.g. the headline, the call-to-action button, the subject line)
• Create two or more versions of the element (e.g. version A has headline A, version B has headline B)
• Divide the test audience into groups randomly (e.g. 50% see version A, 50% see version B)
• Run the test for a sufficient amount of time (e.g. a week, a month)
• Compare the results (e.g. which version led to more sales, more users completing the onboarding flow, or a higher open rate)
• Make a decision and implement the winning version

Note that for the test to be accurate, it is important to have a large sample size, and to control for other variables that could affect the results.

A few more things to consider when conducting A/B testing:

• Make sure the test audience is representative of your overall user base. For example, if you’re testing a product landing page, it’s important to have a test audience that is similar to the people who typically visit that page.
• Use a statistical significance calculator to determine if the results of your test are statistically significant. This will help you determine if the difference in performance between the two versions of your webpage, app, or email campaign is due to chance or if it’s a real difference.
• Be mindful of the order of the test. If you test version A first and then version B, people might have a different reaction to version B because they’ve already seen version A. To avoid this, you can use a technique called “randomization” to randomly show the versions to the test audience.
• Be mindful of the duration of the test. A/B testing requires a sufficient amount of time to run, to ensure that the results are statistically significant. The duration of the test will depend on the size of your audience and the goal of your test, but it’s generally recommended to run the test for at least a week.
• Avoid testing multiple changes at once. A/B testing is most effective when you are testing one specific change at a time, so you can clearly see the impact that change has on your goal. If you test multiple changes at once, it will be difficult to determine which change is responsible for any improvements or declines in performance.
• Always be ready to act on the results. Once the test is complete, you should be prepared to act on the results, whether that means making changes to your website, app, or email campaign, or continuing with your current approach.