A/B Testing

What is A/B testing?

A/B testing is a method of experimentation in which an audience is separated into two groups. Each is shown variations on a marketing campaign to determine which performs better.

Also known as split or bucket testing, A/B testing software gives group A one version of a segment of marketing content. This is typically the control group. Group B, the challenger group, is shown the same content with one aspect changed. Testers then analyze the results to see which has a higher conversion rate.

Marketers use A/B testing to more accurately understand the impact of small, measurable changes made to a website or app’s design and collect important data about those changes. This means that any updates will be based on real audience data, rather than best guesses about what might work well.

Types of A/B testing

Most A/B tests are conducted on audience-focused pieces of marketing content, like website pages, emails, or paid ads. These experiments typically fall under one of three types of test:

• Split testing is the most traditional type of A/B test, where an existing webpage is recreated with changes to one variable and then presented to two audiences. These are helpful ahead of a website redesign to see which one audiences respond to better.
• Multivariate testing changes variables on a page. However, these tests change several variables at once and create many more page versions than in a split test, sometimes as many as 8 to 12 variations. The goal with multivariate tests is to find the winning combination of variables for higher conversion.
• Multi-page testing changes or adds an element to several pages at one time. For example, a call to action button could be added to the homepage and several service pages during these experiments.

Most of the top A/B testing tools support all these types of tests. 

Basic elements to change in A/B tests

Marketers choose a variable based on what type of content they’re testing. Whether it’s a paid ad, an email, or a landing page, experimental variables can include:

• Headings and subheadings. Headlines grab attention. Testing variations of headings and subheadings can refine copy in advertising, which should then be replicated on landing pages for marketing message consistency. It’s not only the wording itself that can be tested. Placement, font type or size, and even heading alignment can all be altered during A/B testing.
• Images. Like headings, images drive traffic from an ad or fade into the ether of the internet. Running ad variations with different images or videos can generate interesting data for future marketing campaigns.
• Call to action buttons. Marketing materials are supposed to encourage users to convert and become paying customers. Call to action buttons and links are the best way to communicate a desired behavior to a site visitor; placement and styling of these on a landing page or within an email are crucial.
• Navigation menus and links. Any means of moving users around a site should be clear and easy to use. Testing navigation bars and fields is an important part of any website redesign process, ensuring that the user experience is the best it can possibly be.

Benefits of A/B testing

Any marketing experiment takes time to complete, but A/B testing brings several benefits that can ultimately save money and drive an increased conversion rate. 

• Insight into a target audience. Everyone interacts with websites differently, but A/B testing supports marketers in finding usage patterns that indicate issues with content or website design. This is especially true if audiences are segmented by a common theme rather than randomly. Testing can outline critical insights for changes that need to be made for a better UX.
• Lower website bounce rate. Keeping a website bounce rate as low as possible is best for both long-term conversions and search engine optimization (SEO). By continually testing and improving the design and usability of a website, the bounce rate naturally decreases as user pain points are addressed.
• More accurate budgeting. Advertising is expensive, so testing ad variations to find the one that has the highest conversion rate could save thousands on designs that aren’t right for a target audience. 
• Data-driven understanding. The best marketing campaigns are built around tangible data from real audiences. With A/B testing, changes can be made quickly and rolled out to audiences in real time.

Best practices in A/B testing

Just about any aspect of a landing page, email, or ad can use A/B testing. It can be easy to get carried away with making hundreds of changes at once. This not only skews the data, but it also makes it impossible to understand what has actually driven better results. 

Instead, by following the best practices for A/B testing, the impact of set variables is easier to identify.

• Understand current user issues. There’s no use in fixing something that isn’t broken. Start with reviewing customer support and survey information, along with on-site user behavior data, to determine what users are having problems with and how to address that.
• Change one variable at a time. Unless performing multivariate testing for a specific purpose, it’s best to start with traditional split testing with only one variable changed and a control group set up to counter the difference. Isolating this independent variable makes results much clearer once testing is complete.
• Decide on an appropriate sample size and test time. Like any form of experimentation, testing requires time to gather meaningful and usable data. To be sure that results are relevant to any future marketing campaigns, it’s also vital that the test population is a representative size and composition in relation to the usual target audience for the marketing materials being tested.

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