Intermediate Guide to A/B Testing in Digital Marketing

Capturing audience attention gets tougher every day for marketers. With consumers bouncing between devices, platforms, and countless digital touchpoints, optimizing messaging and experiences can often feel like a guessing game.

A/B testing, a data-driven way to experiment with different approaches and experiences, takes the guesswork out of digital marketing. By creating variations of marketing elements like ads, emails, web pages, and landing pages, A/B testing helps you identify what resonates best with your audience. 

The insights you gain are informed by data and made scalable with technologies that include AI and machine learning. These real-world tests inform the changes that help you maximize your digital marketing impact the most.

What is A/B Testing in Digital Marketing?

A/B testing, sometimes referred to as “split testing”, involves creating two different versions of a digital marketing asset – like an email, website page, or ad – to see which one performs better with your audience. 

You expose one group to Version A and another group to Version B, measuring how each variation impacts key metrics. Testing answers questions like:

• Does the new email subject line boost open rates? 
• Which call-to-action (CTA) button color drives more purchases? 
• Which landing page design leads to more form submissions or sign-ups?”
• Does offering a discount code decrease cart abandonment rates?

A/B testing provides the concrete data you need to optimize campaigns and make decisions versus relying on hunches.  It’s a scientific approach that pits variables against each other head-to-head. Running controlled experiments based on user behavior lets you lead with data, so you can continually refine your digital marketing presence.

What are the Different Types of A/B Tests?

Basic A/B tests compare two versions of an asset, but there are several test variations you can use to maximize insights. The main types include:

• A/B Tests: The classic split test analyzes two versions of a page/asset to see which performs better. Ensuring a large enough sample size is critical for reliable results.
• Multivariate Tests: Multivariate tests go beyond looking at two A/B versions of an asset. They test multiple combinations of elements of something in each test. For example, different headlines, images, and CTAs can be tested at the same time. This allows you to analyze how different variables interact with each other, which often produces deeper insights about how elements may or may not work together.
• Split URL Tests: This method tests completely different page designs and experiences at different URLs. 
• Multipage Tests: For multipage tests, an entire user flow like a checkout funnel is tested or the same element (security badge, testimonial etc.) is systematically changed across multiple pages to examine impact. 
• SEO A/B Split Testing: A specialized approach for testing on-page SEO elements like titles, meta descriptions etc. by splitting traffic between control and variant page groups.

The type of test used depends on your goals. A simple A/B test may reveal the better button color or header image. But to analyze and optimize more complex user experiences, multivariate and multipage tests provide richer insights about what may or may not work well for your audience.

What Should You Consider For A/B Testing on Your Site?

Just about every element of your digital presence is fair game for A/B testing. Here are some top areas to consider:

Copy Testing

Headlines, body copy, CTAs, microcopy like form field instructions, error messages, and tooltips – you name it, and it can be tested. The words you use can significantly impact performance, so test different tones, message framing, phrasing, and buzzwords. You should also text different language for different audiences and regions.  

Design and Layout

The digital user experience is shaped by elements like images, videos, color schemes, and white space. The way these elements are presented can make or break the experience. Experimenting with different design approaches can help you guide users on their digital journey – or confuse them – which is why testing design and layout components is so important.

Navigation

Is your primary menu and internal linking helping or hindering users? Use A/B testing to experiment with different navigation styles, information architecture, and accessibility – all with the goal of improving CX and flow.

Forms

Forms can be a roadblock to conversion, particularly if they’re overly complex or ask visitors for too much information. Use A/B testing to streamline forms with a focus on reducing friction. This involves testing different form fields, lengths, progress indicators, page positioning, and CTA buttons. 

CTAs

CTA buttons, popups, graphics, and text invite visitors to act. Optimizing your CTA elements is an effective way to improve KPIs like CTRs, sales, and signups. Test CTA placement, design, copy, font, and positioning to get the most from website traffic, particularly when running ad campaigns. 

Social Proof

Social proof includes things like product reviews, customer testimonials, star ratings, and media mentions. These things add credibility to your business and your website and can influence visitor trust. You can test if the presence of these elements helps improve your website performance and experiment with different types of social proof to see what resonates for your specific audience.

Content Depth

Content is king, as the saying goes, but too much – or too little – content can be a conversion killer. If only Goldilocks had an A/B testing tool! As with most things, finding the right balance of content requires testing and depends on your audience, your website, and your goals. 

How to Conduct an A/B Test

Creating an effective A/B test is a fairly straightforward process. It’s much easier to achieve this when using an AI-powered A/B testing and optimization platform like Monetate. Here’s a quick checklist to help you get started:

1. Identify your testing goal and prioritize what to test based on potential impact. 
2. Create a null hypothesis when you set up your experiment. The null hypothesis is a statement that assumes no difference between the variations you’re testing. It represents the baseline or status quo. For example, if you’re testing a new button color, the null hypothesis would be “The new button color will not have an effect on conversion rates.” The null hypothesis forces you to prove that the change you’re testing actually makes a statistically significant statistically significant difference.
3. Build the test variations which includes the original experience (control) and one or more experiences with the proposed change(s). 
4. Run the experiment, allowing each variation to be served to a portion of your traffic for a set time. 
5. Analyze performance data to see if the change(s) produced a statistically significant improvement over the control. This involves checking if the results are strong enough to reject the null hypothesis that there is no difference between the variations. If the data allows you to reject the null hypothesis with confidence, and you can implement the winning variation.

Tools with dynamic testing capabilities can automate routing traffic to the winning variation during the experiment. Leading personalization platforms integrate A/B testing into overarching CX optimization.

What is the Purpose of A/B Testing in Digital Marketing?

A/B testing helps digital marketers make informed, data-driven decisions about campaign planning, execution, and optimization. When you have tangible data to back up your decisions, you get more impact from your creative, media placement, targeting, landing pages, text – anything that your customers interact with along their buying journey.  Testing replaces hope with data.

Instead of hoping a new marketing asset like an ad or landing page will drive more sales, reduce shopping art abandonment, motivate shoppers to return (and on and on), you can use A/B testing unearth what truly works. Insights from testing are also incredibly helpful when you need to understand what motivated your audience to act (or not).

A/B testing gives you an empirical way to continually optimize and improve messaging, creative, UX, and targeting. It also lets you get more from your marketing spend by removing low-performing variables.