A/B tests

Why should you conduct A/B testing?

Conducting A/B testing allows UX researchers to test how particular changes affect the user experience and user behaviors on a website. Although A/B testing doesn't always involve studying websites, it's the most common use case for this type of research. Researchers can test how a single variable affects what users do on a site. For example, they can check whether a different call-to-action button will result in more newsletter signups or subscriptions for a digital service.

Thanks to A/B testing, UX teams can make data-driven decisions to improve the overall user experience, increase conversion rates, and even reduce bounce rates.

The most benefits of A/b testing can be seen in industries such as:

• E-commerce
• Email marketing
• Software as a service
• Social media
• Entertainment (e.g., streaming services)

A/B testing helps UX designers make informed decisions based on statistically significant results. If one web page variant outperforms the other, you should deploy it.

Types of A/B testing

UX researchers determine what improvements are beneficial to a site by testing elements such as:

• CTA buttons
• Checkout pages
• Headlines
• Page variants
• Forms

To check how the appearance of these elements influences user behavior, researchers use various types of A/B tests.

Split URL testing

Split URL testing is a bit different from standard A/B tests because it doesn't concern a single change. The goal of split URL testing is to test two variants of the same website.

Let's say you want to introduce more than a few changes to your web page but don't want to affect the existing URL, so you create a separate one. This results in two versions of the website—the control version and the variant. Then, your website traffic is split between these two versions, and your team calculates the conversion rate of both web pages. The test results will show you which one performs better.

This type of A/B testing can be helpful when you want to test layout design or workflow changes.

Multivariate testing

Multivariate tests are similar to the split URL testing method but don't involve creating a separate web page. Multivariate testing focuses on testing multiple elements of a site; for example, you may want to test a new design for the checkout button, hero image, and one of the headlines simultaneously.

Multivariate testing is a good solution if you want to test multiple variations of design elements without creating a separate website. It's also faster since you don't have to create sequential A/B tests.

Multipage testing

Multipage testing is commonly used to test the changes made in a sales funnel. It consists of making similar changes on multiple subsequent pages, aiming to show how particular changes influence a conversion or bounce rate.

However, it's important to note that although this method tests multiple changes, they should still be kept to a minimum because the test results might be inconclusive.

Conducting A/B testing: step-by-step

It's good to have a plan to organize your efforts before you start conducting A/B testing.

Research and find the hypothesis

Before you start conducting A/B testing, you need to determine how your website currently performs and identify potential areas for improvement. Tools like Google Analytics can help you with this task.

Let's say that during this research, you find that the website's contact form isn't converting as well as it should. You will hypothesize that changing its design should increase the conversion rate. Then, it's a matter of creating suitable high-quality variants to obtain reliable results.

Select changes to make

Once you've researched your site's performance and identified which elements underperform, you should decide how many of them to test. This will determine the number of A/B tests. Focus on the most crucial design elements if you want to keep the tests simple and not time-consuming.

Moreover, you should decide what changes you'll make to create a variation of the control design. For example, if you take the contact form as an example, you can change the number of text fields so users can fill it out quickly.

Perform A/B testing

After you defined your hypothesis and selected design elements to test, you should also decide which type of A/B testing will be best for your purposes.

Additionally, you should consider how long you will run the test. For example, you need to be able to determine after what time you will be able to achieve statistical significance and a noticeable effect. In the case of the contact form, a noticeable effect would be the negative or positive change in the conversion rate of the form.

Analyze the results and deploy changes

The last step is to analyze the results of the A/B testing and deploy changes. This step consists of analyzing how user behavior has changed and the results of adopted metrics.

In the case of our contact form example, this would be the conversion rate and its percentage. If the conversion is better for the variation design, you should deploy it. Otherwise, you don't need to make any changes, although you may want to come up with a new and better design and test how it will perform, which means conducting another round of A/B testing.

You should also remember that A/B testing is not just a one-off event; the results you obtain now will be helpful in future tests.

What tools should you choose for A/B testing?

There are all sorts of tools available for performing A/B testing. To gather quantitative data, you can use Google Analytics, Omniture, Optimizely, Kissmetrics, etc. Thanks to them, you can collect data on how much time users spend on your web page, how high the bounce rate is, and so on. Tools like heatmaps can help you determine where exactly users spend the most time on your website.

Surveys and feedback tools enable you to gather insight directly from users and customers. With them, you can determine elements for improvement that quantitative research methods might not discover.

You can also gather qualitative research data by using tools for session recording during user testing. During such tests, you can observe user behavior more closely and uncover issues along the user journey.

Limitations of A/B testing

A/B testing has many valuable advantages for an organization, but as with everything, it has limitations that you should remember.

Not enough traffic

Your website needs to draw sufficient traffic to conduct reliable A/B testing; otherwise, the results will be inconclusive. The number of website visitors is especially important for achieving statistical significance. In the case of small websites, the result of an A/B test might lead to an incorrect conclusion, and the changes may further hurt website performance.

Understanding changes in user behavior

A/B testing will tell you how users behave on a website and what actions they take; however, it won't provide the necessary context to explain why they act a certain way. That's why using qualitative research methods alongside A/B testing allows you to better understand user behavior.

Focus on short-term results

To perform successful A/B testing, you need to run it for a sufficient amount of time. Ending the test early because it shows either favorable or unfavorable results will lead to incorrect conclusions. Sometimes, users need time to adapt to a new design, and the initial results may not look optimistic. The situation may improve with time, so you must allocate sufficient time for A/B testing.

It's recommended that an A/B test should run for 1–2 weeks, during which user behavior will stabilize and yield more reliable results.

Summary

After reading this article, you should know what A/B testing involves and what kind of projects it's most suitable for. Testing digital products before they hit the market is crucial for fulfilling business goals and users' needs. You shouldn't try to guess what users' goals and desires are, and A/B testing eliminates this guesswork and allows you to create products that users will want to purchase and use.