A/B testing, also known as split testing or bucket testing, is a method of comparing two versions of a webpage, email, or other marketing asset to determine which one performs better.
In an A/B test, you take your existing asset (Version A, or the "control") and modify one element to create a new version (Version B, or the "variant"). This element could be anything from a headline or image to a call-to-action button or a color scheme.
You then show these two versions to two similarly sized audiences selected at random, and measure their interaction with each version. The version that yields a better conversion rate (i.e., achieves the desired action at a higher rate) is considered the more effective version.
The key point is that you only change one element at a time. This way, you can be confident that any difference in performance is due to the one factor you altered, rather than some other variable.
A/B testing is a powerful tool that can help you optimize your website or other marketing materials for your specific audience, leading to higher engagement, more conversions, and ultimately, more revenue. It's part of a broader discipline known as conversion rate optimization (CRO).
A/B testing is important for several reasons:
A/B testing allows businesses to make careful changes to their user experiences while collecting data on the effects of these changes. By seeing the impact on metrics like conversion rates, time on page, and bounce rates, businesses can use A/B testing to confirm whether a new design or change improves the user experience on their website or app.
A/B testing is a way to mitigate the risk of making major changes, like a complete website redesign, because it allows you to test changes incrementally. You can test a new design or feature with a small portion of your audience before deciding whether to roll it out to everyone. This way, you can avoid potential issues that could result in loss of revenue or user dissatisfaction.
Decisions based on gut feelings or assumptions can lead to ineffective results. A/B testing provides a systematic framework for discovering what actually works best. The results are data-driven and can provide statistical confidence in your decisions, which can then be justified to stakeholders.
At its core, the main objective of A/B testing is to find the variant that maximizes an outcome of interest - this could be clicking a button, completing a form, or purchasing a product. A/B testing allows you to tweak elements of your website or app to increase conversions, leading to higher revenue.
A/B testing can lead to better return on investment (ROI) for various marketing activities. For example, by testing two versions of an email campaign, you can send the more effective version to the majority of your subscribers, thus getting more value (i.e., conversions) out of the same budget.
Finally, A/B testing facilitates a culture of continuous improvement. Instead of large, infrequent updates based on guesswork or trends, you can continuously make small, data-driven improvements. Over time, these can compound into a significantly better performance.
Conducting an A/B test involves a series of steps to ensure that you get valid, actionable results. Here's a broad overview of the process:
Your first step is to figure out what you want to improve. This could be anything from increasing the number of sign-ups, boosting engagement, reducing bounce rate, improving open rates on emails, etc. This goal will determine what element(s) you need to change for the test.
Decide what element on your webpage or in your marketing campaign you want to test. This could be a headline, call to action, form layout, email subject line, color scheme, etc. Remember that in a simple A/B test, you should only test one element at a time to ensure your results are valid.
Once you've identified the element to test, create a variant. This is the alternative to the current version (the "control"). Make sure the change is significant enough to potentially have a real impact on user behavior.
Divide your audience into two groups. One group will see the control, and the other will see the variant. It's important that the allocation of users to each group is random, to ensure the results aren't skewed.
Use A/B testing software to serve the control to one group and the variant to the other. The software will also track the results of the test.
After the test has run for a sufficient time, analyze the results. The A/B testing software will typically provide statistical analysis to show which version of the element performed better.
If the results are statistically significant, implement the winning variant. If there's no clear winner, you may need to redesign your test or choose a new element to test.
A/B testing is not a one-time process. It's a continuous cycle of testing, learning, and improving. Always look for new opportunities to optimize your user experience and meet your business goals.
Remember, for valid results, it's important to only run one test at a time on any given page, and to test long enough to gather sufficient data. It's also critical to ensure that your test is fair and that external factors are not skewing your results.
A/B testing can be applied in many different ways, depending on the specifics of what you're trying to optimize or learn. Here are some common types of A/B tests:
This is the most basic form of A/B testing where two versions, A and B, are compared, which are identical except for one variation that might affect a user's behavior. Version A might be the currently used version (control), while Version B is modified in some respect (treatment).
This is a technique for testing a hypothesis in which multiple variables are modified. The goal of multivariate testing is to determine which combination of variations performs the best out of all possible combinations.
In this type of A/B test, the versions of a webpage being tested are hosted on different URLs. This is useful when significant changes are being tested, like a complete redesign of a page.
This form of A/B testing involves testing variations of multiple pages that lead to a final conversion goal. The series of pages is also known as a 'funnel'. The purpose of this test is to see which series of page variations gives the best conversion rate.
This type of A/B test is commonly used in email marketing campaigns to identify which version of an email yields better results. Variables like subject line, email content, sender name, call to action, and send time can be tested.
In this testing, different versions of a mobile app are tested to see which one performs better. This can include testing different features, designs, or workflows within the app.
A/B testing seems simple, but there are a number of things that can go wrong during the test that make the results less accurate than they could be. This could result in making changes that don't actually improve things, or worse, have a negative impact on the tested metric. Some of the common problems with A/B testing are listed below:
Making changes to your website could also make changes to your search engine ranking. It would be undesirable for your A/B tests to cause you to lose ranking on major search engines. Search engine companies are aware of the need for testing, however. Google has put together a helpful list of things to keep in mind when testing to ensure your SEO isn't impacted:
Here are some examples of A/B tests:
In this A/B test, the variable is the main headline on a website.
Variant A might be the current headline, such as "Get Quality Products at Affordable Prices."
Variant B, the proposed alternative, could be something like "Discover Unbeatable Deals on Top-Tier Products."
The success metric could be click-through rate, time spent on the website, or conversion rate (purchase, sign-up, etc).
In this scenario, the subject line of an email campaign is tested.
Variant A might be a straightforward, informational subject like "New Spring Collection Now Available."
Variant B might try a more personal or urgent tone, such as "You're Invited: Be the First to Shop Our Spring Collection!"
The success metric could be the open rate or click-through rate of the email.
This A/B test involves the design and layout of a landing page.
Variant A uses the current design of the landing page, perhaps featuring a product image prominently with a short description and a "Buy Now" button.
Variant B might test a different design where a video replaces the product image, accompanied by more detailed product information and reviews, with the "Buy Now" button placed at the end of the page.
The success metric here could be conversion rate, bounce rate, or average time spent on the page.
This A/B test would look at different call-to-action (CTA) buttons on a webpage or app.
Variant A could be a simple, straightforward CTA like "Sign Up."
Variant B could test a more compelling or intriguing CTA, such as "Start Your Journey Today."
The success metric here would typically be the click-through rate or conversion rate for the CTA.
In this A/B test, the pricing structure for a product or service is tested.
Variant A might involve a one-time purchase price for a product or service.
Variant B could test a subscription model, where customers pay a smaller amount but on a recurring basis.
The success metric here would be overall revenue, the average purchase value, or conversion rate.
Remember, A/B testing is most effective when only one variable is tested at a time. This allows for clear, accurate results about what changes are driving different behaviors.
Group of experts from Storyly's team who writes about their proficiency.
