Have you ever heard about having two plans: plan A and Plan B?
In case a plan doesn’t work out for someone, they keep a second plan (plan B) prepared to accomplish the task.
A/B testing works similarly. The “A” and “B” means that there are two versions of something, each version shown to a separate audience—and as the word “testing” suggests—the two versions are tested to see which one works better.
This is why it’s also called “split testing,” because you have to split your audience into two groups—one that’s shown version A, while the other that’s shown version B.
In case variant A brings less satisfactory results compared to variant B, you can use the latter one to achieve better results, and vice versa.
In website optimization, the A/B testing research method is used to check which version of a website element, (say, a landing page or a sign-up menu) works better for users and the business.
But how exactly is A/B testing done to optimize websites?
In this article, we will talk about how you can use A/B testing to optimize your website and achieve your desired goals: enhance user engagement or increase conversions.
How to Use A/B Testing to Optimize Your Website?
A/B testing is not limited to only testing two versions of a web element at a time. The method can also be used to evaluate more than two, (let’s say, four) versions of an element, in which case it is called A/B/n testing.
But if we’re strictly speaking of “A/B” testing, then it involves two versions: A and B.
Below are practical steps for implementing A/B testing to enhance your website and get better results:
Step 1: Define Your Goal: What Are You Trying to Achieve?
A/B testing comes with some risk, because you’re “testing” your website.
There’s a chance the new element you’re testing will work, and a chance it might not work. That’s why you should always define your goal first.
What are you trying to achieve with A/B testing? Do you want to increase user engagement so that they stay longer on a product page?
Do you want to increase the conversion rate of your product? Do you want more people to sign up to your newsletter? Or, are you trying to increase clicks on a call-to-action (CTA) button?
Step 2: Identify the Website Element
Once you know what you want to do, you need to identify which website element relates to your goal and can influence that outcome.
For example, if you’re thinking of improving the number of sign ups to your newsletter, you might need to optimize the sign-up menu’s:
headline,
CTA button,
visual element (it's usually an image),
input form (like email),
privacy assurance statement, or
color scheme.
These are the elements that you will have to test.
In A/B testing, though, you only test one element at a time. Testing multiple elements at a time has its own complications; the results become difficult to evaluate as to which exact element led to a positive outcome.
So, even if the sign up rate is influenced by a multitude of elements, you test one element at a time.
Step 3: Create Two Versions (A and B) of the Element
It’s A/B testing, so of course, you’ll be creating two versions of the element you want to test. For example, let’s say you want to change the sign up menu’s CTA button.
You will create a version B of the original version, which is version A:
Version A (the control): This is the original element. It’s the CTA button that reads “Sign Up”
Version B (the variant): This is the new, modified element. This new CTA button reads “Join Now,” which you think might perform better than the original one.
Likewise, for any element, the change should be clear and also thought-out, especially if your site is performing well.
You don’t want to try random changes to an element and try to muddle through it via just guesswork and end up wasting time and resources while you can think and plan things out first.
Also, don’t make overwhelming changes for specific goals or small outcomes.
For example, don’t redesign your entire page just to squeeze out some more engagement. Instead, test a single variable, like the font or the line spacing.
Step 4: Decide How Much Audience to Split
The modified version of the element is displayed on an identical yet separate page. In order to test the new element, you will have to split your audience between the two pages.
How much audience each page should get?
By default, A/B tests split your audience into 50/50 in which half of the audience sees version A, while the other half sees version B.
50/50 split is the most common approach to A/B testing because it gives both versions an equal chance and makes the comparison fair. The evaluation also becomes easier.
But in some cases, for well-performing websites, it’s better to send a smaller segment of the audience to the modified page, because of the risk factor: You don’t know if the changes will perform better (or even as successful as the original one) or not.
For example, if you’re making changes to a product page that’s getting good traffic and conversions, splitting the traffic into 50/50 risks reducing your conversions by a greater percentage compared to a 70/30 split, if the changes don’t perform well.
(Though, it’s mostly done when you know your existing page has good performance through performance data.)
That’s why, consider how much audience to split if your content is already performing very well and you know it. To minimize risk, some tools let you adjust the split (e.g., 70/30 or 90/10).
Risk minimizing is particularly important for well-working sites if the changes to be tested are substantial, because the impact could be significant.
For example, with a 90/10 split, only 10% of users see the new checkout flow at first. Then you can gradually roll out a new version while still testing.
Step 5: Measure Metrics and Analyze Results
The success of an A/B test depends on what you measure. This will take you back to your goal. The measurements you record, also called metrics, should match your goal. For example:
For email sign-ups, measure conversion rate (how many visitors signed up) and not just how many attempts each sign-up got.
We’re measuring end results here, not user activity. If one version shows better results, it means it is working better. Likewise:
For product pages, measure click-through rate or purchases.
For overall engagement, measure time spent on page or bounce rate.
Once the test has run long enough to gather significant data (usually at least a few hundred visits per version), you can reliably compare the results. The version with better results comes out as the champion.
Step 6: Apply What You Learned
A/B testing can take numerous experiments to produce significant results. Because there’s no definite “this will work” and “that won’t” answer to whether a change will be fruitful or not.
Instead, it’s about building knowledge over time. Once you find a champion (the winning element), implement it on your site, then move on to the next test.
Over time, these small improvements will add up to big gains in conversions and user satisfaction.
For example, an e-commerce store might first test button colors, then later test product image sizes, and finally test the checkout process.
Each small change will eventually work harmoniously and contribute to a smoother user experience and bring more sales.
Conclusion
A/B testing in web optimization means testing two elements of a website to check which one works better for the website and its users.
You can A/B-test your website to improve its performance by: defining your goal, identifying which element to improve, creating two versions of the element, deciding how much audience to split, measuring and analyzing the results, and implementing the learned changes across the website.
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