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A/B Test

What is an A/B test?

A/B Test Definition — An A/B test (sometimes referred to as split testing) is a method for producing, launching, and comparing two versions of one thing. Businesses may use A/B testing to compare the performance of a new banner ad, a homepage, a headline, an infographic, or almost anything else. 

Running an A/B test is a common strategy for marketers targeting a variety of audiences, and is fairly straightforward once you familiarize yourself with the process.

In essence, two versions of either a message or design are presented to an equal number of users. The differences between the two may be significant or subtle: images, fonts, layouts, audio, video, or tone can all be changed. 

The two items you test should be designed to test certain hypotheses you have about what will perform best with your audience(s). 

It’s crucial to understand what types of data you intend to focus on before launching an A/B test, though. This prevents you from wasting time on experiments that fail to present the information you’re looking to explore. All results must be measurable and applicable to your ongoing operations. 

How long should an effective A/B test run? That’s up to you. But allow enough time for a strong user-base to try the ad, webpage, etc. Otherwise, you may struggle to gain an accurate conclusion, or worse, draw conclusions that aren’t necessarily there based on misrepresentative data.

The benefits of an A/B test

A comprehensive A/B test offers a number of benefits. Let’s take a closer look at what they are. 

Improve conversions.

Running an A/B test on a webpage, landing page, sales video, or almost any type of content will help you to optimize performance.

How? 

By changing subtle parts of your messaging or design, you’ll discover what resonates best with your audience. 

You may be surprised to find that small changes, such as changing the size of a button by a few pixels, makes a difference on sign up rates. Or that changing the color of your core CTA button impacts conversion. But these are precisely the kinds of things that A/B testing will uncover.

And if you run several A/B tests on different key elements across your website or marketing campaign, you can gradually deliver the most engaging customer experience. 

Remember though, it’s really important that you only ever change one thing at a time so that you know which change made a difference.

Base big decisions on stable foundations

Looking to refresh your website?

Want to roll out a new PPC campaign?

Well, both involve risk. Your current website or PPC campaign might struggle to achieve the results you’re looking for, but switching things up could make it all worse. 

No business can afford to rush into a decision. That’s where an A/B test comes in: you can include the current version of a web page or app alongside the new one to see if changing it really is going to have the effect you’re hoping so. 

Once the test is over, you’ll have the data upon which to build your new site or campaign — reducing the amount of potential risk.

Examples of an A/B test

Businesses and marketers enjoy a generous range of freedom when running A/B tests. Here are two simple examples of how A/B testing can work for you:

Experimenting with your landing pages

A landing page plays a huge part in how successfully you convert top of funnel leads.

But, to be successful, the landing page has to grab visitors’ interest and hold it. You can run two versions of a specific landing page at the same time to see which drives the most conversions, or which has the highest bounce rate.

Once it’s clear which landing page performs best, you can analyze it to determine why it came out on top. 

How is it different to the other version and how is it better? With these insights, you can work to improve performance more widely by optimizing your landing page in a data-driven way.

Identifying the best messaging for your ads

Another application for A/B tests is determining the most effective ad copy for your business. 

In the case of PPC ads, you might want to specially test which keywords and headlines perform better. Both elements play a crucial role in the success of your PPC marketing, and running an A/B test reveals the right option. 

Or, in the case of Facebook ads, you might decide the same messaging but with a different image on two ads, to see which performs best.

You can keep refining ads over time, too, building on the ‘winner’ of one test by changing it slightly for the next.

When do you need an A/B test?

An A/B test is worthwhile when trying to improve anything your audience interacts with. This could be when you plan to make a big change to your homepage, shopping cart, testimonials section, or marketing campaigns. 

For example, an A/B test works brilliantly when overhauling your website. You can experiment with different ideas which appeal to your audience, and find out which style, color scheme, layout, or tone have the biggest impact on behavior. 

Your users will show you the way. Just take their lead and run with it.

Q&A

Question: How to calculate sample size for an a/b test?
Answer: There are multiple formulas you can use in order to calculate the most efficient sample size. The sample size should be determined by the confidence you have in the results of a particular sampe size after an A/B test is performed.

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