How to Do A/B Testing to Increase Conversions [Step-by-Step]

A/B testing involves comparing two versions, A and B, of a website to see which works better. One will be a control version, and the other will be an ‘experiment’. You’ll show both to your audience, and they decide which is better. It’s a simple but powerful way to improve your marketing. 

Keep reading to learn what the A/B testing definition is and what is A/B testing in marketing. 

What is A/B Testing?

A/B testing is an experimental approach. It involves comparing a control and experimental version of an object. There is always a single difference between the two versions. You’ll compare their performance to determine which version is better.

For example, you could create two versions of an ad. The control version of the ad could have product-focused ad copy. The experimental version could have you-focused ad copy. You’ll show both versions to your audience to judge which performs better. 

You can judge performance using metrics like conversions. In this case, the you-focused ad copy would have higher conversions. You may get 50 conversions from the you-focused ad copy and only 10 from the product-focused copy. 

You’ve discovered that you-focused ad copy performs better. So, you’ll only use you-focused ad copy instead of product-focused copy. 

You can also do A/B testing SEO to improve your site’s search optimization. 

A/B testing is a continuous process. So, you’ll repeat the experiment to continually improve your content. Returning to our above example, you’ll now perform a new test. 

You’ll already use you-focused ad copy for both since the previous test proved it more effective. But you’ll now include a different ad graphic in each ad and repeat the experiment. 

A/B testing is crucial for conversion rate optimization because it systematically improves your content. Using A/B testing tools, like an A/B testing calculator, lets you maximize your conversion rate because you’ll optimize your content.

But you have to consistently use the right A/B testing marketing approach to succeed. 

What is Multivariate Testing? 

A/B testing and multivariate testing are similar but different. A/B testing is when you change one variable between two pages and compare them. Multivariate testing involves changing multiple variables and creating different combinations of variables to test. 

For example, an A/B test would involve changing your website’s CTA. 

A multivariate test would involve changing your CTA and page layout. You’d then test the 4 combinations of different CTAs and page layouts to find which combination is best. 

Whether you should do multivariate testing vs A/B testing depends on your goals. Multivariate testing requires more time and expense but can provide more in-depth results. A/B testing is faster but not as deep. 

A/B Testing Methodology How to Do It Step by Step 

1. Select Your Test Variable 

Your test variable could be your title, layout, or images. You’ll likely want to sequentially test multiple variables. Decide which one you want to do first. For instance, in our above example, we chose to first test ad copy and then a graphic. 

2. Choose Your Testing Variable 

Create your hypothesis. This is your theory of what characteristic works better. Our A/ example’s hypothesis was that you-focused ad copy would be more effective than product-focused copy. 

You could also do A/B testing social media, for example.

3. Create Control and Testing Variables 

Your testing variable will have the characteristic you’re testing, while your control variable won’t. The testing variable in our example was the you-focused ad copy. The control variable was the product-focused ad copy. 

4. Create Testing Groups 

Both your control and testing variables should be shown to the same size and type of audience. You’ll do this to minimize potential biases. Ideally, you’ll get an evenly sized audience that you’ll randomly divide into two groups. 

Your sample size should also be large enough to be representative. Too small of a sample won’t accurately bring you results. Also, test both groups simultaneously to minimize bias. 

5. Determine Your Test’s Statistical Significance 

Your A/B test’s statistical significance is how confident you are that the test results are accurate. For example, a 95% statistical significance means there’s a 95% chance your test results weren’t biased. 

The higher your statistical significance, the more confident you can be in your test’s conclusion. 

A/B Testing Examples 

Bannersnack

Bannersnack (now called Creatopy) provides online ad design tools. They wanted to improve sign-ups on their landing page. So, Bannersnack performed landing page A/B testing with different landing page designs. Ultimately, they concluded they needed a larger CTA button. 

They subsequently discovered a higher CTA increased their sign-ups by 25%. 

Re:member

Re:member is a credit card company. They discovered qualified people would leave their website before signing up. They performed A/B testing to create a new application form. The new application form prioritized their credit card’s benefits. 

Their new form increased overall conversions by 17%. 

Swissgear 

Swissgear provides travel luggage and bags. They performed A/B testing to increase conversions on their product page. They created a new product page as their testing variable. 

The new page has features like a ‘special price’ and ‘add to cart’ sections. The original page did not. The result was a 52% increase in conversions. 

VAIO 

VAIO experimented with banner ads to discover which were best for conversions. They A/B tested whether customers preferred laptop customization emphasized in banner ads, among other features. 

They discovered the new banner ads increased by up to 21%. 

Fab

Fab is an online marketplace for home accessories and goods. They hypothesized adding an ‘Add to Cart’ text in their CTA button would increase their click-through rate. So, they performed an A/B test to confirm. 

Their A/B test resulted in their click-through rate increasing by 49%. 

Fab’s example proves the importance of choosing the right content. Mandala AI helps you A/B test your content on each social media platform to identify the most engaging content. 

A/B Testing Best Practices 

Follow these 5 best practices for A/B testing to maximize your results. 

Do not A/B Test Everything 

A/B testing is effective, but you shouldn’t overdo it. You should only use A/B testing when the results are worth your time and effort. A/B testing is an expensive and time-consuming process. So, don’t use it for variables that don’t matter. 

Test One Element at a Time 

Test different qualities of your content sequentially instead of testing multiple things at the same time. Testing multiple qualities at the same time will confuse your results. 

Say you test your content’s copy, design, and layout at the same time, and conversions increase. You won’t know which quality increased your conversions. 

Define Your Metrics for Success

Ideally, choose a single metric, like conversions, to choose how to judge your A/B test. Having a single metric simplifies your A/B test and makes it easier. Using multiple metrics can complicate your results. 

Document Your Test Results 

Document your results so your business benefits long-term. Your results can educate your employees and provide additional information long term. You can also perform tests in the future on previous data. 

Use A/B Testing Tools 

Use A/B testing tools to simplify your A/B testing. These tools are designed to make A/B testing easier and faster for you. So, ensure you access good quality tools. 

You’ve now learned how to do A/B testing the best way. 

Conversion Rate Optimization Through A/B Testing 

Cro A/B testing will improve your conversion rate. How? You’ll systematically A/B test different elements of your site till you get the best combination. Every round of A/B testing gradually improves your site’s conversions.

Added together, you’ll experience a large long-term increase in conversions. 

For example, your site may receive 100 visitors monthly. Only 5% of them convert. You’ll now A/B test different elements of your site, including your CTA, copy, graphics, and website layout. 

After each A/B test, you discover how to optimize each element. Optimizing each element improves your conversion by a little. Added together, your conversion rate may increase from 5% to 8%. 

Cro A/B testing achieves this by enhancing user experience little by little. For example, when you A/B test your CTA, you discover what type of CTA users like the most. 

Similarly, when you A/B test your copy, you discover what type of copy users like the most. And when you A/B test your layout, you discover what layout users find best. 

So, every A/B test improves your site’s user experience little by little. 

In fact, A/B testing is the best way to enhance your site’s user experience. You get validated results from practical experimentation. 

A/B Testing Tools

These are the 5 best A/B testing tools. 

1. Google Optimize 

Google Optimize is part of the wider Google Analytics platform. So, it’s natively integrated with all other Google products, like Google Ads. It’s also a simple-to-use platform that comes with many features. 

You can perform website A/B testing, multivariate A/B testing, and even split A/B testing. That said, it’s not the most advanced platform. 

Google Optimize A/B testing may not be the best choice if you have a large enterprise. 

2. Optimizely 

Optimizely is an enterprise-level platform that lets you experiment and personalize your website. It’s an advanced platform with impressive features, like multi-page experiments. 

In fact, Optimizely’s biggest feature is that you can run multiple experiments simultaneously on the platform. So, it’s a great A/B testing platform for large businesses.

3. Omniconvert 

Omniconvert is an all-in-one CRO platform that provides excellent A/B testing features. It’s a relatively more affordable platform that’s marketed towards ecommerce businesses. 

They provide client-side testing, and you can integrate Omniconvert with other tools. Overall, it’s a good option for smaller businesses, especially ecommerce ones. 

4. A/B Tasty 

A/B Tasty is a dedicated A/B testing application. They provide A/B testing, A/B multivariate, and funnel testing. It’s a more advanced platform that provides advanced statistical features. 

These features include advanced targeting scenarios. For instance, you can trigger tests based on factors like URL, location, and demographics.

That makes A/B Tasty a great A/B testing website.

5. Convert 

Convert is a website personalization and testing platform. They mainly target medium-sized ecommerce companies. Convert’s features include a visual editor for simple tasks and an advanced style sheet for complicated ones. 

Convert seamlessly integrates with Google Analytics and 90 other platforms. It also comes with over 40 different elements for building customer profiles. 

Wrapping Up

You’ve now learned the A/B testing definition and read about A/B testing examples. A/B testing is the best way to improve your website’s conversions. That’s because it’s a scientific way to determine what works. 

A/B testing is a continuous process. It’s also time-consuming and costly, but when done right, it provides great results. So, learn more about A/B testing to determine how it can benefit your site.