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Glossary keyword - A/B testing

A/B testing

A/B testing is a way to compare two options of a webpage to detect which one works better. This testing simultaneously shows two options of the same page to separate groups of visitors. The purpose of A/B testing is to determine the best webpage this is driving higher conversions

Why A/B testing

To aggrandize the target audience by gaining new customers, the number of visitors on a website is a crucial factor. Therefore, the “conversion rate” is used for evaluating this factor, which is the result of A/B testing. E-commerce websites mainly apply this testing to increase the number of visitors to their websites. The purchase funnel and conversion funnel are good examples for A/B testing. 

Thanks to A/B testing, companies can make the right changes to user experiences by gathering data about customers’ preferences on results. As a result, a company is capable of learning how and which elements of user experiences affect user behavior. Not only e-commerce websites but also B2B marketing companies use A/B testing to expand their relationships with business stockholders. 

The testing creates a chance to know which changes had an influence on visitors’ behavior. Product designers can also use A/B testing to depict how new elements of a webpage affect user experience. This testing helps product developers optimize user engagement, product onboarding, and in-product experiences, as well. Generally, A/B testing provides some benefits: satisfying visitors, having a better Return on Investment from available traffic, preventing riskful modifications and fruitfully redesigning a website. 

How it works

While A/B testing almost always uses the same option with equal probability to users, sometimes reactions to variants may be disparate. Before starting A/B testing, collecting data is the first step in order to identify goals. As a result, it is possible to know how many people visit a webpage and what conversion purposes are. Next, creating hypothesis and variations give directions to create data about visitors’ behavior. Finally, it is necessary to run experiments and analyze results because they are the final steps to complete A/B testing. 

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