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What is A/B testing? How do you perform an A/ B test? What do I need A/B testing for?
A/B testing (often defined as split testing) is the process of analyzing two versions of a website page, text, or some other marketing element and carefully calculating the output difference.
In the online environment, the amount of visitors to your website is equal to the number of incentives you need in order to grow your market by attracting new customers and creating relationships by appealing to existing ones. And it’s your conversion funnel that decides if your website gets strong traffic and if there are more visitors who actually convert.
Since most marketers have come to realize, the cost of having traffic of any quality can be enormous. A/B testing allows you to make the most of your current traffic and helps maximize conversion without having to spend on new traffic acquisitions. A/B testing will give you a high ROI because even the slightest improvements will lead to a significant increase in conversions.
In other words, A/B testing helps people, organizations, and businesses to make thoughtful adjustments to their user interfaces. This also helps them to develop theories and understand better why some elements of their interfaces influence the actions of their users.
Users, opponents, and combinations are the facts or data you collect from A/B testing. Each version of a marketing asset gives you information about visitors to your website.
In this case, your specific user takes the shape of a marketing tool that you believe would perform well or that has performed well in the past, whether it’s a web page, newsletter, Facebook Ad, or anything else for that matter.
There is greater consistency and reliability with larger sample sizes, as the outcomes of A/B experiments are getting closer and closer to reflecting consumer tastes accurately. This ensures you can be confident of the most important improvements you make to your webpages.
A/B testing is essentially a method of research in which users are randomly shown two or more variations of a website, and statistical analysis is used to decide which variation performs better for a given conversion target.
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