Why you should be A/B testing your content

Testing is an important phase in any design process. It reveals errors, provides important feedback, and gives you critical data that you can apply to a new iteration or version of your initial design. Testing most kinds of web content is usually an ongoing process that takes a couple steps and revisions, and there are a lot of methods that people prefer to use when testing their content. In this article, we’ll be talking about A/B testing, a method which is very common today. We’ll talk about what A/B testing is and the various merits of using A/B testing as opposed to other methods.

A/B Testing Overview

Also known as split testing and bucket testing, A/B testing is a testing method that involves testing one version of a web page or an app against another version of the same webpage or app. This is generally done to determine which one has better performance or which one users react better to in terms of design, responsiveness, and so on. After the test is complete, the two variations are weighed against each other for their data points. The basis of this test is pretty much the scientific method — you perform an experiment with a control group and then review your data once it is complete.

Improve Your Content

One of the main reasons to test two versions of something against each other is for the simple purpose of deciding which one is better. If you have two competing designs and you aren’t sure which one will appeal better to customers, A/B testing can help you make that choice. Testing two similar things against each other can also highlight what might be missing from each design, and the version you end up using can also involve the successful elements of the design you didn’t use.

Reduce Risks in Big Decisions

When launching an app or website, you want to make sure it is as effective as possible. Those who have run A/B tests know that one simple change, for example, the placement of a call to action, can mean the difference between full percentage points in conversion rates. Intensive testing allows you to target design aspects to achieve their maximum results.

Adapt to Your Customers

As we mentioned earlier, A/B testing can involve elements of focus testing or customer and user feedback. This means you can take a look at what versions of your content your intended audience reacts to in a more positive manner, and highlight the elements of that design. Without testing, you run the risk of releasing a version that is not optimized for your users.

It’s Cost Effective

You might think that it sounds silly to expect a test that requires two versions of something to save money, but it can. Not only does an advanced testing procedure result in a stronger final product that ultimately makes more money, testing also relieves the potential stress of budgetary failures that result from poorly conceived projects. Usually, programs that provide A/B testing services are affordable as well, providing a high ROI.

It Measures Actual Behavior

Before A/B testing, there were too many hours spent between individuals with differing ideas for designs that they think would be more effective. There is no reason to debate, as A/B testing speaks to what the customers prefer directly. Unlike some kinds of testing that rely on theoretical models and other things, A/B testing measures the very real components of two competing designs against each other. There’s no room in the data for anything that isn’t real.

Avoid the Correlation = Causation Pitfall

Sometimes, we can assume — or be led to think — that an outcome of some type is directly caused by a certain element of the design when this might not be the case. A/B testing can reveal this kind of discrepancy.

Improve your Bottom Line

Ultimately, A/B testing is about creating a stronger app or website. Obviously, that will help your business and thus, improve your bottom line.

A/B testing is really a tool that should be part of every web page/app design. It provides the ability to truly optimize a design for users and there is really no limit to how far the optimization can go.

This article was originally posted at danielboterhoven.tech.