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Simply put, A/B testing is a way to compare two versions of something to figure out which performs better and delivers better results. Although in today’s day it’s most commonly used by organizations to optimize their online platforms, such as their website or blog, A/B testing has been around for decades and has had many different uses.
The A/B testing experimental design takes the form of a controlled experiment in which two or more versions of a website (or other online platforms) are shown to customers to identify which version impacts your key metrics more positively, and therefore produces better results.
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To understand the concept better, let’s consider the following example of A/B testing:
Let’s assume that you’re a clothing retailer who sells their clothes through different channels, including through an online website. You want to test whether the size of the “add to cart” button influences buying decisions, and if so, then to what degree. To collect this data, you decide to use A/B testing.
To run the test, you will show two different versions of the “add to cart” button to different sets of users. These users will be assigned to groups randomly when they click on your site. It is important that the only difference in the websites shown to the groups is the size of your button so that any change in buying behaviour can only be the result of the change in the button. To measure the success of both versions, you can measure how many users clicked on the “add to cart” button. This information will allow you to determine if there was a difference in the number of clicks based on the size, and the size of the difference as well.
It is important to note that while conducting A/B testing, there may be other influences that are responsible for a difference in the performance of both versions. This reinforces the significance of randomization as it can minimize the influence of other factors on the results of the two versions being tested.
The following steps can be used to effectively create a framework for your A/B testing experiment:
The first step in any experimentation is to identify a problem. Before you can conduct A/B tests, you must try to identify whether your users are facing any problems and what these problems are. At this stage, you must evaluate the different problems faced by your business or by your users, and then try to find proof for these problems.
Once you’ve validated the problem, you can begin finding different solutions. In this stage, you must brainstorm multiple potential solutions and then select two or three of them that you believe are likely to yield the best results. Once you’ve selected the two or three best solutions, you must find up to four versions, or variants, of each.
Before you begin your A/B tests, you must first decide the metric using which you will define the success of your variants. It is a good idea to determine the following three metrics before starting tests:
Now that you know the solutions you want to test, as well as the metrics that will be used to measure their success, you can begin your A/B testing. Use the results of your testing to optimize decision-making or to improve your website effectively.
The following are a few advantages of employing the A/B testing experimental design:
A/B testing allows you to identify what users like most. This information helps you optimize your online platforms while keeping user preferences in mind, you improve user experience (UX), and potentially even user engagement.
In this context, conversion rate refers to the percentage of visitors on your site that makes purchases or subscribes/sign-up. A/B testing is a simple, yet effective way, for companies to boost their conversion rate.
When you use A/B testing to frequently enhance your website, you are able to produce better content that resonates with your users more.
Cart abandonment rate refers to the percentage of online shoppers who add items to their virtual shopping basket but then abandon it before the completion of the purchase. A/B testing can be used to determine the optimal amount of tweaks to the order pages that will influence users to complete their purchases.
Explore all the survey question types possible on Voxco
Explore all the survey question types possible on Voxco
An A/B testing experimental design is a research design used to test two versions of something in order to identify which delivers better output.
A few advantages of A/B testing are improved user experience, increased conversion rates, improved content, reduced risks, increased sales, and reduced cart abandonment.
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