A/B Testing (also known as split test or bucket test) is a method to compare two versions of a website or application, from which to find which version works well.
Testing is also known as Split Testing or Group Testing A/B testing is basically an experiment in which two or more variations of the page are shown to the user at random. And analytical statistics are used to determine which variation performs better for certain goal conversions.
Using A/B testing to compare a variation directly to an existing experience allows you to question changes to a website or app. And then you can collect decimal data about the effectiveness of those changes.
Testing will take the guesswork out of website optimization and allow decisions about informational data that will turn business conversations from "we think" to "we know". .
By measuring the variability in your metric, you can ensure that every change has positive results.
This allows them to build hypotheses and better understand why determinants of their experience influence user behavior.
In other words, they can be proven their opinion of the best experience for a given target - wrong through the A / B test.
Not only answering one-time questions or resolving disagreements, A / B Testing can be consistently used to continuously improve individual experiences and goals.
Checking for a change at a given time will help them determine whether the change will affect the user's access behavior or if there are other changes.
Gradually, based on that, they can incorporate the effects of many successful changes from previous experiments to demonstrate an improvement in the new experience over the old one.
There are different ways to implement a / b testing but what is the most effective way to implement A / B testing process? Here is a sample A / B Testing procedure you can use to start your test:
Gathering data: Your analytics will usually provide sharp, clear insight into where you can start optimizing. It gets you started with high-traffic areas of your website or app. As this will allow you to collect data more quickly. Finding pages with low conversion rates or high drop-off rates can improve.
Defining your goal: Your conversion goal is a metric you're using to determine if your variation is more successful than the original version. Goals can be anything from a click of a button or a link to a sales website.
Hypothesis: Once you've identified your goals, you can start generating AB Testing ideas and hypotheses about why you think they'll be better than the current version. Once you have a list of ideas, prioritize them according to the expected impact and difficulty of implementation.
Variations: Use your A / B Testing software (such as Optimizely). This makes it possible to make custom changes to an element of your website or mobile app experience. This can be as simple as:
Change the color of a CTA button
Swap the order of elements on the page
Hide navigation elements or something completely customizable.
Many of the top A / B Testing tools have visual editors that will make these changes easier.
Make sure your experiment can work as expected.
Test run: Start your experiment and wait for your users to access! At this step, visitors to your website or app will be randomly assigned to control or change your experience. Their interactions with each experience are measured, calculated, and compared to determine how each works.
Analyze the results: When your experiment is complete, it's time to analyze the results. Your A / B Testing software will output the data from the test and show you the difference between how the two web versions are performing. And is there a statistically significant difference?
You have to create at least 2 Layouts for compare and do A/B testing.
If you don't know "How to add layout", follow HERE
Main page, I set "Test 1" for it.
Comparing page, I set "Test 2" for it.
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