A/B Testing is one of the most used and frequent types of Conversion Rate Optimization (CRO) tests. Commonly known as "split testing," A/B testing is an analytical testing tool that produces data used to make informed marketing decisions for online business owners and vendors.
For E-Commerce websites, A/B testing can be an invaluable tool used to help to improve your bottom line. A/B testing works by using controlled tests on your audience to test variations to a single campaign variable. From these tests, you gather valuable empirical data that helps you to make decisions on what marketing strategies work best for your business' goals.
What A/B Testing Is
Saying simply, when you run an A/B test, you observe how one version of the content is performing with a change to a single variable. For example, you are a business owner who wants to see if changing the CTA (call-to-action) button fosters engagement and increases your CTR (click-through-rate).
To A/B test this changing, you are to create another web page that features the changes you want to test. This alternative web page would serve as a "challenger" to the current page. Following this testing period, you analyze and assess the statistical significance of the information collected through these tests. Based on this significance, data directly affects the call for a change, including the rate of conversion on each version of the web page.
Once you know what you will test on your website, make a list of the variables you will be testing. Do not run multiple tests at the same time, as this could affect your test results. Next, you are to show the change made to two equally sized audiences, and analyze which one performs better. You do this by taking 100% of your web traffic and splitting it evenly into different versions: one reflecting the challenger, and one staying the same.
Version A of your Control page will have 50% of your website's traffic, and Version B of your Challenger page will have the remaining 50% of your website's traffic. A/B tests need to run simultaneously to account for any variations in timing that could affect the results. The length of time you run your tests depends on the amount of traffic your website receives. The time aspect of A/B testing is intuitive and depends on a valuable sample size of your web audience. Tests can last from a few days to a couple of weeks, depending on company size and web traffic.
Finally, ensure that when you are A/B testing, you are not over-testing when you should be optimizing user experience. A great way to stay aligned with your goal is taking the time to ask yourself if these tests align with your goal metric.
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