A/B Testing comprises of a set of cycles that one must follow sequentially in order to arrive at a realistic conclusion. In this section, we will talk in detail the steps of A/B Testing process that you can use to run tests on any website page −
Background research plays a critical role in A/B Testing. The initial step is to discover out the bounce rate of the website. This can be possible with the help of several widely available background research tools like Google Analytics and others.
Data from Google Analytics can assist you to discover visitor behaviors on the websites. It is always advisable to collect enough data from the site. Try to discover the pages with low conversion rates or high drop-off rates that can be further improved. Additionally calculate the number of visitors per day that are required to run this test on the website.
The next step is to set your business or conversion goals, which will help in understanding what the objective is. When that is done, then you can discover the metrics that determine whether or not a new version is more successful than its original version.
Once goal and metrics have been set for A/B Testing. The next step is to discover ideas on how to improve the original version and how to make it better than the current version. Once you have a list of thoughts, prioritize them in terms of expected impact and trouble of implementation.
For example, one of the most effective thing is to add pictures to a site, which will help in decreasing the bounce rate to some extent.
There are many A/B Testing tools in the market that has a visual editor to make these changes successfully. The key decision to perform A/B Testing successfully is by selecting the correct tool. Some of the most commonly available tools are −
There are various types of variations that can be applied to an object like using bullets, changing numbering of the key elements, changing the font and color, etc.
Present all the variations of your site or applications to the visitors. Their actions will be monitored for each and every variation. Besides, this visitor interaction for every variation is measured and compared to determine how a particular variation performs.
Once this experiment is finished, the next step is to analyze the results. A/B Testing tool will present the data from the experiment and will disclose you the difference between the performance and efficiency of various versions of a website page. It will also show if there is a important difference between variations with the help of mathematical methods and statistics.
For example, if the pictures on the website page have reduced the bounce rate, you can add in more pictures to increase the conversion. If you see no change in bounce rate because of this, return to the previous step to create a new hypothesis/variation to perform a new test.