To do this, two segments of users are shown different variable versions to determine which one provides the best experience and has the largest positive impact on metrics. A/B testing is generally used to enhance website, software and product optimization processes, allowing developers to eliminate guesswork and focus on making the improvements backed by testing.
Why do we do A/B testing?
- A/B testing is used to test the performance of a variation of a feature against its control to determine which provides more value.
A/B testing is one of the most effective ways of gathering data to make informed decisions when performing optimization tasks on websites, software or products. Before performing an A/B test, specific features or functionality are identified as having the potential to perform better and an updated version is created by developers. This identification process requires pulling insights from data to determine where and how changes should be made. However, this data can only be used to form hypotheses about how an updated feature will perform. A/B testing allows product managers and experienced optimizers to put their hypotheses into action in order to prove or disprove their assumptions in a controlled environment.
How do you perform an A/B test?
- A/B testing provides one segment of users with a variation and another segment with a control version before measuring results.
After forming a data-driven hypothesis and creating a variation of a site feature, product or software, A/B testing can begin. Two equally numbered groups of users, or segments, must be formed, and one segment must be provided the control version. The other segment will be given the new version of the feature, known as the variable. The two groups will then be monitored for an equal amount of time and the data from the test will be recorded. The metrics used will vary based on the feature being tested and what the variable hopes to accomplish, as determined in the hypothesis. Some common examples of metrics tested when looking at variations of a webpage include time on page, interaction with certain elements, number of sessions, bounce rate and more.
Data analysts can take the results of an A/B test and scale them across a larger group of users to determine which is the more optimized of the control and the variable. The version that provides the most value to both the organization and the user should be determined the winner of the A/B test. The version will likely be rolled out on a permanent basis or may be tested against other versions if multiples have been formed. A/B testing can be used when optimizing or forecasting nearly any aspect of business and offers benefits that could continue paying off for many years.
What is beta testing?
- Beta testing is one of the final steps taken before a product or feature is released and is intended to catch bugs and gather user feedback.
Beta testing comes before A/B testing and is performed to check if any errors or software bugs are present in a product, site feature or software before it is made readily available to the public. This trial period helps provide a full view of the user’s experience on a greater scale and can help flesh out the intricacies of how certain products will function on different operating systems, devices and browsers.
During beta testing, development and product teams stay on alert to potential errors and bugs and work to fix them without impacting the functionality of the rest of the product. This often requires more time for each fix when compared to a live product. Once no additional bugs or errors are detected in the initial version of the product, beta testing can end and the product can be released in full.
