With most of businesses being data-centric, A/B testing has gained greater traction as businesses are keen on ensuring that their applications meet their customer interests in their application usage. A/B testing isn’t as easy as it appears to be. There are many myths and wrong practices prevailing in the industry, which is why most of the testing processes are failing. To clear myths and help businesses to conduct A/B testing successfully, here we discuss A/B testing in detail and its methodology.
A/B testing is a statistical way of comparing two or more versions. It determines which version performs better but also to understand if the difference between the two versions is statistically significant.
Even Harvard Business Review on A/B states that "A/B testing, at its most basic, is a way to compare two versions of something to figure out which performs better. While it’s most often associated with websites and apps, Fung says the method is almost 100 years old."
A common dilemma that companies face is that most think they understand the customer well. But, in reality customers do behave much contrarily than companies predict either consciously or subconsciously. Most often, users either don't know or are unable to make out strong reasons why they make a decision and instead they just do it. When running an A/B test, you might find the results can often be very embarrassing, and customers can behave much inversely to what you would think. So, it's better to conduct tests rather than relying on intuition.
For example, in marketing or web design, one might be comparing two different landing pages with each other or two different newsletters.
Let's say you take the layout of the page and move the content body to the right versus the left. Maybe you change the call-to-action from black to blue, or your newsletter subject line has the word "offer" in Version A and the word "free" in Version B.
For A/B testing to work, stating out your criteria is key for the success prior to the beginning test. What is your hypothesis? What do you think will happen by changing to Version B?
Maybe you're hoping to increase the conversion rate, newsletter signups, or increase your open call criteria for success ahead of time. Also, you may want to make sure that you split your traffic into two (it doesn't have to be 50-50) and you want to figure out what is the minimum number of people needed to run your A/B test to achieve statistically significant results. One can do this with multiple versions.
A powerful website relies on different aspects and interface designs are one of the important ones. If a website has a professional and pleasant design, then it is likely that more people will spend time on website.
Some developers like utilizing A/B testing by comparing two different versions of a website with regards to design. This means that some elements of the website are different between the two. Typically, a web developer makes both designs available to the users and then compare which of the two will be preferred by visitors.
Basically, send your users the two web sites through a random system and choose parameters to measure which of the versions works better. Some people may count success based on how many people choose to sign up or the number of sales that a certain version receives.
There are certain things to remember with A/B testing. For example, you should be able to collect enough data before you conclude, and follow the guidelines for statistical confidence to make sure that your results are enough to create a conclusion, and always have both versions of your design tested at the same time.
Since the results may be different from one day to the next, don't assume that your instincts are correct. Always wait for the results before you forming the conclusion that one version is better than the next. Many times you will notice that users will respond better to the one that you like. Conduct as many A/B tests as you would like to. This way one can have accurate results and get the preferable design for your site.
The following procedure is critical to get accurate results:
A/B testing is a way to collect information about design changes of a website. Analyzing the results with the A/B test statistics is the key to a successful user experience.
Raj Kumar works as a Sr. Test Engineer with V-Soft Consulting India Pvt and has 8+ Years of Software Testing experience. He has good knowledge in Agile process methodology, ServiceNow, and AI domains. He has gained experience in various testing areas that include, Web services, Mobile Testing, GUI, Functional, Integration, System, Ad-hoc, Usability, Data Base, Smoke, Regression and Retesting.