A/B Testing
By: Isa Vit
Tags: User Interface, Statistics, Product Management, Artificial Intelligence, User Experience

This article has been written by Fahad Dalwai, an ISA-VIT member.

Ever wondered how companies increase retention and improve the User Interface experience for their customers? Or how they choose which content or picture to show on their main page? Or even which colour to make the button in their apps?

Well, in the current ever-increasing sphere of the internet, the answer (most of the time) is A/B testing!

What is it?

By definition, A/B testing is a way to compare two iterations of something that works best. In a simpler and more comprehensible way, it is basically an experiment in which users are randomly shown two or more versions of something (most of the time this is a website or an application), and statistical analysis is used to identify which variation works best for a certain conversion target group. Test results from this allow for better optimization of the website and permit for relevant data decision-makers to move market conversations from “we think” to “we know.” A really basic example for A/B testing would be, for example, to submit two randomized email versions to your client lists and determine which ones produce additional sales. From the feedback received from there, then, the winning version can only be sent later since we know that this was more engaging.

Another example would be if you check two ad copy versions to see which one is efficient at converting guests. Using the results gathered from it, you can know how & where to focus more time on getting the best returns. As you can see, the possibilities and uses of it is almost limitless. As long as you have a controlled & truly random sample base and are not performing more than one test at a time, A/B Testing is definitely the way forward!

The conduction of a standard A/B test

History & origin

If we look back at its past, it seems apparent that A/B testing’s root can be traced back to James Lind’s ‘A Scurvy Treatise’ from 1753. It was found that in the 18th century, scurvy was the principal cause of maritime disease and death in the Royal Navy. In order to find the necessary antidote, Lind separately isolated six scurvied seamen and gave each a potential antidote remedy that various medical authorities had believed could possibly cure scurvy. Five pairs of seamen were prescribed vinegar, mustard and garlic purges, and other potential remedies. These seamen remained scurvied. However, for the remaining pair, Lind prescribed oranges and lemons & found that the pair quickly recovered, which proved that it was the best antidote. While the test seemed very rudimentary, it was in essence the first properly recorded example of A/B testing. This system is almost 100 years old and one of the simplest forms of randomized controlled testing, though these days it is most commonly associated with websites and application UI Development. By measuring the impact that changes in your UI have on your metrics, you can ensure that every change produces positive results which can enhance your business and growth.

Current scenario and uses of A/B testing

In the present generation, many say that the popularity of A/B testing has grown, as companies have started to realize that the online environment is well-suited to help them answer important questions like what are people most likely to click on, what are they interested in buying, or how do they like to register on the website? A/B testing is now being used to evaluate everything from website designing to headlines to even product descriptions. In fact, another surprising fact is that most of these testing and experiments run without the subjects (which is: you!) even knowing that they are a part of it.

Running an AB test that directly compares a variation against a current experience lets you ask focused questions about changes to your website or app, and then collect data about the impact of that change. (Image from: https://www.optimizely.com/optimization-glossary/ab-testing/)

If we look at some successful cases of A/B testing experiments which have been recognized as a large success, we see that one interesting & striking example is that of WorkZone, which managed to increase its leads through its testimonials page by about 34%. WorkZone is a US-based software company that was looking to revamp and increase its brand reputation. In order to increase sales, the company had put up a customer review section next to the demo request form on the lead generation page. Soon, they realized that the customer review section was hindering users from concentrating on the main aspect of the page, further distracting visitors from filling it. They decided to try changing logos to black and white and see whether the change would help increase the number of requests & surprisingly, it worked! They saw a massive spike in customer traffic, and it is safe to say that they adopted the updated lead generation page after this.

Now if we look at another example, Netflix comes to our mind, which went even further with their experimentation. The company makes different images for each and every video and tests these images themselves with a small proportion of their user base. The one which gets the most responses is called the winner and is then used as imagery for all of Netflix’s members. This is a very simple trick, but is one that has had a massive impact on the user experience. Video viewing for titles with images selected from such experiments increased by as much as 30%!

Looking beyond just the tests and their benefits, one important point to mention is that A/B testing is one of the most important components of the overarching process of Conversion Rate Optimization (CRO) — which is the practice to increase the percentage of website or device users who perform a desired action. Using this, we can gather both qualitative and quantitative user insights to enhance our online presence and business. You can further use this collected data to understand user behaviour, engagement rate, pain points, and even satisfaction with website features, including new features, revamped page sections, etc.

In conclusion, we can see that A/B testing is a very useful and important metric in order to achieve more efficient and better data understanding, especially for designing and implementing a better User Interface.