What is this important corporate tool, the AB Test? Learn from Cases of AB Tests
Modern companies continue to improve their services and products to capture consumers’ attention and maintain their competitiveness. In this process, companies will find they need to innovate and modify existing services before launching them to the market. The challenge for businesses is to predict what changes will be positively received by consumers. One of the most powerful tools in these situations is the AB test.
The AB test is an experiment that compares two or more versions of services offered to users to determine which will perform better. This technique allows companies to make optimal choices in a variety of areas, from product development to marketing strategies. In this module, we will explore the importance of AB test cases.
AB testing is a Gross Hacking methodology that conducts an experiment by comparing two or more versions (A/B). It is sometimes called a split test or bucket test because it compares two or more versions and then selects the option that gives better results. Usually, AB tests are conducted to make more effective data-driven results. Specifically, UX/UI design, marketing campaigns, content strategies, etc. set parameters they wish to improve, devise hypotheses, experiment, and then select versions that achieve higher results. Because specific parameters such as “improving the user experience,” “conversion rate, return rate, and sales improvement” are targeted, AB tests are usually repeated and do not end in a single comprehensive trial.
For example, if you add a Call to Action (CTA) button on a website, you will perform an AB test until you have a CTA design that produces the best results that the enterprise wants, such as improving their button click conversion rate. It is important to note that even if a particular version is concluded to be effective in one customer group, that may not be the case in another customer group. In other words, the results of the AB test cannot always conclude that one feature is superior to the other. The A/B test must be conducted with a clear goal in mind, as it is a way to find an optimized version for a particular customer, and test results may not be applicable to all situations.
The AB test methods are as follows: When conducting an AB test, select the elements of the page (ex: CTA, headline, image, etc.) to be tested and produce more than one version of a design. It is recommended that the parameters you want to improve are well-defined and that only one hypothesis be established per experiment. This is because the environment you wish to experiment in can only be controlled by setting a control variable and manipulated variable with one clear element in mind.
Afterward, similar amounts of users are grouped according to the number of elements, and data analysis tools are used to conduct the remainder of the test. A version that is aligned the closest with the predetermined goal will then be selected.
Designers, marketers, and developers are all susceptible to making the mistake of being unable to objectively judge their own products. AB testing is helpful when you wish to avoid these mistakes and improve your services in the right direction. The AB test illuminates the right direction service improvement should take as it shows the user’s attitude toward the service with outcome-oriented indicators referred to as event data. For example, when designing an application, suppose that when a button is placed at the top of the page, the click conversion rate is 1% higher than when it is at the bottom of the page. In that case, you can objectively conclude that it is better to place the button position at the top of the page.
The AB test must be backed by continuous operations to fully benefit from it. Based on the results of the AB test, we persistently improve the user experience and our understanding of our target customers so that we can continue to deliver optimized services to our potential customers. As AB test results accumulate, you can effectively see what your customers are responding to, paying attention to, what strategies are making a big difference, where they need to be improved, what factors are wasted, and so on. The more this repeats, the better your understanding of the user will be, as even the same AB test can help you establish a better hypothesis and get better results.
The AB Test is especially valid in the next two situations.
1) If performance is not reaching optimum levels
If the CTA buttons, marketing campaigns, etc. do not reach their target performance, the AB Test can be used to detect problems. It can be particularly effective if you need to induce subconscious user actions.
2) If you need to determine which approach is effective when you start something new
If you need to minimize the risk of making new attempts, the AB Test can help you make better decisions by contrasting the performance of the two different methods.
So what are some successful corporate AB test cases? Let’s take a look at examples of companies determined to have benefited from AB testing.
The first AB test case is Netflix. Netflix is a company that improves almost all of its services through AB tests. In addition to UI design, various functions such as encoding quality and page loading time are also tested. The landing page, which is uniquely Netflix, was also produced through AB Test. Netflix experimented with several copies with a CTA button located in the center of the landing page. CTA MVT (multivariable experiments) such as “JOIN NETFLIX,” “JOIN NOW,” and “TRY IT NOW” and free trial period (7 days, 14 days, 30 days) MVT are typical examples. In the case of the CTA MVT, the click rate was higher when the “get started” copy was applied to the CTA than when the “30-day free experience” was applied. That’s how the short, concise “TRY IT NOW.” CTA was all that remained on Netflix’s landing page.
*MVT (Minimum Viable Test): Developing and launching a test version with minimal functionality and monitoring user response
The second AB test case is Geppetto. Geppetto launched a new feature at the top of the community chat room but found that it was not utilized by users, and conducted an AB Test to solve it. A hypothesis was set to “increase the user’s use of notifications when the exposed to an example pop-up,” and the evaluation index was set to “notification functions usage number by users.” As a result, the group exposed to the example pop-up showed approximately a 38% increase in feature use compared to the group that was not. In addition, Geppetto’s team reviewed whether there were any other unexpected side effects of exposure to pop-ups. Because no other negative data was found, Gepetto decided to go ahead and expose all customers to the tested pop-up.
Several companies, including Toss TUBA and Bank Salad Data Foundation Experimental Platform Team, have separate operations for conducting AB tests. In the case of domestic companies, it is not common to disclose AB test cases, so we recommend you look to overseas examples instead.
So far, we’ve talked about what AB tests are, how AB tests are conducted, and their importance. The AB test is a verification tool optimized for deducing insights from data that helps you make the most appropriate decisions. By analyzing the AB test cases, companies can identify consumer responses and preferences, which can help them improve their products and tweak their marketing strategies. This enables companies to strengthen their competitiveness and significantly increase consumer satisfaction.
An understanding of data is essential as AB tests are based upon data. Elice provides essential coding and data training for this data-driven era, helping companies go through a successful digital transformation. It would be a good idea to conduct data training across the enterprise through Elice training to facilitate data-driven decision-making. Start the first step in data training with Enterprise DX Training Platform Elice!
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