What is A/B testing?

A/B testing is a comparison method that is used to compare two versions of an app or webpage against each other to identify the version that performs best. The method is also known as "bucket testing" or "split testing". 

Digital marketing is one of the key elements for any company that aims to achieve its set goals or increase the sale of its products and services. Moreover, achieving such goals requires a series of strategies, one of which is A/B testing. The A/B testing involves developing and introduction and comparing two versions of same elements to determine the best element. The test helps us to appropriately use email marketing strategy, or increase the efficiency and effectiveness of a landing page. When an individual detects that a page is facing some challenges, it is required to take appropriate action to improve the click throughs and opening rates.

How to do A/B Testing

Technically, A/B testing can be termed as a technique used by the analysts to compare the effectiveness of different elements that serve the same purpose. The is based on using two groups of users where one element is tested in one group and the other element on another group. After that, the evaluation is carried out using the numerical and statistical methods to determine the most convenient element. 

Generally, the parameters to be evaluated are established before testing take place. This comprise of determining the methods of evaluation, the time required for the evaluation process and the objectives and goals of evaluation. In addition, it is important to recognize that this type of evaluation involve modifying only single element or one variable since such modifications does not influence other variables that may affect the result. 

Besides when the test comprises of several variables at a time, it gives rise to another type of test known as multivariate tests which is more complex that A/B test. The use of this of multivariate tests helps to detect various problems on the website and further explore the causes of the difficulties such as few users subscribing, high rebound rate, or few people conversing on the website, the act that is attributed problems of design, overload of the information and inappropriate font size among other issues. These problems have direct influence on the revenue of the company. Therefore, it is important for the company to carry out continuous testing using A/B testing to increase the bandwidth and allow higher percentage of communication.

What is the A/B testing method?

A/B testing methods is based creating and evaluating two versions of the same elements that the company is expected to introduce into the market. For example, the company may intend to introduce blue and yellow CTA buttons. The method involves the evaluation of each version to determine the effective version that work best. Contrarily, making many variations has considerable benefits since it keeps many users connected and it does not have any negative impacts on the users. many variations also increase the revenues to the company because it increases the number of subscribers to the link. The effective use of A/B model is based on focusing the attention on the elements that affect the rate of opening an email, and the clicks that made by the users on the landing page. Some of the elements tested in the A/B test include;

• Colors, sizes, words, and location of the CTAs
• The titles and the bodies of the product description
• The form extension and the type of field used
• The visual structure or the design of the webpage
• The ways of presenting the promotional offers and the prices of the products.
• The presentation of the elements of images such as content, purposes, quantity and location of the pages and landings of the products.
• The quantity of texts on the blog post or web page.

Any change that occurs to the variables under test are useful, and therefore, I recommend that at the start of the test, the analyst should try to differentiate variables as much as possible to arrive at conclusive decision on what the direction the test should take. For example, when the company has the website that has both yellow and green colors and intends to optimize the Ads, it should not start the process by carrying out A/B test through switching between the yellow tones. Rather the process should begin by testing the color that work best between yellow and green. 

Secondly, I would recommend that the analysist should not put a limit of the number of tests he performs. The analyst can improve the result by carrying out many tests to determine with high level of certainty the best element. The analysist should not base his conclusion on the first test. Rather it should be based on critical analysis of various successive tests. In additional before deciding on the element, the analysist should ask questions such as, is the test effectively done? Has the test managed to improve the website? After confirming these questions, the analyst can go on make conclusion. Besides, during the process, the analysts observe inquisitive behavior trends. 

Appropriate observation of A/B trend behavior helps the analyst to determine the improvements in the tests. The method is also a quantitative approach that measures and improve understanding the behaviors patterns and help in developing solutions to problems facing the effectiveness. Lastly, the analysts should not alter the data and the results since they are the key elements for improving the platform.