How to do the A / B test to double the conversion rate of your application

On this basis, developers are recommended to perform micro tests to improve the conversion rate of the application. A common test used to evaluate the conversion rate is the A / B experiment.

When verifying the conversion rate, a tool known as CRO (Conversion Rate Optimization) is used to monitor and improve a mobile application in terms of performance and functionality.

Interestingly, A / B tests incorporate logic to determine user interest based on how the developer frames their hypothesis to convey a positive result. It also recognizes the opinion given by the users and represents the results based on the elements.

Are you familiar with the A / B experiment?

Well, this technique is mainly used to identify the best elements that will attract more users. To start the test, developers are expected to formulate a mobile landing page that offers a replica of what users feel when they interact with the application.

In addition, an A / B experiment requires that application developers subdivide their audience equally and then place them in different variants to represent an element. Once you have done this, the test delivers the results of what item was most preferred by the users.

Note: Application developers should never forget that A / B testing is a cyclical process that requires timely optimization.

Steps to follow in an A / B experiment

As mentioned, the testing process is a continuous activity that is carried out in stages. Below is a step-by-step procedure for A / B tests:

Research and critical thinking: this is to get a clear picture of what developers want to test based on the identified objectives. At this stage, application developers must also draw a hypothesis, preferably the "If_, then_ due to_" model, to synchronize with the application's algorithm.

Choose variants: just from a good hypothesis, developers can find their variants. In addition, application developers must ensure that the different variations are represented by separate icons without changing the content.

Take the test: in this step, the developers can go ahead and start the test.

Data analysis: involves the interpretation of the data to determine the most preferred variant.

Adoption of an adequate alternative: implementation of the winning variant to achieve optimal results.

Conduct review tests: it is recommended to design tests that run for 7 days. Developers must also ensure that new updates are configured with the identified hypothesis.

In addition to being a continuous process, there are situations that may require an A / B experiment. Circumstances include; Search results, comparison with competitors and during the installation process.

Parameters used in the tests of the A / B application

The app store has simplified the work for application developers since it only uses eight elements to increase conversion rates. These are:

Screenshot: Application developers must package their information creatively since screenshots say a lot about a product. Explanatory screenshots can improve app discovery in the app store and require a regular update to keep up with competitors. This tool also serves as a platform to reach undecided potential users.

Name: the use of keywords is essential for an application to obtain better rankings in the search box. A clear and attractive title gives a vivid impression of what the product is. By doing so, developers can direct more organic traffic to download the application.

Icon: the criteria used is similar to the title, since both sell the image of the application, so a single icon will attract more audience and a better conversion rate. Developers should keep in mind that the application store's algorithm analyzes colors, graphics, characters, logo, and simplicity.

Video: visual images can be used to sell an application in the Play Store and even increase search rankings without the use of words. Currently, the iOS allows you to play short videos shortly after the icon appears. According to experts, the video can have a positive or negative influence on the number of installations. This acts as an indicator of the vitality of showing the best output in the app store.

Price of the application: to obtain an appropriate application cost, developers must establish the elasticity of the mobile application. This will help determine when and at what speed to increase or decrease prices.

Introduction and description: the introduction of an application can determine if the conversion rate will increase or decrease. It goes without saying that developers must give a brilliant first impression to users and an accurate description of the product.

Reasons for the failures experienced in the A / B tests

It is not guaranteed that all A / B tests will be successful. With this in mind, it is important to mention the causes of the failed tests. Are:

Use of small sample size: this results in programmers having partial information about the anticipated result.

Incorrect method of application testing: this may be due to poor research and bias in the users questioned.

Misinterpretation of A / B test results

"Single action" mentality: a common mistake among developers who neglect changing trends in the market and, therefore, obtain results that are not reflected in current market positions.

Conducting erroneous experiments: by virtue of this, application developers can rush to get positive results from A / B tests, so they end up omitting vital stages that produce false information.

Other simple ways to improve the conversion rate of applications include:

Adding a call-to-action box to retrieve information like "I like", "dislikes" and comments through social networks.

The use of interactive ads that encourage users to click and must meet certain objectives before moving on to the next phase.

In conclusion, the results obtained from the micro tests can be evaluated to improve the performance of an application in the application store. The tests of the App A / B also have resources in the reduction of the data required for testing, risk management, improvement of sales, minimization of bounce rates, monitoring cart abandonment and analysis of the application.

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