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How To Experiments A / B in the Google Play Store listing

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How To Experiments A / B in the Google Play Store listing

What is the first thing the user sees when they browse the store? – The Google Play tab. In the file there are numerous elements that directly influence the download. Learn to optimize them!

Using the A / B experiments we can find the most optimal combination of resources (screenshots, short text, icon, description …) to get better conversions or downloads of the app.

As I commented in the ASO Manual, any improvement in the file has a direct chairman email list impact on the On-Metada factors, so we must take advantage of all the functionalities that are within our reach to scale positions and get more downloads.

A simple change of icon can suppose an improvement of the conversion of + 30% (I have read about a case recently).

At the moment the experiments are not available in iTunes Connect but we can replicate the insights and see how the Apple Store users behave.

Example of a creative tab from the visual point of view

Tips prior to the A / B experiment
The objective of the digital marketing professional is to interpret the results and decide which works best. To be able to make correct decisions it is necessary to be clear about some aspects:

Focus on a clear objective: It is important to be clear that we want to try a new concept, but without mixing different tests.

Test a single attribute per experiment: It is better to do simple tests to be able to attribute the variation to that change. If we make many changes we will not know which one is impacting or if one is negative but its effect is made up by the overall result.

Constant tracking: If the experiment is really working badly we can stop it before overtaxing the total conversion.

Duration of the test: The experiment should last between 7 and 10 days at least. When testing days during the week and on weekends we have more complete data.

Start with the icon: It is interesting to start testing with the icon of the app. For example, a game can have the image of a character or an object. The results can be very different.

Experiment habitually: Always have experiments in progress, although what we test seems not to make much sense. The famous trial-error in marketing is key.

Look at the competition: Can not you think of where to start? Looking at the competition is the first step to take ideas.

What is the room for improvement? Depends on the margin of improvement we must be cautious. If the percentage change is too small, we should not make big decisions.

Types of experiments
In Google Play we can find two types of experiments, depending on where they are applied and what elements are tested:

Global: Global experiments are applied to all places that have the default language. Only tests with graphic changes (not text) are allowed.

Localized *: The localized experiments only apply to the selected language and allow to do graphic tests and also of text resources.

  • In order to perform the localized experiments we must have our application in at least 2 languages.

Create an A / B test on the Google Play tab

Step 1: Access and select type
Access the Google Play console and select “Presence in Google Play Store” -> “Experiments in the Play Store tabs”.

Once inside you select the type of experiment you want to perform (global or localized).

Step 2: Configure the A / B test
Add a name
Select a segmentation * (maximum 50% of users). * If more variants are added to the experiment the public is divided among these.
Select the attribute to test.
Add variants (you can add up to three).

Step 3: Analyze and make decisions
Analyze the results and make decisions: From the panel of experiments allows us to “apply” the changes in the file if the test is satisfactory.

In this example we should still give the experiment more time, it does not have enough data to make a decision.

It is advisable, as long as the budget allows, to carry out acquisition campaigns to achieve a greater number of results. Sometimes organic traffic is not enough for these tests.