Working with Graphic Metadata: how to boost the app’s conversion with the help of A/B testing?

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Yaroslav Rudnitskiy
Working with Graphic Metadata: how to boost the app’s conversion with the help of A/B testing?

The main two tasks of ASO are to boost the app’s visibility and improve the conversion rate. In our previous articles, we’ve talked about how to boost the app’s visibility to get more impressions and page visits. In this article, you will learn how to boost the conversion to install rate.

Page elements affecting the conversion rate

The App Store - in the case of this store, we’re looking at the impression to install right out of the search traffic. The users are much more likely to install the app right out of the search results.

The conversion from app page views to installs is analyzed when receiving paid traffic from advertising channels. The average conversion from page views to installs for search traffic will be just over 100%. There may be fewer page views compared to app installs because it’s possible to download an app from search results, and thus, there is no point in exploring this funnel for organic traffic.


There is a number of important app page elements, but more often than not, the only information that truly matters when it comes to making an install decision is what the user sees in the search results:

  1. App icon
  2. Screenshots
  3. Video
  4. Headline
  5. Subtitle
  6. Average app rating

If your app is tightly integrated into the user’s life (bank apps, for instance), it’s crucial to take extra attention to each one of these points:

  1. The number of stars in the rating
  2. Featured reviews
  3. Other parts of the landing page (app info, description, what’s new, app confidentiality agreement)

Google Play. The conversion funnel for all traffic to this app store consists of app page views and installs, as Google Play's own analytics don't provide app impression data.

The first three aforementioned conversion elements should take priority because they are visible in the search results, and are the first step for the user to make a decision to install.

The first step in the user’s decision-making process to install from search are the elements, visible in the search results:

  1. App icon
  2. App name
  3. Average app rating
  4. App size
  5. Number of installs

The users can see the following when searching by brand: Screenshots, Short description, Video (if auto-play is enabled), and Description image (as a video cover if auto-play is disabled).

It’s important to monitor other parameters that might influence conversion as well:

  1. Short description
  2. Featured reviews
  3. Other (description, app info, developer info)

The most viewed screenshots on Google Play are the first two, and the first three on the App Store. If you want to make changes to screenshots, start by hypothesizing the screenshots visible at first glance at the app page.

The app description in Google Play is almost never read by users visiting the app page. Don’t be afraid to enrich the text with keywords and don’t rush to judge the conversion rate fluctuations because of it.

At the moment, the Featured Graphics element in Google Play is visible as a video cover - when the app is featured, and for all users that have auto-play disabled. If the app has no video and it's not featured - this element will not affect conversion in any way, simply because this banner will not be shown in the store at all.

Based on the insights provided above, when it comes to the App Store, you should put an accent on the Headline, Subtitle, App icon, Screenshots, Video, and Average rating. When it comes to Google Play, the App Icon, App Name, and Average rating should get more attention.

Coming up with a hypothesis

To begin changing graphic elements, you must first formulate hypotheses for testing. But where can you get ideas to formulate one?

Your competitors. Your competitor’s apps and their graphic assets will help you find elements that will increase your conversion rate. Track your competitors' graphical metadata updates via the App Update Timeline in AppFollow and you will know which tactics and hypotheses of your competitors worked best. As we know, there is no silver bullet in ASO that will solve all your problems, and thus:

  • Avoid copy-pasting entire elements of other apps
  • Monitor trending and niche apps (the largest publishers are sometimes too big to fail and don't care about their app pages at all).
The puzzle element is popular in the match-3 category - the competitors of Playrix began to copycat it too.

Ideas on the internet. Much like any marketing work, ASO utilizes all the same techniques of reference search in order to formulate hypotheses. Pinterest or Behance might be a good place to start. On these websites, by knowing your target audience and category, you can choose a graphic style and discover more unique ideas to design screenshots.

There are websites on the Internet (scrnshts.club) that collect app graphic elements and categorize them. Not all of them keep up with app frequent updates, however, so expect to find plenty of outdated information.

For a better understanding of the visual culture in another country, we recommend paying attention to the resources the users from a given country use on a daily basis. With this method, you can localize graphics for any market in the world successfully.

In-app marketing experience. After a UA campaign, each app is likely to have gigabytes of leftover creatives. You should always take into account past experience by revisiting the creatives that were successful in the past and consider your current ad campaigns (if any are available).

Never forget that your app landing page is part of your overall conversion funnel. If creatives in ad networks and visual parts of the app page overlap, the conversion can be negatively affected, resulting in fluctuations of traffic and sales volume. This business is not without pitfalls - the new graphics may have a positive effect on the organic traffic conversion, but will negatively affect the performance of advertising campaigns. Thus, before you make any changes, be sure to look at the results of the entire funnel first.

With the combination of the sources provided above, you can hypothesize and produce creatives for icons and screenshots without any restriction. Once the creatives are in your hands, you can update the graphics there and then and come what may begin testing.

A/B Testing process

The testing of app page elements is done in order to reduce the risks of a sharp change in conversion rate after an app update. The principles of testing are in the distribution of tested elements (2 or more) to the target audience of the application. With the help of A/B testing, it's possible to predict the most likely result of metadata updates of the app page.

App testing tools

Google A/B Testing tool is the most common tool for A/B testing of metadata.

Pros: it’s completely free and you can find it in the “Store listings experiments” in Google Play Console. With its help, you can test up to 3 variations of metadata for a localized app version, globally or at the special app page. Below are the elements available for testing:

  • App icon
  • Screenshots
  • Video
  • Description picture
  • Short description
  • Full description

Cons: Performing testing on all traffic channels (both organic and advertising campaigns) and the confidence interval of the test result both have a certain margin of error. After the applied result, you have to monitor the real changes in conversion rate. Another disadvantage is the inability to test graphics other than those meant for Google Play, but it’s not Google’s fault :).

Apple Search Ads. Testing graphics in the App Store is done by using Apple Search Ads. With its Creative Sets, you can configure up to 10 sets of app screenshots and videos from those uploaded to the current app version, and see with which sets of graphical assets convert better.

There are a few notable differences:

  1. You can only test graphical assets - screenshots and videos
  2. The testing is done only within the ad itself. The app metadata will remain the same.
  3. All metadata for testing must be present on the app page already, so if you have a complex app page design, there will be only so much you can truly test.

Third-party testing solutions. These will help you build a separate app landing page that emulates a given app store page that will receive traffic in order to conduct testing.

Some of these solutions will present raw data (the number of page views and the number of conversions per install), and leave you to interpret the result yourself (ASO Giraffe). Some have an analytical platform (Splitmetrics) that is able to conduct testing, present raw data, and calculate the expected result based on input data.

Among the advantages is the testing of any part of the App Store or Google Play app page on any type of user (which is especially convenient if you already know your target audience).

A disadvantage is in the high cost for working with platforms and a separate fee for attracting traffic to the landing page, which sometimes is far from cheap.


Testing changes on the “live” app page. If no other method is available, it's possible to conduct tests not only in the format of simultaneous testing but also by gradually changing graphic elements at equal time intervals. This method is the riskiest because you force your users to accept the current graphic assets without any alternative. Therefore, this method can be tried when you have test results on Google Play and you don't have the resources to test graphics for the App Store in some other way.

Rules to conduction of testing

Almost all of the rules below will apply to the Google A/B testing tool as it's the most common metadata testing tool.

1. Each experiment should be conducted over 7 days. Some users behave differently in the store depending on the day of the week - thus giving root to the definition of Weekly fluctuations. For instance, on Monday, the popularity of sports apps grows, and on Friday, the popularity of taxis does the same thing. To account for these fluctuations, conduct tests for at least 7 days - despite your app category and other factors.

2. Plan hypotheses from general to specific. To avoid repetition, it’s necessary to distribute hypotheses from large-scale (background, location of mockups, colors) to small (slogans, highlighting elements, etc.) during planning.

3. Test one element in one testing scenario. In order to interpret the results more easily, it’s necessary to test one element in a single test scenario. Testing two or more elements (icon + screenshots) can lead to a situation where the icon increases conversion and the screenshots decrease it. You will not get hard data this way.

4. Conduct testing with a similar number of users. It’s done with the same goal - to reduce the margin of error during testing.

5. Keep paid traffic flow at the same level. When testing with the built-in Google Play tool, the presented options are split equally across traffic from all traffic sources. It’s not worthwhile to increase or decrease the number of purchased installations for a more accurate result.

6. Validate your tests. To validate your tests, you can run reverse tests (B/A), or tests with a large number of items (A/B/B, A/A/B). If the result is not false positive, it will be confirmed during testing.

Google A/B testing tool lets you test up to 3 test sets of elements + the current one simultaneously. When setting up a hypothesis, make sure to have more than one option. In a single test case, it’s necessary to run tests in A/B /B format. If you are unsure of the test result, run the validation test in the same format.

This approach can help you to avoid a false-positive test result since the efficiency is calculated with a confidence interval of 90% (for conventional instruments, a confidence interval of 95%).

Interpreting the results

Depending on the testing tool used, you will have various options to interpret the results. Some tools provide you with raw data that you need to interpret yourself using an Excel spreadsheet. Some of them have functionality that will present you with the result of conversion growth.

Test interpretation in Google Play leaves a lot of questions unanswered.

Once each tested variant has the necessary number of installs to interpret the results, you can see an indicator of effectiveness.

An indicator of effectiveness is the percentage of a possible change in the number of installs when applying the current option with a 90% probability.

The data you get from a given tool is an average indicator and it must be compared with the current position of traffic across all channels. If you are actively running advertising campaigns, you need to pay attention to the entirety of the product funnel - from the conversion of ad creatives to retention indicators.

Conclusion

Testing the elements of an application page is crucial to the success of any application. By conducting testing, you can cover a variety of tasks: audience research, the need for certain app mechanics, current trends, as well as increasing the app conversion itself.

Prioritize the elements you intend to test if you need to adjust your app conversion. Decide which elements need to be improved first and foremost, and outline the goals the testing is trying to achieve.

Formulating clear hypotheses and building a test plan will help you stay focused on the research. To get ideas, use all possible channels, don’t focus only on competitors' references.

Make sure to choose the tool that will suit your needs, convenience, and budget. Don't forget that it’s easier to conduct an experiment and interpret the results in a 7-day time frame, with stable traffic, and with well-defined hypotheses.

Compliance with all the rules above will help you get more realistic results, and reduce the likelihood of a deplorable experience overall.

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