Case: Lab Cave Achieves 45.8% Conversion Boost by A/B Testing App Store Screenshots

lab cave app store screenshots

It goes without saying, the two major app stores have different layouts, although the product page elements are quite the same. The differences were evident to the ASO experts at Lab Cave, a mobile growth company providing ASO and Ad Mediation services. After having tested their Play Store visuals, the company made a sensible decision to optimize their App Store screenshots.

Discovering what works better for different app stores was a strong reason, but the main one, however, was to identify what boosts the conversion rate from page visits to installs.
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Guide to Mobile A/B Testing: Proven Strategies, Professional Tips, and App Store Optimization Best Practices

mobile A/B testing guide by SplitMetrics

Mobile A/B testing has been around for quite a while and for a good reason. It can be widely used for marketing purposes: from getting data on the behavior of the target audience to user acquisition on major app stores.

If you understand the value of data-driven decisions, mobile A/B testing might become your go-to solution as it lets you get beyond the guesswork. Splitmetrics teamed up with Apptimize and created a comprehensive guide to App Store and in-app A/B testing which will help you grow your mobile business.
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All You Need to Know about A/A Testing – from Goal Setting to Results Interpretation

A/A testing is the tactic of using a testing tool to test two identical variations against each other. Whether it is worth to conduct A/A testing and, if so, for what purposes are the questions that invite conflicting opinions.

In this post, we explore why some users of testing tools like SplitMetrics practice A/A tests and dwell on the things they need to keep in mind while performing this sort of tests.
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Unriddling Search in App Store: How to Run Category and Search A/B Tests

category and search A/B test

Ever thought how category and search ranking affects your app’s conversion rate? Taking into consideration that about 65% of downloads are the result of a search in the App Store, the most successful mobile marketers never disregard the optimization of their apps for competitive surrounding.

If you aim at winning search in the App Store, a consistent mobile A/B testing strategy is a must. Search and category split tests should become an integral part of such strategy and today we’ll discuss how to run this kind of A/B experiments to guarantee the best possible results.
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Advancing Mobile A/B Testing with Bayesian Multi-Armed Bandit

A/B Testing with Multi-Armed Bandit

In the course of an A/B experiment, the correct calculation of a sample size is one of the key success ingredients. Yet, sometimes the amounts of traffic necessary for statistical significance of tests put app publishers off. Indeed, a required sample size can be large that means a test lasts longer than you’d like.

However, this obstruction is not that dramatic if you run your A/B tests with help of SplitMetrics. The thing is the platform can apply an alternative approach called Bayesian Multi-armed Bandit (MAB), which can solve the above-mentioned drawback without even bothering you.

A Bayesian Multi-armed Bandit test allows choosing an optimal variation of the two or more. Unlike a classic A/B test, which is based on statistical hypotheses testing, a Bayesian MAB test proceeds from Bayesian statistics. In this post, we’ll learn more about the principles behind it.
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Creative Sets: Testing within Apple Search Ads and Limitations of This Method

Search Ads Creative Sets testing

The introduction of Creative Sets gives Apple Search Ads users a great opportunity to align various creatives to different keywords. On top of that, now app publishers can test Apple Search Ads assets. However, it doesn’t mean that Apple’s update made full-scale A/B tests possible within the App Store itself.

In this post, we will figure out how Creative Sets can be used for Apple Search Ads assets testing and what are the limitations of this experimental method.
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SplitMetrics Latest Update: Advanced Video Analytics for App Preview A/B Tests

SplitMetrics app preview A/B tests

Video content is conquering the world. According to Hubspot, 90% of users state that product videos facilitate the decision-making process. However, when it comes to app previews in the App Store, one shouldn’t rush to pack their product pages with three 30-second long videos.

There are a lot of cases when a clumsy app preview doomed app’s conversion giving the impression that the publisher doesn’t really care. Nevertheless, smart app previews work their video magic and boost downloads. According to our study, optimization of video previews triggers the average conversion increase of around 16%.

A/B testing is one of the best ways to reduce the risk factor of app previews. To take your video experiments to the next level making them even more impactful, the SplitMetrics team prepared brand new advanced analytics for app previews in the App Store.
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Determining Sample Size for Mobile A/B Testing: Method Based on Statistical Hypothesis Testing

Sample Size for mobile A/B testing

One of the most popular questions app publishers ask is how much traffic they need to run valid A/B tests. Unfortunately, there is no answer with a magic number that will fit every single experiment. An optimal traffic volume for mobile A/B testing is individual and depends on such factors as a traffic source, app’s conversion rate, and targeting.

However, we can get to the bottom of sample size calculations for mobile А/В testing. It’s really important to have a full understanding of it as sample size has a considerable effect on checking the significance of the observed difference in variations performance.

In this post, we’ll also review one of sample size measuring methods which is widely used and helps to make a statistically valid decision based on the results of your mobile A/B testing.
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Case Study: OLBG A/B Tests iOS Screenshots and Gets 61% Conversion Boost

ios screenshot tests with SplitMetrics

With ever increasing ad costs, harnessing organic traffic and achieving massive App Store presence should be publishers priority. OLBG, the company that creates apps with sports betting tips, is no exception.

OLBG decided to use A/B testing to improve their iOS screenshots, increase conversion rate and maximize presence in the App Store as it was their biggest source of free organic installsJess who looks after the marketing at OLBG agreed to share the story behind the optimization of their iOS screenshots.
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Mobile A/B Testing Results Analysis: Statistical Significance, Confidence Level and Intervals

statistical principles of SplitMetrics mobile A/B testing

The aim of mobile A/B testing is to check if a modified version of an app page element is better compared to the control variation in terms of a certain KPI. In the context of app store pages A/B testing, conversion becomes a core KPI most of the times.

However, we all know that it’s not enough to create an experiment with 2 variations, fill it with a dozen of users and expect distinctive and trustworthy results. What turns any split-test into an A/B test you can trust then?

The main characteristic that defines a successful A/B experiment is a high statistical significance which presupposes you’ll actually get a conversion increase the test promised uploading a winning variation to the store.
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