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Mobile game data analytics

Want accurate predictive analytics post-IDFA?

Our predictive SKAN-enabled dashboards turn advanced data modeling into user-friendly visualizations. They act as a single source of truth and present clear KPIs for all your team. 

What are the key mobile game metrics to analyze?

Analytics will always be key in efforts to develop, optimize, and grow a mobile game. It is crucial to analyze what players are doing with your game, in addition to calculating metrics and key performance indicators (KPIs), such as daily active users and retention rates.

But what are some of the most essential metrics to track for your mobile game, and how can you gather the most accurate data for your game post-IDFA?

What are the key mobile game metrics to analyze?

In order to help grow your mobile game, you will need to track two broad categories of metrics: those related to user engagement, and those concerning monetization

And of course, those two fundamental metric types go hand in hand. If your game is fun and engaging, you are more likely to be able to gain healthy revenue from it. That will be the case irrespective of how your game makes money – such as through revenue from ads, monthly subscriptions, or in-game purchases (the latter also known as ‘microtransactions’). 

So, now that you know these two broad types of metrics are inherently interlinked, what are some of the most important mobile game metrics to analyze? 

Essential metrics for measuring aspects of user engagement include: 

  • Daily active users (DAU), which refers to the number of users who open your game in a day 
  • Monthly active users (MAU), which is the number of users who opened your game at least once during the month
  • Stickiness rate, which is the ratio between DAU and MAU. This metric allows you to measure how effective your game is at keeping players coming back each day. You can calculate it by dividing DAU by MAU, and then multiplying by 100. So if, for example, your game has 350 DAU and 4000 MAU, this would equate to a stickiness rate of 8.75%. This means that in a 30-day month, the average player of your game opens it on 8.75% of the days, or about 2.6 days out of 30 (8 divided by 100 x 30 = 2.625). 
  • Retention rate, which is another way to measure your game’s ability to keep players returning. There are several different measures and formulas that you can use to track retention – the approach you choose will likely depend on your goals for user engagement. Games developers typically want to aim for high levels of daily usage, which means they need to track the number of people who keep coming back to the game each day after their first login. As with stickiness, if you can achieve high retention with your mobile game, you will be in a better position to drive higher user lifetime value (LTV). 


The below metrics will help determine the effectiveness of the monetization strategy you’re using for your game: 

  • Average revenue per daily active user (ARPDAU), which you can calculate by dividing daily revenue by DAU. So, if your game generates $6,000 a day in revenue and you have 350 DAU, which would mean your ARPDAU is $17.14. 
  • In-game purchases, also known as IAPs, or microtransactions. If in-app purchases are crucial to your game’s business model, creating events for tracking each one will help you see how well your game mechanics are working for monetization. 

Why is it difficult to get accurate data for your mobile game?

Apple’s introduction of its App Tracking Transparency (ATT) opt-in privacy framework for all Apple devices after the release of iOS 14 – and enforced after iOS 14.5 – profoundly impacted the world of mobile advertising. 

ATT requires all iOS apps to seek permission from users before they are able to share their data with other companies. Before ATT, all iPhone users were automatically opted into such data tracking, unless they had used the Limited Ad Tracking setting to actively opt-out. 

This, in turn, enabled developers and marketers to access user-level data and user-level attribution through the unique iOS advertising identifier known as the Identifier for Advertisers, or IDFA. This unique, random identifier assigned by Apple to each and every iOS device, allows advertisers to monitor how users engage with their ads, as well as their post-install activity. 

ATT introduced a pop-up displayed within apps, asking the user whether they would like to “allow the app to track your activity across other companies’ apps and websites”. With users being specifically asked whether they want to opt in or opt-out, and being opted out by default, this has led to most users now opting out. 

As a result, it has become much more difficult for developers to access accurate user-level data that will allow them to best optimize their game for growth. 

So, with most iOS users from version 14.5 and above denying access to their user-level data, what else can you do to obtain accurate data for your mobile game? Well, you may have to largely depend on SKAdNetwork (SKAN), which is Apple’s privacy-centric aggregated distribution framework. 

However, SKAN only provides limited data with which to optimize the performance of your mobile game. Furthermore, with the recent introduction of SKAN 4.0, cohort data grouping has become much more difficult. 

As for Android, Google has set out its own proposal for the management of privacy-safe mobile advertising and marketing measurement on the Android platform. It’s called Privacy Sandbox for Android, and it aims to heighten consumer privacy, at the same time as continuing to allow advertisers to create ad campaigns and measure KPIs in relation to their investment. 

The Privacy Sandbox project’s goal is to depreciate the Google advertising ID, or GAID, which is Google’s own device identifier for advertisers that allows the anonymous tracking of user ad activity on Android devices. However, it is also true that restrictions are much less harsh on Android than they are on iOS. 

How accurate can data analysis be for mobile games post-IDFA?

So, you might imagine on the basis of the above that it can be quite difficult to achieve accurate data analysis for a mobile game in the post-IDFA environment. 

And sadly, that can be very much true for many developers. If data is the fuel that you need to help power the performance and growth of your game, it is a fuel that has become harder to come by in an age of increasingly limited user-level data. 

Privacy has become an ever-greater priority in the mobile app marketing landscape, thanks to developments like Apple’s introduction of its ATT prompt. 

Innovation, however, is also continuing, and there are now various solutions – both new and relatively established – that app developers can turn to, in order to access granular and accessible insights into how well their games are doing. 

One example of such a solution is Apple’s aforementioned SKAdNetwork, also known as SKAN. Apple introduced this privacy-centric API in 2018; it is a type of direct install attribution, designed to provide accurate attribution to advertisers for their iOS campaigns, without revealing any user-level or device-specific data, thereby preserving user privacy. Within this framework, SKAN conversion values enable marketers to grade user value following the install. 

Also potentially invaluable for gaining access to accurate data analysis for your mobile game post-IDFA, is predictive analytics, of which examples include user lifetime value (LTV) and return on advertising spend (ROAS). With predictive analytics, marketers can confidently make data-driven decisions about users’ future value, even if they are dependent on very limited data points at an early stage of the funnel. 

In summary, then, can data analysis still be accurate and granular in this changed environment in which the usability of IDFA has been severely limited? The short answer is yes. However, it is a task that continues to be increasingly difficult as a result of the aforementioned privacy-oriented changes, and a mobile measurement partner (MMP) won’t be sufficient on its own to help you achieve it. 

How SuperScale can get you accurate mobile game data on a single dashboard

So, if you can’t count on an MMP, who can you turn to if you wish to gather accurate data on your mobile game? 

Here at SuperScale, we stand ready to provide you with a complete all-inclusive solution for your mobile game. Contained within our service is a fully managed, state-of-the-art data analytics platform, which can give you incomparable predictive ROAS for your mobile game, and enable user acquisition (UA) and LTV growth, augmenting your existing in-house capabilities. 

What’s more, you can enjoy such visibility – as provided for a broad range of metrics – via a single dashboard. Indeed, our product and marketing SKAN-enabled dashboards can be custom-built to suit particular needs; the end result is industry-leading ROAS and LTV forecasting for a post-IDFA world. 

To learn more about whether SuperScale could be the right partner for your efforts to help realize the maximum potential of your mobile game, please don’t hesitate to request a 15-minute qualification call with us today.


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