
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.
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?
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:
The below metrics will help determine the effectiveness of the monetization strategy you’re using for your 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.
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.
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.
Even the most successful blockchain gaming publishers still rely primarily on organic growth. They too struggle with scaling their games & apps with performance-driven marketing results. To grow blockchain games sustainably, like in mobile F2P markets, studios need updated fundamentals for performance-driven growth.
Watch SuperScale’s Ivan Trančík track session at Pocket Gamer Connects London 2022.
We’re the games growth specialists, using a combination of data and services to help publishers and owners grow their games. Get in touch for a free performance evaluation and find out how to get more revenue from your game while saving time and costs.
For technical issues and general inquiries, please contact our support.
SKY PARK Offices
Bottova 2622/2
Bratislava, 811 09
Slovakia
Beyond, Aldgate Tower
2 Leman St
London, E1 8FA
United Kingdom
Hacker Dojo
855 Maude Avenue
Mountain View 94043
San Francisco
We use cookies on our website.
By clicking “Accept All”, you consent to the use of all the cookies. You may click “Manage cookies” to provide a controlled consent or decline cookies. More information in Cookie Policy.
Cookie | Duration | Description |
---|---|---|
__cfruid | session | Cloudflare sets this cookie to identify trusted web traffic. |
cookielawinfo-checkbox-advertisement | 1 year | Set by the GDPR Cookie Consent plugin, this cookie is used to record the user consent for the cookies in the "Advertisement" category . |
cookielawinfo-checkbox-analytics | 1 year | Set by the GDPR Cookie Consent plugin, this cookie is used to record the user consent for the cookies in the "Analytics" category . |
cookielawinfo-checkbox-functional | 1 year | The cookie is set by the GDPR Cookie Consent plugin to record the user consent for the cookies in the category "Functional". |
cookielawinfo-checkbox-necessary | 1 year | Set by the GDPR Cookie Consent plugin, this cookie is used to record the user consent for the cookies in the "Necessary" category . |
cookielawinfo-checkbox-others | 1 year | Set by the GDPR Cookie Consent plugin, this cookie is used to store the user consent for cookies in the category "Others". |
cookielawinfo-checkbox-performance | 1 year | Set by the GDPR Cookie Consent plugin, this cookie is used to store the user consent for cookies in the category "Performance". |
elementor | never | This cookie is used by the website's WordPress theme. It allows the website owner to implement or change the website's content in real-time. |
viewed_cookie_policy | 1 year | The cookie is set by the GDPR Cookie Consent plugin to store whether or not the user has consented to the use of cookies. It does not store any personal data. |
Cookie | Duration | Description |
---|---|---|
__cf_bm | 30 minutes | This cookie, set by Cloudflare, is used to support Cloudflare Bot Management. |
visitor_id971653 | 10 years | Used by Pardot to provide protection against hackers. |
visitor_id971653-hash | 10 years | Used by Pardot to provide protection against hackers. |
Cookie | Duration | Description |
---|---|---|
_calendly_session | 21 days | Calendly, a Meeting Schedulers, sets this cookie to allow the meeting scheduler to function within the website and to add events into the visitor’s calendar. |
_gaexp | 1 month 6 days 9 hours 24 minutes | Google Analytics installs this cookie to determine a user's inclusion in an experiment and the expiry of experiments a user has been included in. |
Cookie | Duration | Description |
---|---|---|
_ga | 2 years | The _ga cookie, installed by Google Analytics, calculates visitor, session and campaign data and also keeps track of site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognize unique visitors. |
_ga_PSXVCB6R9N | 2 years | This cookie is installed by Google Analytics. |
_gat_UA-173282740-1 | 1 minute | A variation of the _gat cookie set by Google Analytics and Google Tag Manager to allow website owners to track visitor behaviour and measure site performance. The pattern element in the name contains the unique identity number of the account or website it relates to. |
_gid | 1 day | Installed by Google Analytics, _gid cookie stores information on how visitors use a website, while also creating an analytics report of the website's performance. Some of the data that are collected include the number of visitors, their source, and the pages they visit anonymously. |
CONSENT | 2 years | YouTube sets this cookie via embedded youtube-videos and registers anonymous statistical data. |
pardot | past | The pardot cookie is set while the visitor is logged in as a Pardot user. The cookie indicates an active session and is not used for tracking. |
Cookie | Duration | Description |
---|---|---|
uuid | never | MediaMath sets this cookie to avoid the same ads from being shown repeatedly and for relevant advertising. |
VISITOR_INFO1_LIVE | 5 months 27 days | A cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface. |
YSC | session | YSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages. |
yt-remote-connected-devices | never | YouTube sets this cookie to store the video preferences of the user using embedded YouTube video. |
yt-remote-device-id | never | YouTube sets this cookie to store the video preferences of the user using embedded YouTube video. |
yt.innertube::nextId | never | This cookie, set by YouTube, registers a unique ID to store data on what videos from YouTube the user has seen. |
yt.innertube::requests | never | This cookie, set by YouTube, registers a unique ID to store data on what videos from YouTube the user has seen. |