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Google Analytics for Mobile Apps

analytics

After a mobile app is developed, the next steps include marketing to find users, learning about the users who use it, and how they navigate it.  It is also important to find the most valuable users in terms of revenue and other conversion metrics.  With analytics tracking code in the app, owners can collect data on users and sessions.  Analytics reports accessed within AdMob – another Google product – make it easy for mobile app developers to measure performance.

Generating Revenue

One goal for many app developers is generating revenue with their app.  A common method comes in the form of paid downloads which charge a one-time fee.  Many developers use the strategy of a freemium model so users can test features before paying for the premium version.  They could also sell through in-app purchases or in the real world through traditional e-commerce.  Revenue can also be generated by display ads for other apps, often in the form of a small banner along the bottom of screen or by displaying a full screen when the app is first opened.  Although this second option offers a very visual way of getting the user’s attention, it can disrupt experience.  It is okay to try multiple revenue models and use analytics to see which one works the best.

User Sources

To view performance and determine which sources generate revenue, marketers can log into an AdMob account and review analytics just as they would through the standard Google.com/Analytics site.   A useful exercise is to go to sources and see what got users to the site to purchase the app.  Detailed reports are available for Google Play and iTunes which can help determine which is the better driver of traffic.  The Google Play Referral Flow provides detail specifically on how users move through each step of the acquisition process.

High and low value users

After examining how users are acquired, the next step is to determine which ones are high value which depends on how the developer defines them.  Is the KPI used the level of engagement or level of monetization which is when users make in-app purchases?  A user may use the app occasionally but not spend any money which is probably a low value user.   Developers can use analytics data to compare high and low value users in terms of geography, interests, and number of sessions and then adjust marketing campaigns accordingly.  These comparisons among different types of users can be done with segmentation which allows for real time filtering of data.

Screen Interactions

To drill into how users interact with elements on each screen, Event Tracking is needed.  The default analytics data only shows activity with the screen, not any of the interactive content such as button clicks.  When this is implemented properly with categories used to identify groups, actions to describe the events and labels for additional info, developers can see how positive behaviors (purchases) and negative behaviors (exits) are influenced by Events that occurred in a user session.  For example, do users leave the app after a specific interaction with it?

User Loyalty

Loyalty is a good measure of user interest in the app and is a useful metric to compare to conversions.   If conversions are low for users who only had a couple sessions, it could be users did not see any benefit to using the app and chose not to return.  In this scenario, advertising early on in session use may be needed to show users the benefits of the app and incentivize them to continue using it.  It is also possible they are coming in through a marketing campaign that is attracting the wrong audience.  Marketers can look for common attributes in a segment of users who had a low loyalty rate and adjust campaigns as needed.

Remarketing

Remarketing  provides an opportunity for marketers to target content to visitors that visited site or the app based on specific criteria.   For example, users that have not opened the app for one month can be shown an offer specific to them to invite them to use the app again.  Audience lists in analytics offers predefined lists and allows apps owners to also create custom lists based on user configuration of segments.  Once these lists are established in analytics, they are available as target audiences for AdWords campaigns.

Creating a solid app is a huge accomplishment, although it is only the first step.  Using analytics can help businesses make decisions about revenue models for app monetization, tools to use for promotion, and assist in the development of a measurement plan for specific objectives.   Use of AdWords and AdMob can enhance the data available to businesses and should be used in conjunction with analytics for the full picture.

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