App data measures app usage in a given market or location. Additionally, it collects information about the users of these mobile apps.
This data comes from app stores like Google Play and App Store as well as internal data from companies selling the apps. Additionally, websites offering apps, either on their site or in an app store, can install cookies that track website visitors to their eventual downloads, providing valuable demographic and behavioral information about app users.
There are several different types of information presented by mobile app datasets, available in APIs or CSVs.
Internal data includes user profiles, language, app upgrades, and real-time location via location tracking SDKs.
Alternately, data that may be more publicly available is usage data. Examples include number of downloads, number of active users, average number and duration of sessions per user, and, of course, in-app purchase data.
Companies use this data to monitor their performance among existing customers and their promotional campaigns targeting new customers. It also lends itself to market research for companies weighing whether to enter a new market or develop a new app in their suite of products.
With the millions of mobile apps available and the many sources of data for any of them, the most important test of quality is the seamless integration of data. Naturally, this process must occur constantly.
Once you have collected and standardized the data, focus on proper cleansing.
Users, regulators and sometimes even the app developers and advertisers are unaware of the extent to which data flow from smartphones to digital advertising groups, data brokers and intermediaries that buy, sell and blend information, he said.
Gen Z users spend an average of 4.1+ hours per month in top non-gaming apps, or 10% longer than older demographics. They also engage with apps more often, with 20% more sessions per user in non-gaming apps, at 120 sessions per month per app, compared with older groups.
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