A session is a period of time wherein a user interacts with an app. Usually triggered by the opening of an app, a session records the length and frequency of app use to show developers, marketers and product managers how much time users spend within an app.
For example, session data can be used to determine the average length of time users spend on an app, as well as the time of day users are most likely to engage with a particular app.
Sessions can be analyzed in a way that reveals how users truly interact with an app.
By combining analysis of session metadata (e.g., session length) with usage data (e.g., tracking certain in-app events) and then analyzing behavior across a user base, app businesses can identify opportunities or problems within their apps that can be optimized for improved performance down the line.
Proper mobile marketing analysis of sessions can ultimately help companies to create an optimal experience for users that maximizes the potential for monetization and retention.
Sessions are tricky to define on mobile. Some companies define sessions as the period of time from when a user opens an app, but this overlooks the way most users now engage with mobile devices. Rather than engaging with a single app at the same time, most mobile users bounce between multiple apps. It might be switching from browsing the mobile web to quickly changing track in a music app; it could be shifting from one messaging app to another; maybe it’s even by following deep links back and forth from two apps owned by the same company.
This is why Adjust’s definition of sessions is slightly different from the norm. We define a session as “a span of activity separated by 30 minutes (minimum) to the next”. This helps us to account for the multitasking nature of mobile device usage and present a more accurate picture of how often and how long a user may be on an app.
Session analysis is easily accessed through Adjust’s dashboard. Once the Adjust SDK is integrated with an app, businesses are able to analyze sessions and use that to understand user behavior further.