Sensor Tower Gains Richer App Update Timeline and Faster-Than-Ever Store Intelligence

Key insights

6 takeaways

01
Sensor Tower expanded its App Intelligence platform to track 14 new update types for iOS apps and 6 new update types for Android apps.
02
The App Update Timeline now provides granular tracking of metadata changes, including specific insights into Top In-App Purchases and Related Apps for iOS.
03
Store Intelligence performance has been upgraded to significantly increase loading speeds for large datasets and long date ranges.
04
New data granularity options in Store Intelligence allow users to toggle between Auto, Daily, Weekly, and Monthly views for download and revenue estimates.
05
Custom data granularity settings selected within the Store Intelligence platform are now automatically applied to exported CSV files and API data requests.
06
The new tracking features for App Intelligence began capturing data in early December 2015, with no retroactive data available for updates prior to that month.

Summary

Sensor Tower has implemented significant upgrades to its App Intelligence and Store Intelligence platforms to improve data granularity, processing speed, and tracking capabilities. These enhancements, introduced in December 2015, aim to provide enterprise users with more responsive tools for monitoring mobile application performance and market trends across iOS and Android ecosystems.

The App Intelligence platform now features an expanded App Update Timeline, which tracks 14 new update types for iOS and six for Android. Key additions include the ability to monitor changes in Top In-App Purchases and Related Apps, allowing users to track competitive shifts in monetization strategies and store visibility. These updates are accessible via the App Profiles section, with data collection beginning in early December 2015.

Simultaneously, the Store Intelligence platform has undergone performance optimizations to accelerate the loading of large datasets. A primary improvement is the introduction of flexible data granularity, allowing users to toggle between daily, weekly, and monthly views for download and revenue estimates. This functionality extends to exported CSV files and API outputs, providing greater analytical versatility for long-term trend analysis. By incorporating these user-requested features, the platform enhances its utility for developers and analysts seeking deeper insights into app store dynamics.