Introducing the Device Usage dashboard | Adjust

Summary

Adjust has introduced a Device Usage dashboard in Datascape to help teams understand the composition of their active user base and explain why some releases appear to have no immediate metric impact. The central argument is that app, OS, and SDK updates propagate gradually: days or weeks after launch, a meaningful share of users may still be on older versions, so aggregate metrics can look flat or misleading until adoption is segmented. The dashboard is preconfigured with widgets showing DAU by SDK version, DAU by OS version for iOS and Android, DAU by app version for iOS and Android, and DAU by ATT status.

This lets teams track SDK adoption and legacy SDK usage, monitor upgrade velocity, compare behavior across OS versions, and see how iOS users are distributed across consent states. Supported dimensions include app version, OS name, OS version, OS version major, device type, SDK version, ATT status, country, language, and date, enabling device-and-country analysis of audience composition. The article provides no hard benchmark data; its key data points are the available dimensions and prebuilt visualizations.

Actionable takeaways: use the dashboard before and after app releases or SDK rollouts to validate adoption and isolate version-specific performance changes; add OS and device context to engagement, monetization, and attribution analysis; combine country and device dimensions to assess market-level audience shifts; and save the template as a custom dashboard to fit team needs. For ad ops decision-makers, this supports more accurate diagnosis of performance fluctuations, better rollout timing, and cleaner segmentation when evaluating campaigns or monetization by app/device environment. Because the dashboard is a template in Dashboards > All dashboards, teams can adapt settings and save a custom view, reducing manual report-building and speeding investigation.

It is most useful for adding context to performance analysis rather than replacing attribution reporting.

Analyst Note

The Device Usage dashboard is less about device data than about the shifting role of the MMP. Adjust — now under AppLovin — is packaging analytics as pre-built templates rather than custom reporting projects, nudging closer to the BI layer that UA teams have historically assembled themselves or bought separately. What's notable here is the framing: version fragmentation is treated not as a technical footnote but as an explanation for why performance metrics can appear to stall after a release.

The key implication is that dimensions most teams treat as static — OS version, SDK version, ATT status — are being repositioned as segmentation variables. That carries more weight in a post-ATT environment, where consent state can quietly reshape an iOS cohort's apparent performance long after a campaign has ended, and where comparing network or store-level results against a base still running an older build is a familiar source of misread variance.

Competitively, this follows a broader pattern of attribution platforms expanding into self-serve analytics to remain sticky as deterministic measurement erodes. Worth watching is whether pre-built dashboard templates become the default entry point for performance reviews, displacing ad hoc report building.