Battery Optimization Template for PMs

For a typical consumer app, aim for less than 5% per hour during active foreground use and less than 0.5% per hour when backgrounded. Navigation and fitness apps that use continuous GPS will naturally consume more (10-15%/hr is reasonable during active tracking). Compare against competitors in your category. Check your app's ranking in Settings > Battery on both platforms, and investigate if your app appears in the top 3 battery consumers for more than 5% of users.

Optimize first, disable only as a last resort. Users turned on Low Power Mode to extend battery life, but they still want your app to work. Reducing sync frequency, lowering location accuracy, and simplifying animations are good optimizations that preserve functionality. Disabling background sync entirely or removing location features creates a degraded experience. Only disable features that are genuinely optional (video autoplay, analytics, background content preloading). For tracking which features users rely on most, the [mobile analytics plan](/templates/mobile-analytics-plan-template) covers feature usage instrumentation.

Xcode's `xctrace` command-line tool can capture energy traces during automated tests. For Android, use `dumpsys batterystats` before and after a test scenario, then analyze with Battery Historian. Neither gives you exact mAh measurements in CI, but both provide relative comparisons against a baseline. Set a threshold like "energy usage during test scenario must not increase by more than 10% vs baseline" and fail the build on regression. For deeper investigation, [usability testing](/glossary/usability-testing) with real devices in realistic conditions provides ground truth.

Network activity, specifically frequent small requests that prevent the radio from entering sleep mode. Every network request wakes the cellular radio, which stays powered for 10-30 seconds after the request completes. Making 20 small requests over 5 minutes keeps the radio continuously active. Batching those into 2-3 larger requests lets the radio sleep between bursts, reducing energy consumption by 40-60%. This is why request coalescing and push-over-polling are the highest-impact optimizations for most apps.