Customer Success Story: Leading Music App Curbs Battery Drainage by 35% after Adopting Eagle Tester.
1.5x improvement100% Insight100% Prevention of battery consumptioninto feature decision-basedof uncontrolled battery after using Eagle Testeron battery draindrain
One of Mobile Enerlytics’ early adopters is a leading music app that ranks among the top 10 most popular apps in Google Play, with over 100 million monthly users.
This hugely popular mobile music app delivers a new release every month. Its quick release cycle requires fast-paced development, debugging, and testing. What’s more, its marketing team doesn’t want to be on the “Top 10 Battery Draining Apps” list. Boosting user engagement, therefore, is a major priority, as well as a key strategy for increasing revenue. To that end, product managers and developers need to figure out which features to include without increasing battery drainage.
Figure 1. Battery drain trend of a leading music app in 3-minute music playback
measuring energy consumption (UaH) per release.
The battery drain for each release of the mobile music app is shown in Figure 1. The bar chart dashboard is organized by measuring the energy consumption (UaH) per release. After the release of version 6.8, Eagle Tester ran a source code battery drain analysis, enabling developers to quickly identify a UI energy bug causing 2X battery drain during the music playback scenario. The bug was eliminated in release 7.0, reducing battery drainage by almost 1.5X. Eagle Tester has helped the app vendor to optimize its battery consumption in subsequent releases, such as release 7.9, whose battery drainage was reduced by almost 2X.
Occasionally, new features are added to an app. For example, release 8.0 was designed to increase user engagement, but it also consumed more battery power, therefore defeating its original purpose. Without Eagle Tester, it was impossible to accurately determine the value of the new feature in proportion to its extra battery consumption. With Eagle Tester, the product manager was able to quantify the feature’s battery consumption and make informed decisions when deploying new features.