Mobile apps surface no visibility into crashes, slow screens, or abandoned flows without instrumentation. Mobile Analysis Service (MAS) collects performance, stability, and behavioral data from your iOS and Android apps, and presents it in dashboards your team can act on immediately.
What MAS does
MAS covers three core areas:
Performance monitoring — tracks app launch time, page load duration, network request latency, and ANR (Android Not Responding) rates across device models and OS versions
Crash analysis — captures crash and exception details with stack traces, grouped by root cause so you can prioritize the issues affecting the most users
Custom event tracking — records user interactions and funnel steps you define, enabling analysis of feature usage, conversion rates, DAU (Daily Active Users), MAU (Monthly Active Users), and retention
Key metrics
MAS surfaces the following data out of the box after SDK integration:
|
Category |
Metrics |
|
Stability |
Crash-free session rate, crash-free user rate, ANR rate |
|
Performance |
App launch time, page render duration, network request success rate and latency |
|
User behavior |
DAU, MAU, session length, user retention, funnel conversion rate |
|
Custom events |
Any in-app action you instrument with the MAS SDK |
How it works
Integrate the MAS SDK into your iOS or Android app.
The SDK collects performance samples and event data on-device.
Data is uploaded to MAS and processed in real time.
View dashboards, set alerts, and query custom event data in the MAS console.
Who uses MAS
|
Role |
What they get |
|
Mobile developers |
Crash details and stack traces to reproduce and fix issues faster |
|
QA engineers |
Performance baselines and regression detection across device models |
|
Product managers |
Feature usage data, funnel analysis, and retention trends to guide roadmap decisions |
|
Operations teams |
Real-time monitoring and alerting during app releases and promotional campaigns |
Why MAS over general-purpose analytics
MAS is built on the same instrumentation stack that powers the Alipay app across hundreds of millions of users and a wide range of devices and network conditions. It has three advantages over general-purpose web analytics tools:
Mobile-native data model — captures mobile-specific signals such as device model, OS version, network type, and battery state that web analytics tools do not collect
Multi-terminal support — covers iOS, Android, and H5 (hybrid pages within mPaaS mini apps) from a single SDK and console
Deep mPaaS integration — correlates analytics data with mPaaS performance data, hotfix deployments, and remote configuration changes so you can trace cause and effect