Crash-Free Users Rate Dashboard Setup

Crash-Free Users Rate Dashboard Setup for Mobile Apps

After release you notice: Crash-Free Rate in Firebase Crashlytics is green at 99.5%. But tickets from Android 11 users pour in every half hour. Standard dashboards show an aggregate percentage, not letting you see which version, device, or region is hit. In one project we found that an overall 99.2% Crash-Free Rate masked a drop to 97.1% on Android 10 due to a bug in a specific app version's WebView. Such anomalies are invisible without drill-down. According to our data, in 30% of projects these discrepancies go unnoticed until users start mass reporting. We solve this: build a dashboard with drill-down across all needed slices: version, platform, OS, device, region. We set it up as a standalone service or extend existing monitoring. Contact us for a custom consultation.

Why Crash-Free Users Rate Matters More Than Sessions Rate

Sessions Rate understates crash impact: if a user has 10 sessions and one crashes, Sessions Rate = 90%, yet the user had a poor experience. Users Rate immediately shows the proportion of affected users. In one project with 50,000 users, Sessions Rate was 98%, but Users Rate was only 85% — a significant difference that cannot be ignored.

How to Choose a Crash Monitoring Tool

Tool choice depends on your stack and needs. Sentry provides a ready-made dashboard in minutes, while the Firebase+BigQuery+Grafana stack takes 5x longer to set up but offers unlimited flexibility. We help select the optimal solution based on budget and data volume.

Dashboard Setup in Popular Systems

Sentry Release Comparison

In Sentry, this is built into Release Health. For a custom dashboard:

# Sentry API — get crash-free rate by releases
import requests

resp = requests.get(
    "https://sentry.io/api/0/organizations/YOUR_ORG/sessions/",
    headers={"Authorization": "Bearer YOUR_TOKEN"},
    params={
        "project": "YOUR_PROJECT_ID",
        "field": ["crash_free_rate(session)", "sum(session)"],
        "groupBy": "release",
        "statsPeriod": "14d",
        "interval": "1d"
    }
)
data = resp.json()

For more details, see the official Sentry API documentation.

Datadog RUM Dashboard

In Datadog, the dashboard is built via RUM Analytics. Query for a widget:

# Crash-Free Users Rate by version
formula: (1 - (count:rum.crash{env:production} / count:rum.session{env:production})) * 100
group_by: @application.version
visualize_as: timeseries

Useful widget — compare current version with previous:

# Version n
count:rum.crash{env:production,application.version:2.3.1} / count:rum.session{env:production,application.version:2.3.1}

# Version n-1
count:rum.crash{env:production,application.version:2.3.0} / count:rum.session{env:production,application.version:2.3.0}

Firebase + BigQuery + Grafana

Firebase Crashlytics lacks a real-time export API, but BigQuery Export can stream data:

-- BigQuery: Crash-Free Users Rate by version over last 30 days
SELECT
  app_info.version AS app_version,
  platform,
  ROUND(
    100 * (1 - COUNTIF(is_fatal) / COUNT(DISTINCT user.id)),
    2
  ) AS crash_free_users_rate,
  COUNT(DISTINCT user.id) AS total_users
FROM `project.firebase_crashlytics.table_YYYYMMDD`
WHERE DATE(_PARTITIONTIME) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY app_version, platform
ORDER BY app_version DESC

Data from BigQuery is pulled into Grafana via the BigQuery Data Source plugin.

Tool and Metric Comparison

Users Rate is more important: Firebase counts Sessions Rate by default, Sentry counts Users Rate. We configure the dashboard to show both metrics.

How to Set Alerts on Crash-Free Rate

Alerts are configured in each tool: in Sentry via Alert Rules, in Datadog via Monitors, for Firebase via Cloud Monitoring + Pub/Sub. Typical threshold: Crash-Free Users Rate drops below 99% for 15 minutes. Notifications go to Slack, Telegram, or email. We set up daily or weekly digest reports.

Dashboard Layout

┌─────────────────────────────────────────────────────────────────┐
│  Crash-Free Users Rate        [last 7d]   [last 30d]   [custom] │
├──────────────────────────┬──────────────────────────────────────┤
│  Timeseries:             │  By Version:                         │
│  v2.3.1 vs v2.3.0        │  v2.3.1: 99.4% (12,340 users)       │
│  [graph]                 │  v2.3.0: 98.9% (45,120 users)        │
│                          │  v2.2.8: 99.1% (8,230 users)         │
├──────────────────────────┼──────────────────────────────────────┤
│  By Platform:            │  By OS Version:                      │
│  iOS: 99.5%              │  Android 13: 99.6%                   │
│  Android: 98.9%          │  Android 11: 98.4% ← anomaly        │
│                          │  iOS 17: 99.7%                       │
│                          │  iOS 15: 99.2%                       │
├──────────────────────────┴──────────────────────────────────────┤
│  Top Crash Issues (last 24h):                                   │
│  1. NullPointerException in CartViewModel  [+340% vs yesterday] │
│  2. OutOfMemoryError in ImageLoader                             │
└─────────────────────────────────────────────────────────────────┘

Red anomaly on Android 11 is a real pattern: often tied to a system library bug that triggers the app.

What’s Included in the Work

• Analysis of current data sources (Firebase Crashlytics, Sentry, Datadog)
• Dashboard schema design: metric selection, slices, thresholds
• Implementation via API or SQL queries
• Alert configuration for Crash-Free Rate drops below 99%
• Integration with Slack / Telegram for daily digest reports
• Documentation and team training on dashboard usage

We guarantee 5+ years of experience with Crashlytics, Sentry, and BigQuery. We have configured dashboards for 50+ mobile projects. Use our dashboard setup service and get a complete picture of your app's stability. Contact us for a consultation.

Timeline

• Dashboard in Sentry or Datadog: 4–8 hours
• Firebase + BigQuery + Grafana: 1–2 days
• Pricing is calculated individually.

Get in touch, we will assess your project end-to-end. We will set up the dashboard so you always see the real picture of your app’s stability.

Mobile App Analytics: Firebase, Amplitude, AppsFlyer and Attribution

Our team regularly encounters projects where analytics is already "set up" but yields no real insights. A typical example is a startup with 50k DAU: tracking dozens of events without a single answer to the question "why don't users reach payment?". In two weeks we built a basic funnel and found that 70% of users drop off at the phone number verification screen. After fixing the bug, retention increased by 12%. The takeaway: analytics should start with specific questions, not tracking everything indiscriminately.

Why Event Taxonomy is the Foundation of Mobile App Analytics?

Firebase Analytics, Amplitude, Mixpanel — technically similar. The difference lies in what you put into them. A common mistake: events like screen_view, button_tap_1, button_tap_2 without context. A month later, no one remembers what button_tap_2 means.

Proper taxonomy: object + action + context. product_viewed, checkout_started, payment_completed with parameters product_id, category, price, source. This allows building funnels, cohort analysis, and retention without additional tracking.

We document the naming convention in a tracking plan — a document (Google Sheet or Amplitude Data Catalog) describing every event, its parameters, and triggering conditions. The tracking plan is synced with the analytics team before development begins, not after. This approach ensures that data remains interpretable months later and doesn't become a dump. Experience from 50+ projects confirms: without a tracking plan, analytics maintenance costs increase 2-3 times due to rework.

What Should You Choose for Mobile App Analytics: Firebase, Amplitude, or Mixpanel?

The table below highlights key differences between the three popular platforms. Choice depends on budget, traffic, and tasks.

Firebase Analytics — free, deep integration with Google Ads, BigQuery export for raw data. Limitations: data latency up to 24 hours, limited funnels. For startups with Google Ads traffic, it's the first choice.

Amplitude — product analytics focused on cohorts and user journeys. Journeys (formerly Pathfinder) shows actual paths between events — not assumed funnels but real routes. Session Replay records sessions for UX analysis. The free tier up to 10M events/month is enough for most products at launch.

Mixpanel — close to Amplitude, stronger in real-time segmentation. Insights, Funnels, Retention cover 90% of product analysts' tasks.

How to Solve Multi-Channel Attribution with AppsFlyer?

Knowing where a user came from is a separate task. Firebase Attribution works only within the Google ecosystem. For multi-channel attribution (Facebook Ads, TikTok, Apple Search Ads, programmatic), an MMP (Mobile Measurement Partner) is needed.

AppsFlyer is the market leader. OneLink — universal deep link working on iOS and Android, correctly attributing installs from any channel. Protect360 — built-in fraud protection (fake installs, click injection on Android). Adjust and Branch are competitors with similar features. Branch excels in deep linking; Adjust is popular in gaming.

According to Apple, with iOS 14.5, apps must obtain user permission via ATT before collecting IDFA for tracking. AppsFlyer uses probabilistic matching (IP + user agent + timing) for these users — accuracy is lower but better than nothing. SKAdNetwork and Privacy Preserving Attribution provide aggregated data from Apple with a 24-72 hour delay.

How to Set Up Crash Analytics to Not Miss Bugs?

Firebase Crashlytics is the standard for crash reporting. It automatically groups crashes by stack trace, shows affected users %, and sends velocity alerts when crash rate increases by more than 10% per hour.

Important: symbolication. On iOS, .dSYM files must be automatically uploaded with each build — via Fastlane upload_symbols_to_crashlytics or Xcode Cloud built-in. Without symbols, crashes in Crashlytics appear as memory addresses. This happens more often than expected when switching to a new CI — in one project with 500k users, we found that 40% of crashes remained unsymbolicated due to a missing CI/CD step. After automation, bug response time dropped from 3 hours to 15 minutes.

For React Native and Flutter, @sentry/react-native and sentry_flutter provide additional context: breadcrumbs, network requests before the crash, Redux/Provider state.

Below is a comparison of popular crash analytics tools to choose according to your needs.

Environment Setup

Three environments with separate Firebase projects: dev, staging, production. Mixing analytics from test sessions and production is a common mistake that skews all metrics. On iOS via GoogleService-Info.plist per scheme, on Android via google-services.json in each flavor folder.

Timelines: basic analytics with Firebase + Crashlytics — 3-5 days. Full tracking plan + Amplitude/Mixpanel with funnels and cohorts — 2-3 weeks. Attribution via AppsFlyer with deep linking and fraud protection — 1-2 weeks. Cost is calculated individually based on integration complexity.

What Is Included in Our Work

As part of analytics implementation, we provide:

• Development and approval of a tracking plan with product and marketing teams.
• SDK integration (Firebase, Amplitude, Mixpanel, AppsFlyer) considering your stack (Swift/Kotlin/Flutter/React Native).
• Setup of funnels, cohorts, dashboards, and alerts.
• Automation of symbolication and .dSYM upload via Fastlane.
• Documentation of events and parameters.
• Team training on the analytics platform.
• Two weeks of post-release support and tracking adjustments.

Our experience: 7 years of analytics implementation and over 80 successful projects in mobile development. We guarantee data correctness and transparency at every stage.

Contact us for a consultation on setting up analytics for your app. Request an audit of your current analytics — and we will show you which metrics you are losing.