Real User Monitoring (RUM) Setup for Mobile Apps: Why You Need It

Imagine this: a user on a Xiaomi Redmi Note 9 complains that the product list screen lags. Synthetic monitoring shows a perfect 60 FPS, but on LTE in their region, there's a network timeout at 8 seconds. Without RUM, you only find out from Play Market reviews. RUM captures every UI stutter, every ANR, every network error. Our experience shows: 30% of critical bugs are invisible in synthetic tests. Implementing RUM across 50+ projects cuts diagnostic time by 3x and saves up to $1500/month by quickly pinpointing bottlenecks.

The difference between RUM and synthetic monitoring

RUM collects data from real devices, while synthetic monitoring runs in a controlled environment. Synthetic monitoring won't show that the payment screen on a Huawei P40 lags due to overdraw in a FrameLayout. RUM captures every event: View, Action, Resource, Error, Long Task. Among these, Long Task is the most diagnostically valuable metric. Datadog RUM docs (Datadog RUM docs) state that over 50% of performance incidents are discovered through Long Tasks.

What data does RUM collect?

Standard event set in mobile RUM:

• View — screen transitions (open, close, duration)
• Action — tap, swipe, scroll, LongPress
• Resource — HTTP request: URL, method, status, size, latency
• Error — handled exception, unhandled exception, ANR, crash
• Long Task — operation on main thread > 100ms (Android) / main runloop > 16ms (iOS)

Of all events, the most diagnostically valuable is Long Task correlated with View. If a 250ms Long Task occurs during the payment screen transition for 15% of users, that's a concrete performance bug, not a vague "it sometimes lags."

Why Long Task is the key indicator

Long Task directly indicates UI blocking. For example, on one project after RUM integration, we discovered that loading the order list on Android performed a heavy sort on the main thread (Long Task 400ms). The fix: move the sort to a coroutine. Without RUM, this issue would have gone unnoticed.

RUM tool comparison

For most products, Firebase Performance is a good start (free, zero-config for networking). But as soon as you need correlation with server traces or custom business attributes, you move to Datadog or Sentry. Datadog processes 10x more events than Firebase Performance at a comparable cost.

How to choose sessionSampleRate

sessionSampleRate determines the percentage of sessions sent to RUM. With high DAU (e.g., 1 million), 100% sessions is expensive. Standard practice: 100% for new releases for the first 48 hours, then drop to 20–30%. To find rare errors (e.g., ANRs on 0.5% of devices), you need high sampling. A balanced choice is 50%.

Setup example with Datadog RUM

import DatadogRUM

RUM.enable(with: RUM.Configuration(
    applicationID: "your-rum-app-id",
    sessionSampleRate: 80,         // 80% of sessions
    telemetrySampleRate: 20,
    trackBackgroundEvents: false   // do not track background events
))

Manual View instrumentation (SwiftUI)

struct ProductListView: View {
    var body: some View {
        List(products) { product in
            ProductRow(product: product)
        }
        .trackRUMView(name: "ProductList")
    }
}

Monitoring a specific network request

// With URLSession + Datadog
let delegate = DDURLSessionDelegate()
let session = URLSession(configuration: .default, delegate: delegate, delegateQueue: nil)

Datadog automatically injects x-datadog-trace-id into every request via this session — no additional interceptors needed.

Typical issues when implementing RUM

A common bug: Views close too early. For example, in UIKit without an explicit stopView call, the agent closes the View on viewWillDisappear, but if the controller presents a bottom sheet over itself, the View closes and reopens, creating a duplicate entry. Solution:

// iOS — explicit View lifecycle management
override func viewDidAppear(_ animated: Bool) {
    super.viewDidAppear(animated)
    RUM.monitor?.startView(viewController: self, name: "ProductDetail")
}

override func viewWillDisappear(_ animated: Bool) {
    super.viewWillDisappear(animated)
    if isMovingFromParent {
        RUM.monitor?.stopView(viewController: self)
    }
}

RUM implementation process

What's included in the work?

• Tool selection based on stack and budget (Datadog / Sentry / Firebase / New Relic)
• SDK integration with optimal sessionSampleRate
• View transition instrumentation (iOS, Android, Flutter)
• HTTP interceptor setup for network resource tracking
• Consent logic configuration for GDPR compliance
• Dashboard construction: p75/p95 View load time, Error Rate, Long Task Rate
• Team training on dashboard usage

Timeline and cost estimation

Basic RUM setup takes 1–2 days. Custom attributes and dashboards add another 1–2 days. On average, the investment pays off within 2–3 months by reducing bug-finding time. The cost is calculated individually based on app complexity and chosen tool. Contact us for a precise assessment. Our team has 7+ years of experience in mobile development. Get a consultation on selecting the right RUM tool.

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.