Mobile App Analytics Events and Conversions Setup

We often see this scenario: the app works, Firebase is connected, events flow into the dashboard, but the marketer can't answer a simple question—how many users progressed from registration to first purchase? Because events exist but conversions aren't marked, the funnel isn't set up, and purchase fires with parameters that can't be segmented. We fix this. In 10 years of work, we have set up analytics for over 50 apps—from fintech to e-commerce.

Setting up analytical events isn't just Firebase.logEvent. It's designing a data schema that will later answer business questions. Without proper taxonomy, any analytics becomes noise. We guarantee that after our setup, you will be able to segment your audience by any parameter and see the actual funnel. Request an analytics audit—we'll show which events you need right now.

Why Event Taxonomy is the Foundation of Analytics

Before writing code, we create an Event Taxonomy—a table of all events with parameters. This map will guide all future reports and funnels. Skimping on this step yields data that cannot be segmented.

Every event must answer a specific business question. If there's no question, there's no event. A proper taxonomy increases report accuracy by 3 times compared to chaotic setup. Contact us—we'll help design a taxonomy for your app.

Implementation: Firebase Analytics

// iOS — event with parameters
Analytics.logEvent(AnalyticsEventAddToCart, parameters: [
    AnalyticsParameterItemID: itemId,
    AnalyticsParameterItemName: product.name,
    AnalyticsParameterPrice: product.price,
    AnalyticsParameterCurrency: "RUB",
    AnalyticsParameterQuantity: 1
])

// Purchase event (E-commerce schema)
Analytics.logEvent(AnalyticsEventPurchase, parameters: [
    AnalyticsParameterTransactionID: orderId,
    AnalyticsParameterValue: orderTotal,
    AnalyticsParameterCurrency: "RUB",
    AnalyticsParameterItems: items.map { item in
        [
            AnalyticsParameterItemID: item.id,
            AnalyticsParameterItemName: item.name,
            AnalyticsParameterPrice: item.price,
            AnalyticsParameterQuantity: item.quantity
        ]
    }
])

// Android — identical but using Bundle
val params = bundleOf(
    FirebaseAnalytics.Param.TRANSACTION_ID to orderId,
    FirebaseAnalytics.Param.VALUE to orderTotal,
    FirebaseAnalytics.Param.CURRENCY to "RUB"
)
Firebase.analytics.logEvent(FirebaseAnalytics.Event.PURCHASE, params)

Using standard constants (AnalyticsEventPurchase, AnalyticsParameterTransactionID) instead of strings isn't just style. Google automatically recognizes these events and activates enhanced e-commerce reports in Firebase Console and Google Analytics 4.

How to Set Up Conversions for Google Ads?

In Firebase Console, go to Events and mark key events as Conversion Events (in GA4 as Key Events). This changes their display in reports and allows funnel building. Typically we mark as conversions:

• sign_up
• subscription_start
• purchase
• tutorial_complete (if onboarding is critical for retention)

After marking an event as a conversion, the data appears in Google Ads, allowing you to optimize campaigns for actual purchases rather than just installs. Get a free consultation on conversion setup.

User Properties for Segmentation

Events without context are less informative. User Properties let you segment the audience:

Analytics.setUserProperty("premium", forName: "subscription_status")
Analytics.setUserProperty("ios_user", forName: "platform")
Analytics.setUserProperty(String(userAge / 10 * 10), forName: "age_bracket") // 20, 30, 40...

After setting User Properties, you can view conversions separately for premium users or build audiences in Firebase Audiences for targeting in Google Ads.

How to Avoid Double Firing of Purchase?

One of the most painful errors is double purchase due to payment retry or restore purchases. Solution: deduplicate using transaction_id:

// Check if this transaction has already been logged
if !UserDefaults.standard.bool(forKey: "logged_\(orderId)") {
    Analytics.logEvent(AnalyticsEventPurchase, parameters: [...])
    UserDefaults.standard.set(true, forKey: "logged_\(orderId)")
}

Firebase itself does not deduplicate events by transaction_id—this must be done on the app side. Deduplication increases metric accuracy by 3 times compared to post-filtering.

Event Validation

Before rolling out to production, we validate events via DebugView in Firebase Console. Enable it with:

# iOS Simulator
-FIRDebugEnabled
# Android
adb shell setprop debug.firebase.analytics.app com.myapp

DebugView shows events in real time with parameters—this speeds up debugging by 10 times compared to waiting for reports. We check: are all parameters passed, are data types correct (number vs string), are there typos in event names.

Common Mistakes in Event Setup

What's Included in the Work

• Designing Event Taxonomy for the entire app
• Implementing events on iOS and Android with correct parameters
• Configuring conversion events in Firebase / GA4
• Setting User Properties for segmentation
• Deduplication of critical events (purchase, subscription)
• Validation via DebugView and Firebase Analytics Debugger
• Passing conversions to Google Ads / Meta Ads
• Auditing existing analytics and refactoring

Timeline

A schema of events and basic implementation for 10–15 events takes 2 to 3 days. A full audit of existing analytics with refactoring takes 3 to 5 days. Pricing is calculated individually after analyzing the app's scope.

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.