Mobile App Analytics Guide

Analytics transforms user interactions into insights that drive product decisions. Learn to track user behavior, analyze engagement funnels, compare cohorts, and make data-driven decisions that increase retention and revenue.

Analytics answers the critical questions:

• • What are users actually doing in my app?
• • Where are they dropping off?
• • Which features drive engagement?
• • How do different user cohorts behave?
• • What changes increase retention?

Best Practice: Design Your Event Taxonomy First

Before tracking, define your events:

This structure makes analysis easier and ensures you track what matters.

Find Where Users Drop Off

Example: E-commerce purchase funnel

The biggest drop is between browse and add-to-cart. Investigate why.

Compare Groups of Users

Split users by signup date or features used, then compare retention:

• • Users who signed up in January vs February
• • Free users vs paid users
• • Mobile vs web users
• • Users from different countries

Cohort analysis reveals which changes impact long-term retention.

Understand User Types

• Power Users: Highly engaged, use multiple features daily
• Regular Users: Consistent usage, 3-4 days/week
• Casual Users: Sporadic usage, mostly weekends
• Dormant Users: Haven't used in 30+ days

Target different segments with appropriate strategies (reengagement campaigns for dormant users, premium features for power users).

Acquisition: How do users find your app? (App store search, referrals, social, ads)

Activation: What's the "aha moment"? (First login, complete profile, first action)

Retention: What keeps users coming back? (Day 1, Day 7, Day 30 retention rates)

Revenue: Monetization metrics (ARPU, LTV, CAC, conversion rate)

Referral: Viral growth (% of users who refer, viral coefficient)

When launching new features, measure their impact on key metrics:

Feature: Quick Reorder Button
Metrics Before → After:
• Average session: 4 min → 6 min
• Payment conversion: 5% → 7%
• Repeat purchase rate: 12% → 18%
• Feature adoption: 0% → 35%

Impact: +$500K/month additional revenue

Understanding User Journeys: Users often touch multiple channels before converting. Attribution models determine which channel gets credit:

• • Last Touch: Final channel gets 100% credit
• • First Touch: First channel gets 100% credit
• • Linear: All channels share credit equally
• • Time Decay: More recent channels weighted higher
• • Data-Driven: ML models determine credit based on actual behavior

Well-designed event taxonomy enables powerful analysis. Group events by type:

{
  "event": "button_tapped",
  "properties": {
    "button_id": "login_primary",
    "screen": "onboarding",
    "timestamp": "2025-01-20T14:32:15Z"
  }
}

Event Types:
• User Actions: button_tapped, screen_viewed
• Business Events: purchase_completed, item_added
• System Events: app_launched, app_backgrounded

In the iOS 14+ era with App Tracking Transparency (ATT), build analytics with privacy first:

• • Collect only events from users who opted in
• • Use probabilistic modeling for aggregate insights
• • Focus on behavioral patterns, not individual tracking
• • Implement privacy manifests explaining data usage
• • Provide user opt-out controls in settings

• Track user IDs: Understand user behavior across sessions
• Add user properties: Subscription status, country, device
• Track timestamps: Analyze time-based patterns
• Avoid PII: Don't track personally identifiable information
• Test before launching: Ensure events fire correctly
• Review data quality: Check for tracking gaps or duplicates
• Run A/B tests: Measure impact of changes with statistical significance
• Monitor dashboards: Weekly review of key metrics

1. 1. Choose an analytics platform (Firebase, Amplitude, Mixpanel, Logtrics)
2. 2. Design your event taxonomy
3. 3. Implement event tracking in critical flows
4. 4. Set up dashboards for key metrics
5. 5. Analyze funnel drop-off points
6. 6. Identify high-value user segments
7. 7. Run A/B tests to improve engagement

Mobile app analytics provides the insights needed to build products users love. By implementing event tracking, analyzing funnels, comparing cohorts, and segmenting users, you can make data-driven decisions that increase engagement, retention, and revenue.