Introduction

Without analytics, you’re building in the dark. You don’t know if features get used, where users struggle, or whether changes help or hurt. Analytics provides visibility into how your app actually performs with real users.

This guide covers the fundamentals of mobile app analytics—what to measure, how to implement it, and how to use data effectively.

Why Analytics Matters

Inform Decisions

Analytics answers questions like:

  • Which features do users actually use?
  • Where do users drop off in key flows?
  • How long do users spend in the app?
  • What brings users back?
  • Is this change improving things?

Data beats opinions in product decisions.

Identify Problems

Analytics reveals issues:

  • Crashes and errors
  • Performance problems
  • UX friction points
  • Feature failures
  • Conversion blockers

Find problems before they drive users away.

Measure Success

Define and track success:

  • Key business metrics
  • User engagement
  • Growth indicators
  • Feature adoption

Know if you’re moving in the right direction.

Essential Metrics

Acqui

Essential Metrics Infographic sition Metrics

How users find and install your app:

Downloads/Installs

  • Total installs
  • By source (organic, paid, referral)
  • By campaign

Install Rate

  • App store visitors who install
  • Conversion from marketing

Activation Metrics

Do new users actually start using the app?

Activation Rate

  • Users who complete key initial action
  • First meaningful engagement
  • Onboarding completion

Time to Activate

  • How long until first value
  • Faster is usually better

Engagement Metrics

How users interact with your app:

Daily Active Users (DAU)

  • Unique users per day
  • Foundation metric

Monthly Active Users (MAU)

  • Unique users per month
  • Broader engagement view

DAU/MAU Ratio (“Stickiness”)

  • How often monthly users return daily
  • Higher = more engaging

Session Metrics

  • Sessions per user
  • Session length
  • Session frequency

Feature Usage

  • Which features get used
  • Frequency of use
  • Feature adoption rate

Retention Metrics

Do users come back?

Retention Rate

  • Day 1: Users returning next day
  • Day 7: Users returning after week
  • Day 30: Users returning after month

Cohort Retention

  • Retention by signup cohort
  • Shows trends over time

Revenue Metrics (if applicable)

For monetised apps:

Revenue Per User (ARPU)

  • Total revenue ÷ total users

Lifetime Value (LTV)

  • Total value from a user over their lifetime

Conversion Rate

  • Free to paid conversion
  • Purchase completion rate

Analytics Tools

Analytics Tools Infographic tions

Firebase Analytics (Google)

Pros:

  • Free for most use cases
  • Integrated with Google ecosystem
  • Crash reporting included
  • Real-time data

Cons:

  • Google data practices
  • Less flexibility in some areas

Amplitude

Pros:

  • Powerful event analytics
  • Good visualisation
  • Behavioural cohorts
  • Product focus

Cons:

  • Pricing at scale
  • Learning curve

Mixpanel

Pros:

  • Event-based analytics
  • Strong retention analysis
  • User journeys

Cons:

  • Pricing can escalate
  • Complexity

App Store Analytics

Pros:

  • Source of truth for downloads
  • App Store specific data
  • Free

Cons:

  • Limited behavioural data
  • No cross-platform

Choosing a Tool

Consider:

  • Budget (many have free tiers)
  • Features needed
  • Team capability
  • Integration requirements
  • Data privacy needs

For many apps, Firebase Analytics is a good starting point.

Implementing Analytics

Event Tracking

Track meaningful actions:

What to Track

  • Key user actions
  • Conversion steps
  • Feature usage
  • Errors and failures
  • Content engagement

Event Naming

Consistent, clear naming:

// Good
button_click
purchase_completed
onboarding_step_completed

// Avoid
btnClk
purchase
step2done

Event Properties

Additional context:

analytics.track('purchase_completed', {
    amount: 29.99,
    currency: 'AUD',
    product_id: 'premium_monthly',
    payment_method: 'credit_card'
});

Screen Tracking

Track which screens users view:

  • Automatic screen tracking (if available)
  • Manual tracking for custom flows
  • Screen names should be meaningful

User Properties

Attributes of users:

  • Subscription status
  • User type
  • Account age
  • Platform/device

Enable segmentation of analytics.

Implementation Tips

Start Simple

  • Track key events first
  • Add more as needed
  • Don’t track everything initially

Be Consistent

  • Naming conventions
  • Property formats
  • Documentation

Test Your Tracking

  • Verify events fire correctly
  • Check properties are accurate
  • Debug mode in analytics tools

Using Analytics Data

Building Dashboards

Create views for different needs:

Executive Dashboard

  • High-level KPIs
  • Trends over time
  • Business metrics

Product Dashboard

  • Feature usage
  • User flows
  • Engagement metrics

Technical Dashboard

  • Crashes
  • Performance
  • Errors

Regular Review

Schedule analytics review:

Weekly

  • Key metrics check
  • Unusual patterns
  • Recent changes impact

Monthly

  • Trend analysis
  • Cohort review
  • Deeper investigation

Quarterly

  • Strategic review
  • Goal assessment
  • Long-term trends

Asking Good Questions

Analytics answers questions:

Usage Questions

  • What percentage of users use feature X?
  • How has usage changed this month?
  • Which features do power users prefer?

Funnel Questions

  • Where do users drop off in onboarding?
  • What’s the conversion rate at each step?
  • What improves conversion?

Retention Questions

  • Are users coming back?
  • What actions correlate with retention?
  • When do users typically churn?

A/B Testing

Use analytics for experiments:

  1. Form hypothesis
  2. Create variants
  3. Measure results
  4. Make decision based on data

Many analytics tools support A/B testing integration.

Privacy Considerations

Regulations

Be aware of:

  • GDPR (EU users)
  • CCPA (California users)
  • Australian Privacy Act
  • App store requirements

Best Practices

Consent

  • Get proper consent
  • Explain data collection
  • Allow opt-out

Minimise Data

  • Collect what you need
  • Avoid sensitive data
  • Anonymise where possible

Transparency

  • Clear privacy policy
  • Explain analytics use
  • Accessible controls

Technical Implementation

  • Analytics consent flow
  • Respect opt-out preferences
  • Secure data handling
  • Regular compliance review

Common Mistakes

Tracking Everything

Problem: Overwhelming data, no insights.

Solution: Focus on meaningful events. You can add more later.

Vanity Metrics

Problem: Tracking numbers that feel good but don’t matter.

Solution: Focus on metrics that inform decisions.

Ignoring Data

Problem: Collecting data but not using it.

Solution: Regular review cadence, decision-making process.

No Baseline

Problem: No comparison point for changes.

Solution: Establish baselines before making changes.

Over-Reliance

Problem: Data alone makes decisions.

Solution: Combine with qualitative feedback, judgment.

Getting Started

Week 1: Foundation

  1. Choose analytics tool
  2. Implement basic SDK
  3. Enable automatic tracking
  4. Test implementation

Week 2-3: Core Events

  1. Identify key events
  2. Implement tracking
  3. Add relevant properties
  4. Verify accuracy

Week 4: Dashboards

  1. Create key dashboards
  2. Set up regular reports
  3. Share with team
  4. Begin review routine

Ongoing

  • Add events as features develop
  • Refine tracking based on questions
  • Regular review and action
  • Continuous improvement

Conclusion

Analytics transforms app development from guessing to knowing. Start with essential metrics. Implement tracking thoughtfully. Review data regularly. Make decisions based on evidence.

You don’t need perfect analytics from day one. Start simple, learn what questions you need answered, and expand your tracking to answer those questions.

The goal isn’t data for its own sake—it’s understanding that leads to better decisions and better apps.