Mobile App Analytics Basics: What to Track and Why

Most app developers set up Google Analytics, forget about it, and then wonder six months later why users aren't sticking around. Analytics without intention is just data collection. Analytics with purpose drives decisions.

Here's how to actually use analytics instead of just having them.

Start With Questions

Before tracking anything, write down the questions you want to answer. Not vague questions like "how is the app doing?" but specific ones:

• Where do users drop off in onboarding?
• Which features do users actually use?
• What percentage of users come back after day 1? Day 7? Day 30?
• What do retained users do differently from churned users?
• Which acquisition channels bring the highest quality users?

These questions determine what you track. Tracking everything is expensive, clutters your data, and makes analysis harder. Track what answers questions you actually care about.

The Metrics That Actually Matter

Acquisition Metrics

Where do users come from, and how much do they cost?

• Installs by source: Organic search, paid ads, referrals, app store browse
• Cost per install (CPI): For paid channels, what are you spending per user?
• Install to registration rate: How many people who install actually sign up?

Engagement Metrics

Are users actually using the app?

• Daily/Monthly Active Users (DAU/MAU): The fundamental engagement metric. The ratio between them tells you how "sticky" the app is.
• Session length: How long do users spend per visit?
• Sessions per user: How often do they come back?
• Feature usage: Which features get used? Which get ignored?

Retention Metrics

Do users come back?

• Day 1 retention: What percentage of users return the day after installing? This is critical. Low day 1 retention means your first experience is broken.
• Day 7 retention: Are users forming a habit?
• Day 30 retention: Long-term viability. This is where most apps collapse.

Retention curves tell you more than any other metric. Plot the percentage of users who return on each day. Healthy apps flatten out. Unhealthy apps keep dropping toward zero.

Revenue Metrics (If Applicable)

• Conversion rate: What percentage of users pay?
• Average revenue per user (ARPU): Total revenue divided by total users
• Lifetime value (LTV): How much does a user generate over their entire time using the app?
• Payback period: How long until acquisition cost is recovered?

Setting Up Event Tracking

Most analytics platforms work on an event model. You define events that matter and fire them when they happen.

Core Events Everyone Should Track

• app_open: User opens the app
• signup_complete: User finishes registration
• onboarding_step_X: Each step of your onboarding flow
• onboarding_complete: User finishes onboarding
• first_core_action: Whatever the main action in your app is
• purchase_complete: If you sell anything

Event Properties

Events alone aren't enough. Properties give context.

Instead of just tracking "button_click," track "button_click" with properties like:

• button_name: "submit_order"
• screen: "checkout"
• user_type: "premium"
• order_value: 42.99

This lets you answer questions like "what percentage of premium users on the checkout screen click submit?" You can't answer that without properties.

Tools Worth Considering

The analytics market is crowded. Here's a quick rundown:

Firebase Analytics (Google)

Free, well-integrated with other Firebase services, decent for basic needs. Limited customization and querying compared to paid tools.

Amplitude

Strong for product analytics. Great retention and funnel analysis. Free tier is generous. This is what we use for most projects.

Mixpanel

Similar to Amplitude. Slightly different interface and pricing. Both are good choices.

App Store Connect / Google Play Console

Built-in analytics from Apple and Google. Basic but free and covers installs, crashes, and store performance.

Funnels: Your Best Friend

A funnel shows how users progress through a sequence of steps. Onboarding funnels. Purchase funnels. Any multi-step process.

Set up funnels for your critical paths and watch where users drop off. If 80% of users complete step 1 but only 30% complete step 2, that's where you focus improvement efforts.

Funnels should be specific. "All users" is too broad. Segment by user type, acquisition source, or device. Different segments often have very different funnel performance.

Cohort Analysis

Cohorts are groups of users who share something in common, usually their start date.

Comparing cohorts over time tells you if your app is improving. Is this month's cohort retaining better than last month's? If you shipped a new onboarding flow, did retention improve for the cohort that experienced it?

Without cohort analysis, improvements and regressions get hidden in aggregate numbers.

Common Mistakes

Tracking Too Much

Every event has a cost. Storage, processing, and most importantly: cognitive load when analyzing. Track what matters. Ignore what doesn't.

Not Tracking User Properties

Knowing that users completed an action is less useful than knowing which users completed it. Set user properties like account age, subscription tier, acquisition source. These let you segment analysis meaningfully.

Ignoring Data Quality

If your events fire inconsistently or with wrong properties, your analysis will be wrong. Test event tracking like you test features. Validate that events fire when expected with correct data.

Dashboard Blindness

Building a dashboard and checking it every day isn't analytics. It's numerology. Ask questions, dig into data, form hypotheses, and test them. Dashboards are starting points, not endpoints.

Privacy Considerations

GDPR, CCPA, ATT on iOS. Privacy regulations affect what you can track and how.

General rules:

• Don't track personally identifiable information unless necessary
• Respect user opt-outs
• Have a privacy policy that accurately describes your tracking
• Use anonymized or hashed identifiers when possible

Apple's App Tracking Transparency has significantly impacted cross-app tracking. Focus on first-party data within your app rather than trying to track users across the internet.

The Bottom Line

Analytics exist to inform decisions. Set up tracking that answers your actual questions. Review data regularly. Make changes based on what you learn. Measure the results of those changes.

That feedback loop is the entire point. Data without action is just overhead.