Why Analytics Matter

If you aren't tracking it, you can't improve it. Mobile analytics provide the "Why" behind the "What." You might know you have 1,000 downloads, but without analytics, you won't know that 800 of them uninstall the app after the tutorial because users can't find the "Skip" button.

Data-driven development moves you away from guessing and towards iterating based on actual user behavior.

Key Metrics to Track

While every app is different, these core metrics are universal:

  • Retention Rate: The percentage of users who return to the app after Day 1, Day 7, and Day 30. This is the best indicator of product-market fit.
  • DAU/MAU Ratio: Daily Active Users divided by Monthly Active Users. This measures "stickiness." A ratio of 20% is good; Facebook is near 50%.
  • Session Length: How much time users spend in the app per visit.
  • CLV (Customer Lifetime Value): How much revenue a single user generates over their life.
  • CAC (Customer Acquisition Cost): How much you spend to get a user. (Aim for CLV > 3x CAC).

Analytics Tools

Choosing the right stack depends on your budget and needs:

  • Google Analytics for Firebase: Free, unlimited, and integrates deep with Google Ads. Best starting point.
  • Mixpanel / Amplitude: Focus heavily on event-based analytics and complex user funnels. Great for Product Managers.
  • AppsFlyer / Adjust: Essential for "Attribution" (knowing exactly which ad campaign brought a user in). Critical for paid marketing.

Event Tracking Setup

Don't just track "Screens Views." Track meaningful user Events along with Parameters.

Bad: Tracking "Purchase Button Clicked".

Good: Tracking "Purchase Completed" with parameters { item_id: 'sku_123', value: 9.99, currency: 'USD' } . this allows you to calculate total revenue.

Start with a "Tracking Plan" spreadsheet to define your events before writing code to ensure consistency across iOS and Android.

Cohort Analysis

A cohort is a group of users who share a characteristic (usually the sign-up week). Cohort analysis lets you see if your product is improving over time.

Example: Did users who signed up in December retain better than users who signed up in November? If you released a new onboarding flow in December, this chart tells you if it worked.

Funnel Optimization

A funnel represents a series of steps a user takes to complete a goal.
Example: Install -> Sign Up -> Add to Cart -> Checkout.

Analytics will show you the conversion rate between each step. If 90% of users sign up, but only 5% add to cart, you have a "Top of Funnel" problem. Maybe the catalog is empty or navigation is confusing. Fix the leaky bucket here.

Privacy Compliance

In the era of GDPR (Europe) and CCPA (California), you must be careful.

  • Consent: You must ask for permission to track users (especially for advertising IDs like IDFA on iOS 14+).
  • Anonymize IP: Enable IP anonymization in your tools.
  • Data Deletion: You must have a way to delete all data associated with a user if they request it.

Respecting privacy actually builds trust with users.