Mobile App Analytics: Which Events to Track and Why

Many apps collect plenty of data and still cannot answer basic questions: Where do new users get stuck? Which feature brings people back? Did the last release help? The problem is rarely too little data. It is unplanned data. Events were added randomly, named inconsistently and never connected to decisions.

This guide shows how to design an analytics plan that answers real questions.

Start with questions, not events

Write down the decisions you need to make. Examples:

• Are people completing onboarding? Where do they drop off?
• Which acquisition channels bring users who stay?
• Do users who use feature X retain better than those who do not?
• Where do payments fail?

Every event you track should help answer one of these questions. If it does not, skip it.

Understand the building blocks

• Event: something that happens (signup_completed, message_sent)
• Event properties: details about the event (method: "email", plan: "pro")
• User properties: attributes of the person (country, signup_date, plan)
• Sessions: groups of activity within a time window
• Funnels: an ordered sequence of events, showing drop-off
• Cohorts: groups of users who share a characteristic, usually their start date

The core event set

Most apps benefit from tracking events across the user journey.

Acquisition and install

• App opened for the first time
• Attribution source (campaign, store search, referral), where available and consented

Onboarding and activation

• Onboarding started, each step viewed, completed or skipped
• Account created (with method)
• Permission prompts shown and answered. See onboarding best practices.
• Activation event: the first time the user reaches the “aha moment” in your product

Engagement

• Key feature used (one event per core action: message sent, workout logged, item added)
• Content viewed, searched or shared
• Notification received, opened (see push notification practices)

Monetisation

• Paywall viewed
• Purchase started, completed or failed (with reason)
• Subscription started, renewed or cancelled. See monetisation models.

Retention and churn

• Return visits (derived from sessions)
• Account deletion or logout
• Uninstalls are measured by the stores and some platforms rather than events inside the app

Quality and errors

• Crashes and non-fatal errors (use a dedicated crash reporting tool)
• Slow screens or failed requests
• Search with no results

Name events consistently

A naming convention saves enormous pain later.

• Pick one style, such as snake_case with object_action: invoice_created, profile_updated
• Use past tense for completed actions
• Avoid duplicating events with slightly different names
• Keep event names stable, and add detail through properties instead of new events
• Document every event in a tracking plan (a shared spreadsheet or file) with name, description, properties and the question it answers

Choose useful properties

Properties give events context, but too many create clutter.

• Include properties you will actually segment or filter by, such as plan, source, platform and content type
• Use consistent types and allowed values
• Avoid putting free text, emails or other personal data into properties

Respect privacy and compliance

Analytics involves personal data, so build privacy in.

• Collect only what you need
• Do not send sensitive information (health, financial, passwords) to analytics tools
• Be transparent in your privacy policy and in the app stores’ data disclosures
• Obtain consent where required, such as for tracking across apps on iOS or under GDPR
• Honour deletion requests
• Prefer anonymous or pseudonymous IDs

Build the views that matter

With a good tracking plan you can build:

• Onboarding funnel: where do people leave?
• Activation rate: share of new users reaching the aha moment
• Retention curves: day 1, 7 and 30 by cohort. See improving retention.
• Feature adoption: who uses what, and does it correlate with retention?
• Revenue metrics: conversion to paid, average revenue per user and churn

Review them on a regular rhythm, such as weekly, and connect each review to an action.

Test your tracking

• Verify events in a debug view before release
• Test edge cases like offline use and app restarts
• Check for duplicate events
• Compare numbers with your backend or store data to catch discrepancies
• Add tracking checks to your QA process. See how to test a mobile app.

Common mistakes

• Tracking everything “just in case”
• Inconsistent naming
• No documentation
• Missing the activation event
• Ignoring privacy rules
• Looking at vanity metrics such as total downloads only

Checklist

• Key questions written down
• Tracking plan documented
• Core events across acquisition, activation, engagement, revenue and errors
• Consistent naming and typed properties
• Privacy and consent handled
• Funnels, cohorts and dashboards set up
• Tracking verified before every release

Final thoughts

Analytics is only valuable when it informs decisions. Plan from questions, keep naming tidy and review the numbers regularly. For help designing tracking into a new app, see our app development service or get in touch.