Data guides product decisions. Without tracking, you browse in the dark. This guide introduces you to how to implement analytics, define meaningful events, and transform data into actionable insights.
Why Track Events
Data-Based Decisions
Evidence trumps opinion. Data shows real behavior.
Understand Users
What they do, where they get stuck, what they ignore.
Measure Impact
Did changes improve or worsen metrics?
Identify Problems
Bugs, friction, abandonment visible in the data.
Types of Events
Page Views
Pages visited. Basic but fundamental.
Clicks
Interactions with elements. CTAs, links, buttons.
Forms
Submissions, errors, abandonment of the field.
Conversions
Value actions: purchase, registration, subscription.
Custom Events
Actions specific to your product.
Event Taxonomy
Consistent Naming
Default: [object]_[action]. Ex: button_clicked, form_submitted.
Properties
Event metadata. button_name, page_location, user_tier.
Hierarchy
Events logically grouped. Navigation, engagement, conversion.
Tracking Plan
What is it
Document that defines all tracked events.
Elements
- Event name
- Description
- Trigger
- Properties
- Data type
- Implementation
Maintenance
Living document. Update with changes.
Analytics Tools
Google Analytics 4
Free, powerful. Event-based.
Mixpanel
Product focused. Funnels, cohorts, retention.
Amplitude
Similar to Mixpanel. Strong in product analytics.
Segment
Customer Data Platform. Centralizes data, distributes it to tools.
PostHog
Open source. Self-hosted possible.
Implementation
Client-Side
JavaScript in the browser, SDK in the app. More common.
Server-Side
Events sent from the backend. More reliable.
###Hybrid
Combination. Client for interactions, server for transactions.
SDKs and Libraries
###Web
gtag.js, Segment analytics.js, Mixpanel SDK.
iOS
Firebase Analytics, Amplitude SDK.
###Android
Firebase Analytics, Mixpanel SDK.
Cross-Platform
Segment, mParticle. One SDK, multiple targets.
Essential Events
Session
Start and end of session.
Authentication
Login, logout, signup.
Navigation
Page views, screen views.
Engagement
Feature usage, content consumption.
Conversion
Purchase, subscription, value action.
Errors
Crashes, validation errors, failures.
User Properties
Identification
User ID for cross-device tracking.
Attributes
Plan, registration date, preferences.
Cohort
Groupings for analysis.
Funnel Analysis
Definition
Sequence of steps until conversion.
Example
Home → Product → Cart → Checkout → Purchase.
Metrics
Conversion by step, drop-off, time.
Optimization
Identify where they lose, optimize.
Cohort Analysis
What is it
Groups users by common characteristic.
Types
Acquisition (registration date), behavior (feature used).
Insights
Compare retention between cohorts. What works?
Retention
Curves
Percentage that returns over time.
Benchmarks
D1, D7, D30 retention. Compare with the market.
Analysis
Where does the curve stabilize? Are there core users?
Privacy and Compliance
Consent
LGPD, GDPR require consent for tracking.
Anonymization
Aggregated data, without personal identification.
Data Retention
How long it keeps data.
Opt-Out
Respect the user's choice.
Data Quality
Validation
Are events correct? Complete properties?
Debugging
Debug tools. Chrome DevTools, SDKs.
Monitoring
Alerts for anomalies in event volume.
Dashboards
Key Metrics
Visible KPIs. Updated in real time.
Segmentation
Filters by period, cohort, platform.
Accessibility
The whole team has access. Democratized data.
Common Errors
Track Everything
Too much data, too little insights. Focus on what's important.
Inconsistent Naming
Each dev invents a name. Chaos.
No Tracking Plan
Ad-hoc implementation. Impossible to maintain.
Ignore Data
Collect without analyzing. Waste.
Conclusion
Events and tracking are the foundation of product analytics. Plan taxonomy, implement consistently, review regularly. Well-collected data reveals what users really do.
##FAQs
1) How many events should I track? Start with essentials: 15-30. Add as needed.
2) Firebase or Mixpanel? Firebase is free and good for apps. Mixpanel has more advanced analytics.
3) Do I need Segment? For multiple analytics tools, it saves work.
4) How to guarantee data quality? Tracking plan, automated tests, monitoring.
5) LGPD affects analytics? Yes. Consent required. Use anonymized mode when applicable.
Also read
- Big Data in Digital Products: Good Practices with Examples
- Data-driven product: the checklist for deciding with data without becoming a hostage to it
- Application Metrics: Validation with Real Cases
- Application Metrics: Validation Quick Guide
- Advanced Analytics - Complete Guide Quick Guide
- Advanced Analytics - Complete Guide Essential Steps