Analytics Tracking

An AI skill that designs and implements analytics tracking plans for websites and applications, creating event taxonomies, measurement frameworks, and tracking code that captures user behavior data needed for informed product and marketing decisions.

What Is This?

Overview

This skill creates comprehensive analytics tracking implementations. It designs event taxonomies that name and structure every tracked interaction, defines properties attached to each event, generates tracking code for your analytics platform, and produces documentation for the entire measurement framework. The output ensures that the right data is captured consistently across all touchpoints.

Who Should Use This

Ideal for product managers who need behavioral data for decisions, growth engineers implementing analytics instrumentation, marketing teams tracking campaign performance, and data teams establishing consistent event naming across products.

Why Use It?

Problems It Solves

Most analytics implementations are ad hoc. Developers add tracking events without a naming convention, resulting in inconsistent data that is hard to query. Important user actions go untracked while trivial events create noise. Without a structured tracking plan, teams cannot answer basic questions about user behavior.

Core Highlights

• Event Taxonomy creates a structured naming convention for all tracked events
• Property Definitions specifies what metadata attaches to each event
• Tracking Code generates implementation code for your analytics platform
• Funnel Mapping defines the events that comprise key conversion funnels
• Documentation produces a tracking plan document for team reference

How to Use It?

Basic Usage

Describe your application and key metrics, and receive a complete tracking plan.

// Generated tracking plan implementation
// Event: user_signed_up
analytics.track("user_signed_up", {
  method: "email",        // "email" | "google" | "github"
  referral_source: "blog", // UTM source or direct
  plan_selected: "free",   // "free" | "pro" | "business"
});

// Event: feature_used
analytics.track("feature_used", {
  feature_name: "dark_mode",
  first_use: true,
  context: "settings_page",
});

// Event: checkout_completed
analytics.track("checkout_completed", {
  plan: "pro",
  billing_cycle: "annual",
  revenue: 144.00,
  currency: "USD",
});

Real-World Examples

SaaS Product Analytics Setup

A SaaS team launching their product needed to track user behavior from signup through activation and retention. The skill designed a 45-event tracking plan covering the full user lifecycle, implemented it in their Next.js application, and created a dashboard specification showing the key metrics each event supported.

total_events: 45
categories:
  acquisition:
    events: ["page_viewed", "cta_clicked", "user_signed_up"]
    purpose: "Measure marketing funnel performance"
  activation:
    events: ["onboarding_started", "first_project_created", "team_invited"]
    purpose: "Track new user activation milestones"
  engagement:
    events: ["feature_used", "search_performed", "export_generated"]
    purpose: "Understand product usage patterns"
  revenue:
    events: ["checkout_started", "checkout_completed", "plan_upgraded"]
    purpose: "Track revenue and conversion metrics"
  retention:
    events: ["session_started", "notification_clicked", "feedback_submitted"]
    purpose: "Monitor user return patterns"

Advanced Tips

Establish a naming convention early and enforce it through code review. Use a tracking plan spreadsheet as the single source of truth that both engineers and analysts reference. Validate tracking in staging environments before deploying to production.

When to Use It?

Use Cases

• New Product Launch set up analytics from day one with proper structure
• Tracking Audit review and fix existing analytics implementations
• Feature Measurement add tracking for new features being released
• Campaign Tracking implement UTM and conversion tracking for marketing
• Data Migration restructure events when switching analytics platforms

Related Topics

When implementing analytics tracking, these prompts activate the skill:

• "Design an analytics tracking plan for my app"
• "Set up event tracking for user behavior"
• "Create a measurement framework for our product"
• "Generate tracking code for our key funnels"

Important Notes

Requirements

• Application architecture information for appropriate tracking code generation
• Analytics platform selection like Mixpanel, Amplitude, or Google Analytics
• Key business metrics to ensure the right events are tracked
• Works with any web or mobile application framework

Usage Recommendations

Do:

• Follow a consistent naming convention across all events and properties
• Document every event in a shared tracking plan spreadsheet
• Validate tracking in staging before deploying to production
• Review tracking quarterly to add new events and retire unused ones

Don't:

• Track everything as too many events create noise and slow your application
• Use inconsistent naming since "signup" and "user_signed_up" create data silos
• Skip property definitions as events without context are hard to analyze
• Deploy tracking without testing as broken events produce misleading data

Limitations

• Cannot guarantee data accuracy without proper implementation validation
• Ad blockers and privacy settings may prevent some events from being captured
• Cross-device tracking requires additional identity resolution setup
• Historical data cannot be backfilled after fixing tracking implementation gaps