Product Analytics

Product Analytics

Event Taxonomy Design

Naming Convention: Object-Action

Format: object_action

Examples:
  user_signed_up
  project_created
  file_uploaded
  subscription_upgraded
  report_exported
  onboarding_completed

Event Properties (always include)

{
  "event": "project_created",
  "user_id": "usr_123",
  "timestamp": "2024-01-15T10:30:00Z",
  "session_id": "sess_456",
  "platform": "web",
  "properties": {
    "project_type": "template",
    "team_size": 5,
    "plan": "pro"
  }
}

Instrumentation Checklist

• Every screen/page view tracked
• All CTA clicks tracked
• Form submissions tracked (start + complete)
• Errors tracked with context
• Key milestones tracked (activation, upgrade, churn signal)
• Properties consistent across events (same field names)
• User identity linked (anonymous → identified on signup)

Funnel Analysis

Funnel Construction

1. Define the conversion goal (signup, activation, purchase)
2. Map required steps in order
3. Identify optional vs required steps
4. Set time window (complete within X days)
5. Measure conversion rate at each step

Funnel Metrics

Diagnosing Drop-offs

• Drop > 50% at a step → investigate with session recordings
• Compare drop-off by segment (device, plan, cohort)
• Run qualitative research at high-drop steps
• Test friction reduction (reduce fields, add progress indicator)

Cohort Analysis

Acquisition Cohort

Groups users by when they joined (week/month). Tracks how each cohort behaves over time.

Week 0: 1,000 users signed up
Week 1: 650 returned (65% W1 retention)
Week 2: 450 returned (45% W2 retention)
Week 4: 300 retained (30% W4 retention)
Week 8: 220 retained (22% W8 retention — stabilization point)

Behavioral Cohort

Groups users by action taken (users who completed onboarding vs those who didn't).

Use behavioral cohorts to:

• Validate aha moment hypothesis
• Compare LTV of activated vs non-activated users
• Identify high-value behaviors to promote

Retention Curves

Benchmarks by Product Category

Reading the Curve

• Smile curve: Drops then flattens → healthy core audience
• Declining curve: Never flattens → product has no sticky value
• High D1, steep drop: Strong acquisition but weak activation

Improving Retention

• D1 retention: Improve onboarding and time-to-value
• D7 retention: Establish habit loop, trigger return visit
• D30 retention: Deepen engagement, show accumulated value

DAU / WAU / MAU

Definitions

• DAU: Unique users with at least one qualifying event in a calendar day
• WAU: Unique users active in the past 7 days
• MAU: Unique users active in the past 28–30 days

Engagement Ratios

Qualifying Event Definition

Do not count all events as active. Define a meaningful "active" event:

• Bad: any page view (inflated by bots/accidental visits)
• Good: core product action (created item, sent message, ran query)

Activation Metrics

Finding the Aha Moment

1. Define activation: what behavior correlates with long-term retention?
2. Segment retained vs churned users
3. Identify actions taken in first session that differ between groups
4. Test hypothesis: does promoting that action improve retention?

Activation Metric Examples

Time-to-Activate

Track: median time from signup to activation event. Goal: reduce time-to-activate while maintaining quality of activation.

Product Health Dashboard

Key Metrics (top of dashboard)

Supporting Metrics

• Funnel conversion rates by step
• Feature adoption (% MAU using feature)
• Error rates on key flows
• Support ticket volume by category

Mixpanel vs Amplitude vs PostHog