Data

Data — Analytics & Intelligence

Measure what matters. Every metric should drive a decision. If it doesn't, stop tracking it.

Metric Hierarchy

Rules:

• North star is singular. Two north stars = zero north stars.
• Vanity metrics (page views, total signups) are not health metrics.
• Every feature ships with tracking. No "we'll add analytics later."

SQL Patterns

Cohort Retention

SELECT
  DATE_TRUNC('week', u.created_at) AS cohort_week,
  DATE_TRUNC('week', e.created_at) AS activity_week,
  COUNT(DISTINCT e.user_id) AS active_users
FROM users u
JOIN events e ON e.user_id = u.id
GROUP BY 1, 2
ORDER BY 1, 2;

Funnel Analysis

WITH steps AS (
  SELECT user_id,
    MAX(CASE WHEN event = 'signup' THEN 1 END) AS step_1,
    MAX(CASE WHEN event = 'onboard_complete' THEN 1 END) AS step_2,
    MAX(CASE WHEN event = 'first_action' THEN 1 END) AS step_3,
    MAX(CASE WHEN event = 'paid' THEN 1 END) AS step_4
  FROM events
  WHERE created_at > NOW() - INTERVAL '30 days'
  GROUP BY user_id
)
SELECT
  COUNT(*) AS total,
  SUM(step_1) AS signup,
  SUM(step_2) AS onboarded,
  SUM(step_3) AS activated,
  SUM(step_4) AS converted
FROM steps;

Rolling Averages

SELECT
  date,
  value,
  AVG(value) OVER (ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS rolling_7d
FROM daily_metrics;

Event Tracking Design

Every event needs:

Naming convention: noun_verb — form_submitted, file_uploaded, subscription_cancelled

Never: click, event1, trackThis, misc

Dashboard Rules

• One question per dashboard. "How is acquisition?" not "Everything."
• Top-left = most important metric. Eye lands there first.
• Comparison always. This week vs last week. This month vs last month.
• No pie charts. Use bar charts. Humans can't compare angles.
• Annotate changes. "Launched feature X" on the timeline.

Data Pipeline Defaults

Keep it simple. You don't need Kafka until you have 10M events/day.