Daily Active Users (DAU)

Overview

Daily Active Users (DAU) measures the number of unique users who actively engage with your product or service within a 24-hour period. It's a critical metric for understanding user engagement, product stickiness, and overall product health.

DAU provides insights into how frequently users return to your product and the level of engagement you're achieving. When combined with MAU, it forms the foundation for calculating user stickiness and understanding long-term user behavior patterns.

Why It Matters

• Product Health: Indicates overall product engagement and user satisfaction
• User Retention: Shows how frequently users return to your product
• Feature Impact: Measures impact of new features on daily engagement
• Revenue Correlation: Often correlates with revenue for usage-based products
• Growth Tracking: Key metric for tracking sustainable growth
• Investor Interest: Critical metric for fundraising and valuation

How to Measure It

Track DAU by counting unique users who perform qualifying activities each day, with proper handling of time zones and activity definitions.

Basic DAU Calculation

-- Calculate Daily Active Users
WITH daily_activity AS (
  SELECT 
    DATE_TRUNC('day', event_timestamp) as activity_date,
    user_id,
    COUNT(*) as events,
    COUNT(DISTINCT session_id) as sessions
  FROM user_events
  WHERE event_timestamp >= CURRENT_DATE - INTERVAL '30 days'
    AND event_name IN ('page_view', 'feature_used', 'action_taken')  -- Define qualifying activities
  GROUP BY 1, 2
)
SELECT 
  activity_date,
  COUNT(DISTINCT user_id) as daily_active_users,
  SUM(events) as total_events,
  SUM(sessions) as total_sessions,
  ROUND(SUM(events)::FLOAT / COUNT(DISTINCT user_id), 2) as avg_events_per_user,
  ROUND(SUM(sessions)::FLOAT / COUNT(DISTINCT user_id), 2) as avg_sessions_per_user
FROM daily_activity
GROUP BY 1
ORDER BY activity_date DESC;

DAU vs MAU Stickiness Analysis

-- Calculate stickiness ratio (DAU/MAU)
WITH daily_users AS (
  SELECT 
    DATE_TRUNC('day', event_timestamp) as date,
    COUNT(DISTINCT user_id) as dau
  FROM user_events
  WHERE event_timestamp >= CURRENT_DATE - INTERVAL '60 days'
  GROUP BY 1
),
monthly_users AS (
  SELECT 
    date,
    COUNT(DISTINCT user_id) OVER (
      ORDER BY date 
      ROWS BETWEEN 29 PRECEDING AND CURRENT ROW
    ) as mau_30_day
  FROM (
    SELECT DISTINCT 
      DATE_TRUNC('day', event_timestamp) as date,
      user_id
    FROM user_events
    WHERE event_timestamp >= CURRENT_DATE - INTERVAL '60 days'
  ) unique_daily_users
)
SELECT 
  d.date,
  d.dau,
  m.mau_30_day as mau,
  ROUND(100.0 * d.dau / NULLIF(m.mau_30_day, 0), 2) as stickiness_ratio,
  ROUND(d.dau - LAG(d.dau) OVER (ORDER BY d.date), 2) as dau_change,
  ROUND(100.0 * (d.dau - LAG(d.dau) OVER (ORDER BY d.date)) / NULLIF(LAG(d.dau) OVER (ORDER BY d.date), 0), 2) as dau_growth_rate
FROM daily_users d
JOIN monthly_users m ON d.date = m.date
WHERE d.date >= CURRENT_DATE - INTERVAL '30 days'
ORDER BY d.date DESC;

Cohort-Based DAU Analysis

-- Analyze DAU by user acquisition cohort
WITH user_cohorts AS (
  SELECT 
    user_id,
    DATE_TRUNC('month', MIN(event_timestamp)) as cohort_month
  FROM user_events
  GROUP BY 1
),
daily_cohort_activity AS (
  SELECT 
    DATE_TRUNC('day', e.event_timestamp) as activity_date,
    c.cohort_month,
    COUNT(DISTINCT e.user_id) as active_users,
    COUNT(DISTINCT c.user_id) as cohort_size
  FROM user_events e
  JOIN user_cohorts c ON e.user_id = c.user_id
  WHERE e.event_timestamp >= CURRENT_DATE - INTERVAL '90 days'
  GROUP BY 1, 2
)
SELECT 
  activity_date,
  cohort_month,
  active_users,
  cohort_size,
  ROUND(100.0 * active_users / cohort_size, 2) as cohort_activity_rate,
  EXTRACT(days FROM AGE(activity_date, cohort_month)) as days_since_acquisition
FROM daily_cohort_activity
WHERE cohort_month >= CURRENT_DATE - INTERVAL '12 months'
ORDER BY activity_date DESC, cohort_month;

Best Practices

1. Defining Active Users

• Use meaningful actions beyond just logins
• Align with core product value delivery
• Exclude automated/bot activity
• Consider different definitions for different user types

2. Time Zone Considerations

• Use consistent timezone for DAU calculations
• Consider business timezone vs. user timezone
• Account for global user base distribution
• Track local DAU for regional analysis

3. Segmentation Strategies

• By user acquisition channel
• By user role or subscription tier
• By geographic region
• By device type (web, mobile, desktop)

4. Growth Strategies

• Onboarding: Drive users to first value quickly
• Habit Formation: Create daily use cases and triggers
• Feature Development: Build features that encourage daily usage
• Notifications: Use thoughtful push/email to drive re-engagement

5. Monitoring and Alerts

• Set up alerts for significant DAU drops
• Monitor weekend vs. weekday patterns
• Track seasonal trends and adjust expectations
• Correlate DAU changes with product releases

Related Metrics

• Monthly Active Users (MAU)
• User Stickiness Ratio
• Average Session Duration
• Customer Retention Rate
• Feature Adoption Rate
• User Engagement Score