How to Calculate DAU and MAU (Daily and Monthly Active Users)

Daily Active Users (DAU) and Monthly Active Users (MAU) measure how many unique users engage with your product within a given time window. These are foundational engagement metrics for any product-led business - indicating whether users find ongoing value in your product.

DAU answers: How many users engaged today? MAU answers: How many users engaged this month? Together, they reveal engagement intensity and product stickiness.

Basic Formulas

Daily Active Users (DAU)

DAU = Count of unique users who performed a qualifying action on a given day

Monthly Active Users (MAU)

MAU = Count of unique users who performed a qualifying action within a 28 or 30-day window

DAU/MAU Ratio (Stickiness)

DAU/MAU Ratio = DAU / MAU × 100%

Step-by-Step Calculation

Step 1: Define "Active"

This is the most critical step. "Active" should mean meaningful engagement, not just presence.

Weak definitions (avoid):

• Logged in
• Opened the app
• Visited any page

Strong definitions (use):

• Completed a core action (sent a message, created a document, made a purchase)
• Engaged with primary feature
• Performed value-generating activity

Step 2: Define the User

Clarify what constitutes a unique user:

• By user ID (logged-in users)
• By device ID (if tracking anonymous users)
• By account (if one account has multiple users)

Step 3: Set the Time Window

For DAU:

• Calendar day (midnight to midnight)
• Define timezone (UTC recommended for consistency)

For MAU:

• Calendar month (1st to last day), or
• Rolling 28 days (more consistent, avoids month-length variation)

Step 4: Count Unique Users

Count distinct users who met the activity criteria within the time window.

DAU = COUNT(DISTINCT user_id) WHERE activity_date = target_date AND action IN (qualifying_actions)

Example Calculations

DAU Example

On January 15th:

• 5,000 unique users logged in
• 3,500 unique users completed a core action

Weak DAU (login-based): 5,000 Strong DAU (action-based): 3,500

The action-based DAU is more meaningful - it measures real engagement.

MAU Example

In January:

• 50,000 unique users logged in at least once
• 35,000 unique users completed at least one core action

Weak MAU: 50,000 Strong MAU: 35,000

DAU/MAU Ratio Example

• DAU (average for January): 3,500
• MAU (January): 35,000

DAU/MAU = 3,500 / 35,000 × 100% = 10%

This means 10% of monthly users engage on any given day - typical for B2B SaaS products.

DAU/MAU Benchmarks

Product Type	Typical DAU/MAU
Social Media	50-65%
Messaging Apps	40-60%
Consumer Apps	20-40%
B2B SaaS	10-30%
E-commerce	5-15%

Higher DAU/MAU indicates more habitual usage. Lower isn't necessarily bad - some products (tax software, travel booking) are used infrequently by design.

Advanced Variations

Qualified Active Users

Add quality filters:

Qualified DAU = Active users who also meet engagement threshold

Example: Users who sent 3+ messages, not just 1.

Active User Segments

Break down by user attributes:

Segment	DAU	MAU	DAU/MAU
Free Users	2,000	25,000	8%
Paid Users	1,500	10,000	15%
Enterprise	800	5,000	16%

Cohort-Based Activity

Track DAU/MAU for acquisition cohorts to see engagement trends:

Cohort	Month 1 DAU/MAU	Month 6 DAU/MAU
Jan Cohort	25%	12%
Apr Cohort	28%	15%

Common DAU/MAU Mistakes

Mistake 1: Weak Activity Definition

Counting logins or page views inflates DAU/MAU without measuring real engagement. Define activity that correlates with value.

Mistake 2: Bot Traffic

Automated accounts and bots inflate user counts. Filter them out using behavioral signals or explicit bot identification.

Mistake 3: Duplicate Counting

Users with multiple devices or accounts can be double-counted. Use a consistent user identifier and deduplication logic.

Mistake 4: Timezone Inconsistency

Inconsistent timezone handling creates daily fluctuations. Use UTC or a single consistent timezone.

Mistake 5: Comparing Across Definitions

DAU based on logins cannot be compared to DAU based on transactions. Keep definitions consistent over time.

Using DAU/MAU for Decisions

Product Health

• Rising DAU/MAU: Engagement improving
• Falling DAU/MAU: Users becoming less habitual
• Stable DAU/MAU: Consistent engagement pattern

Feature Impact

Compare DAU before and after feature launches. Did the new feature increase daily engagement?

Growth Quality

DAU and MAU growing together: Healthy growth MAU growing, DAU flat: Acquiring low-engagement users DAU growing, MAU flat: Existing users more engaged

Retention Proxy

High DAU/MAU correlates with better retention. Users who engage daily are less likely to churn.

DAU/MAU in Context-Aware Analytics

metric:
  name: DAU
  description: Daily Active Users - users completing core action
  calculation: COUNT(DISTINCT user_id) WHERE action IN ('send_message', 'create_document', 'complete_transaction')
  time_grain: daily
  timezone: UTC
  excludes:
    - internal_users
    - bot_accounts
    - test_accounts
  dimensions: [platform, user_segment, region]
  owner: product_analytics

metric:
  name: MAU
  description: Monthly Active Users - rolling 28-day unique actives
  calculation: COUNT(DISTINCT user_id) over rolling 28 days
  activity_definition: Same as DAU
  time_grain: daily (rolling window)
  owner: product_analytics

metric:
  name: DAU/MAU Ratio
  description: Stickiness - percentage of monthly users active daily
  calculation: DAU / MAU * 100
  depends_on: [DAU, MAU]
  owner: product_analytics

With explicit activity definitions, DAU and MAU are consistent - ensuring product and marketing teams measure engagement the same way.