Monthly Active Users (MAU)¶

Definition¶

Monthly Active Users (MAU) is the total number of unique users who engage with a product, service, or platform within a given month. Engagement can include logging in, performing key actions, or interacting with specific features, depending on the product’s goals.

Description¶

Monthly Active Users (MAU) is a key indicator of user base engagement and product stickiness, reflecting how many unique users interact with your product in a 30-day period. It’s a foundational health metric that signals how many people are finding consistent value in your offering.

Its application differs across models:

In SaaS, MAU reveals long-term product utility and adoption
In mobile apps, it reflects habitual usage and retention
In marketplaces or social platforms, it measures network vitality

A rising MAU indicates growth and product relevance, while a decline may signal churn, disengagement, or acquisition gaps. By segmenting by acquisition channel, cohort, or plan, you can surface insights to optimize onboarding, content strategy, or product UX.

MAU informs:

Strategic decisions, like feature prioritization and retention investments
Tactical actions, such as improving onboarding flows or running re-engagement campaigns
Operational improvements, including lifecycle automation and user health tracking
Cross-functional alignment, bringing product, growth, and success teams together around activation and engagement

Key Drivers¶

These are the main factors that directly impact the metric. Understanding these lets you know what levers you can pull to improve the outcome

Activation Rate and First-Week Retention: New users who activate quickly tend to return consistently.
Feature Stickiness and Use Case Depth: Products tied to recurring tasks (e.g., reports, collaboration) see higher MAU.
Outreach and Re-Engagement Programs: Lifecycle comms and success plays keep infrequent users coming back.

Improvement Tactics & Quick Wins¶

Actionable ideas to optimize this KPI, from fast, low-effort wins to strategic initiatives that drive measurable impact.

If MAU is dropping, identify which cohort is fading (new users, returning, dormant) and target them specifically.
Add feature usage nudges post-login to increase engagement sessions.
Run a test with a monthly summary email (“Here’s what your team did this month”) and CTA to log in.
Refine onboarding to compress time-to-value — the faster they succeed, the more they return.
Partner with growth and CS to flag MAU dips early and drive usage-based outreach.

Required Datapoints to calculate the metric
- Unique Users: Total unique users who interacted with the product during the month.
- Engagement Criteria: Define what constitutes “active” (e.g., logging in, completing a purchase, or engaging with core features).
- Timeframe: The specific month being measured.
Example to show how the metric is derived

A mobile app calculates MAU for October:
- Unique Users Engaged in October: 50,000
- MAU = 50,000

Formula¶

Formula

\[ \mathrm{MAU\ Growth\ Rate} = \left( \frac{\mathrm{MAU\ This\ Month} - \mathrm{MAU\ Last\ Month}}{\mathrm{MAU\ Last\ Month}} \right) \times 100 \]

Data Model Definition¶

How this KPI is structured in Cube.js, including its key measures, dimensions, and calculation logic for consistent reporting.

UserEngagement

cube('UserEngagement', {
  sql: `SELECT * FROM user_engagements`,

  measures: {
    monthlyActiveUsers: {
      sql: `user_id`,
      type: 'countDistinct',
      title: 'Monthly Active Users',
      description: 'Total number of unique users who engaged with the product within a given month.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true
    },

    userId: {
      sql: `user_id`,
      type: 'string',
      title: 'User ID',
      description: 'Unique identifier for each user.'
    },

    engagementType: {
      sql: `engagement_type`,
      type: 'string',
      title: 'Engagement Type',
      description: 'Type of engagement that qualifies a user as active.'
    },

    engagementTime: {
      sql: `engagement_time`,
      type: 'time',
      title: 'Engagement Time',
      description: 'Timestamp of the user engagement.'
    }
  },

  segments: {
    activeUsers: {
      sql: `${CUBE}.engagementType IN ('login', 'purchase', 'feature_interaction')`,
      title: 'Active Users',
      description: 'Users who have engaged with the product based on defined criteria.'
    }
  }
});

Note: This is a reference implementation and should be used as a starting point. You’ll need to adapt it to match your own data model and schema

Positive & Negative Influences¶

Negative influences

Factors that drive the metric in an undesirable direction, often signaling risk or decline.
- Customer Churn Rate: A high churn rate indicates users are leaving the platform, which directly decreases the number of monthly active users.
- User Friction: Complex or frustrating user experiences can deter users from returning, negatively impacting MAU.
- Competition: Increased competition can draw users away to other platforms, reducing MAU.
- Technical Issues: Frequent bugs or downtime can frustrate users, leading to decreased engagement and lower MAU.
- Lack of New Features: Failure to innovate or add new features can lead to user boredom and attrition, negatively affecting MAU.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Activation Rate: Higher activation rates lead to more users engaging with the product initially, increasing the likelihood of them becoming monthly active users.
- First-Week Retention: Users who return within the first week are more likely to continue using the product, boosting monthly active user numbers.
- Feature Stickiness: Features that are frequently used or essential to the user increase the likelihood of users returning, thus increasing MAU.
- Use Case Depth: Products that fulfill multiple user needs or are integral to daily tasks see higher engagement, positively impacting MAU.
- Outreach and Re-Engagement Programs: Effective communication and re-engagement strategies bring back users who might otherwise become inactive, increasing MAU.

Involved Roles & Activities¶

Involved Roles

These roles are typically responsible for implementing or monitoring this KPI:

Data & Analytics
Marketing
Product Management (PM)
Product Marketing (PMM)
Activities

Common initiatives or actions associated with this KPI:

Product Adoption and Use
Community Building
Activation Nudges

Funnel Stage & Type¶

AAARRR Funnel Stage

This KPI is associated with the following stages in the AAARRR (Pirate Metrics) funnel:

Activation
Type

This KPI is classified as a Lagging Indicator. It reflects the results of past actions or behaviors and is used to validate performance or assess the impact of previous strategies.

Supporting Leading & Lagging Metrics¶

Leading

These leading indicators influence this KPI and act as early signals that forecast future changes in this KPI.
- Daily Active Users: DAU is a core precursor to MAU, capturing the frequency and scale of daily engagement. Rising DAU typically forecasts higher MAU, as more users are active each day and more likely to remain active over the month.
- Weekly Active Users: WAU serves as an intermediate frequency metric between DAU and MAU. Tracking WAU trends helps anticipate MAU shifts, as weekly engagement signals sustained user interest that aggregates into monthly activity.
- Stickiness Ratio: The DAU/MAU stickiness ratio quantifies how habit-forming the product is. Higher stickiness means users return frequently within the same month, increasing the likelihood they are counted as active for MAU.
- Number of Monthly Sign-ups: The volume of new sign-ups is a direct feeder to MAU. More sign-ups increase the potential monthly active user base, particularly if onboarding and activation are effective.
- Onboarding Completion Rate: This measures how well new users are converted into active users. A higher onboarding completion rate means more new users are likely to start engaging and be included in MAU.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Churn Risk Score: High churn risk among existing users indicates potential future drops in MAU. Analyzing churn risk can help recalibrate MAU forecasts and prompt early interventions.
- Activation Rate by Source: This metric reveals which acquisition channels most effectively drive users to activation and, subsequently, active monthly use. It illuminates channel effectiveness and informs adjustments to MAU growth strategy.
- Customer Engagement Score: This aggregates depth and consistency of engagement, helping refine expectations for future MAU by identifying which users or cohorts are likely to stay active month-over-month.
- Trial Sign-Up Rate: The percentage of visitors starting free trials offers a leading indicator of future MAU, especially if trial-to-active conversion rates are high. Tracking this helps improve MAU forecasting.
- Activation Cohort Retention Rate (Day 7/30): Measures the retention of newly activated users after 7 or 30 days, providing evidence of how well initial engagement translates into sustained monthly activity (MAU). It is crucial for refining MAU growth projections.