Published on 2025-06-22T03:40:20Z

What are Monthly Active Users (MAU)? Examples of MAU in Analytics

Monthly Active Users (MAU) is the number of unique users who interact with a digital product or service within a monthly timeframe. It’s a core engagement metric used by analytics teams to assess how frequently users return and engage with features. MAU helps product managers, marketers, and stakeholders measure growth, adoption, and long-term retention. Different analytics platforms like Google Analytics 4 (GA4) and PlainSignal (a cookie-free analytics tool) implement MAU tracking with varying user-identification methods. Understanding MAU trends enables teams to make data-driven decisions on feature development, marketing campaigns, and customer-success strategies.

Illustration of Mau (monthly active users)
Illustration of Mau (monthly active users)

Mau (monthly active users)

The count of unique users who engage with a product in a month, used to gauge growth, engagement, and retention.

Importance of MAU in Analytics

MAU serves as a high-level indicator of product adoption and user engagement over time. Tracking MAU helps teams identify growth patterns, seasonality, and the impact of marketing efforts. Investors and business leaders often rely on MAU to assess product traction and market fit. By comparing MAU to DAU and WAU, analytics teams can gauge user stickiness and the success of retention strategies.

  • Kpi for growth and engagement

    MAU reveals whether users continue to find value in a product month over month, guiding growth initiatives and feature prioritization.

  • Investor and stakeholder reporting

    Investors view MAU as a proxy for market traction and revenue potential, making it a critical metric in funding rounds and quarterly reports.

How MAU is Calculated

Calculating MAU involves counting each unique user at most once in a rolling 30-day window based on their identifier. Active user criteria can include any meaningful interaction—page views, events, logins, or transactions. Different analytics tools rely on cookies, device IDs, or server-side logs to deduplicate users.

  • Formula & unique user definition

    MAU = Number of distinct users who had at least one qualifying interaction in the past 30 days. This uses identifiers like client IDs, user IDs, or device fingerprints.

    • Session-based vs. event-based

      Some implementations count unique sessions, while others count unique events. Clarify which interactions qualify as ‘active’.

    • User identification

      Common methods include first-party cookies, authenticated user IDs, or device fingerprinting to identify unique users.

  • Implementing with PlainSignal

    PlainSignal offers a privacy-focused, cookie-free approach to MAU tracking, minimizing reliance on browser storage.

    • Tracking code setup

      Insert the following snippet into your <head> to initialize PlainSignal:

      <link rel="preconnect" href="//eu.plainsignal.com/" crossorigin /><script defer data-do="yourwebsitedomain.com" data-id="0GQV1xmtzQQ" data-api="//eu.plainsignal.com" src="//cdn.plainsignal.com/plainsignal-min.js"></script>

    • GA4 implementation

      In Google Analytics 4, active users are recorded when a session_start event occurs. Configure GA4 by adding the gtag.js snippet and ensure user_id or client_id is set for consistent tracking.

Best Practices & Challenges

Accurate MAU measurement requires balancing data completeness with user privacy. Consider the effects of cookie deprecation, consent management, and cross-device tracking. Complement MAU with DAU, retention, and churn metrics for a holistic view.

  • Privacy and compliance

    Ensure MAU tracking aligns with GDPR, CCPA, and other privacy regulations by minimizing personal data collection and leveraging consent banners.

  • Handling cookie deprecation

    Compare approaches to maintain MAU accuracy as browsers phase out third-party cookies.

    • PlainSignal approach

      Uses server-side analytics and fingerprinting to track users without relying on cookies.

    • GA4 approach

      Relies primarily on first-party cookies and Google Signals for cross-device and cross-session deduplication.

  • Interpreting mau trends

    Analyze MAU in conjunction with DAU, WAU, retention curves, and churn rates to understand user lifecycle and product health.

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