What Is MAU (Monthly Active Users)? Definition, Calculation, and Benchmarks

“We have 500,000 monthly active users.” It is the most commonly cited growth metric in product and investor conversations. It is also one of the most frequently misunderstood.

Monthly Active Users, or MAU, counts the number of unique users who interact with your product within a 30-day window. It sounds simple. But behind that simplicity lies a metric that can mean almost anything depending on how you define “active,” what you count as an interaction, and whether you are using it to measure growth, engagement, or something else entirely.

Used well, MAU provides a high-level pulse check on whether your product is growing or shrinking. Used poorly, it becomes a vanity metric that masks declining engagement, inflates perceived traction, and leads teams to optimize for the wrong outcomes.

This guide covers what MAU actually measures, how to define it correctly, how it compares to related metrics like DAU and WAU, what constitutes a good MAU number, and why you probably need better metrics alongside it.

What Is MAU (Monthly Active Users)?

MAU stands for Monthly Active Users. It is a count of the unique users who interact with your product at least once within a rolling 30-day period. Each user is counted only once regardless of how many times they return during the month.

The metric originated in the social media and consumer app world, where user growth was the primary measure of success. Facebook, Twitter, and other platforms reported MAU as their headline growth metric for years, and it became the standard way investors and analysts evaluated consumer technology companies. From there, it spread to SaaS, marketplaces, and virtually every product category.

MAU serves as a top-level indicator of product reach. If your MAU is growing, more people are using your product. If it is declining, fewer people are finding enough value to return within a 30-day window. At this level, it is a useful directional signal. The problems begin when teams try to make it do more than this.

How to Define “Active”

The word “active” is doing all the heavy lifting in Monthly Active Users, and most companies define it too loosely. If “active” means any user who loaded a page or triggered a server ping, your MAU count will include users who opened the app by accident, received an automated email that triggered a tracking pixel, or visited once and never returned. These are not active users in any meaningful sense.

A better approach is to define “active” based on an action that indicates the user received value from your product. For different product types, this might mean:

• Project management tool: Created or updated a task (not just logged in and viewed the dashboard).
• Analytics platform: Ran a report or viewed a dashboard with data (not just landed on the homepage).
• E-commerce marketplace: Browsed products or made a purchase (not just visited the landing page).
• Social platform: Posted, commented, or liked content (not just scrolled passively).
• Communication tool: Sent or responded to a message (not just opened the app).

The definition you choose will dramatically change your MAU number. A communication tool that defines active as “opened the app” might report 100,000 MAU. The same tool defining active as “sent a message” might report 45,000. The second number is smaller but infinitely more useful because it measures genuine engagement rather than passive presence.

Document your definition of “active” explicitly and ensure every team uses the same definition. Inconsistency across teams is one of the fastest ways to make MAU meaningless.

MAU vs DAU vs WAU

MAU is part of a family of active user metrics measured at different time intervals:

• DAU (Daily Active Users): Unique users who perform a qualifying action within a single day. Best for products designed for daily use like messaging apps, social platforms, and productivity tools.
• WAU (Weekly Active Users): Unique users active within a 7-day window. Useful for products with weekly usage patterns like reporting tools, team collaboration platforms, and content creation tools.
• MAU (Monthly Active Users): Unique users active within a 30-day window. Appropriate for products with less frequent but regular usage like accounting software, HR tools, and marketplaces.

The right metric depends on your product’s natural usage frequency. Choosing the wrong time window distorts your view. If you track MAU for a daily-use product, the number will look healthy even as daily engagement declines. If you track DAU for a product people use monthly, the number will look terrible even if every user returns reliably.

Track the metric that matches your product’s intended cadence. If you expect daily use, DAU is your primary metric. If weekly, use WAU. If monthly, MAU is appropriate. And regardless of which primary metric you choose, the ratios between them tell an even more valuable story.

The DAU/MAU Ratio (Stickiness)

The DAU/MAU ratio, often called the stickiness ratio, measures what percentage of your monthly users engage with your product on any given day. It is calculated simply:

Stickiness = DAU / MAU x 100

A DAU/MAU ratio of 50% means that on any given day, half of your monthly users are active. A ratio of 10% means that on any given day, only one in ten monthly users shows up. The higher the ratio, the more habitually users engage with your product.

Here is how to interpret the ratio:

• 50%+ (exceptional): Users engage most days. Typical of messaging apps, social platforms, and tools deeply embedded in daily workflows.
• 20-50% (strong): Users engage several times per week. Typical of productivity tools, project management, and collaboration platforms.
• 10-20% (moderate): Users engage a few times per month. May indicate a product that delivers value intermittently or has room for deeper engagement.
• Below 10% (weak): Most monthly users rarely return. This may be appropriate for infrequent-use products like tax software, but for most products it signals an engagement problem.

The stickiness ratio is more diagnostic than MAU alone because it reveals engagement intensity. A product can grow its MAU through aggressive acquisition while its stickiness ratio declines, meaning new users are arriving but existing users are becoming less engaged. This is a pattern that precedes churn acceleration and is invisible if you only watch MAU.

MAU Benchmarks by Product Type

MAU benchmarks vary enormously by product category, making cross-industry comparisons nearly useless. What matters is understanding the norms for your specific type of product.

• Consumer social apps: The largest platforms measure MAU in billions. For startups, growth rate matters more than absolute numbers. Month-over-month growth of 15-20% is strong in early stages.
• B2B SaaS: MAU is less commonly used as a headline metric because the customer base is smaller and contract value matters more. When tracked, healthy B2B products see MAU closely tracking paid seats, with 70-90% of licensed users active monthly.
• Mobile apps: The average app loses 77% of its DAU within the first three days after install. Apps that maintain 25%+ of users as MAU after 90 days are performing well above average.
• Marketplaces: Both supply-side and demand-side MAU matter. Healthy marketplaces see 30-50% of registered users active monthly, with strong repeat transaction rates among active users.

As with all benchmarks, your own trend line is more important than any external comparison. Is your MAU growing, stable, or declining? Is growth coming from new users or improved retention of existing ones? These directional questions matter more than whether your number is above or below some industry average.

Limitations of MAU as a Metric

MAU has significant limitations that make it dangerous as a primary success metric. Understanding these limitations is essential for using the metric responsibly.

It Treats All Users as Equal

A user who logs in once and leaves is counted the same as a power user who engages daily. A free-tier user who generates no revenue is counted the same as an enterprise customer paying $10,000/month. This flattening of variation hides the distribution of engagement and value that you need to understand.

It Says Nothing About Revenue

MAU can grow while revenue declines. If your highest-paying customers are churning but being replaced by free-tier users, MAU stays flat or grows while the business deteriorates. For subscription businesses, product-led growth metrics that tie user activity to revenue provide a much clearer picture.

It Can Grow While Engagement Declines

A product can increase MAU through aggressive acquisition while existing users become less engaged. If you add 10,000 new users per month and each cohort retains poorly, MAU keeps growing even as the underlying engagement engine weakens. The moment acquisition slows, the retention problem becomes visible, but by then significant damage has been done.

The 30-Day Window Is Arbitrary

Why 30 days? For some products, a user who returns once a month is highly engaged. For others, it indicates near-abandonment. The 30-day window became standard through convention, not because it is universally meaningful. Using it without questioning whether it matches your product’s natural usage cadence leads to misleading conclusions.

Better Alternatives to Raw MAU

MAU is not useless. It serves as a reasonable top-level health check. But it should never be your only engagement metric, and in most cases it should not even be your primary one. Here are metrics that provide deeper insight.

Engaged MAU

Instead of counting any user who shows up, count users who perform a core value action. Meta introduced “Family of Apps Daily Active People” partly because raw MAU was becoming less informative. Define your core actions and track how many users complete them monthly. This separates genuine engagement from passive presence.

Activation Rate

Activation rate measures the percentage of new sign-ups who reach a defined “activated” state, typically completing a key action that correlates with long-term retention. Unlike MAU, which counts everyone equally, activation rate focuses on the critical transition from sign-up to engaged user. For a complete framework, see our activation rate optimization guide.

Feature Adoption Rate

Track what percentage of active users adopt specific features. This reveals engagement depth rather than breadth. A user who adopts five features is far more engaged (and far less likely to churn) than one who uses only one. Feature adoption metrics connect directly to retention in ways that MAU cannot.

L7, L14, and L28 Engagement

Instead of a binary active/inactive classification, count how many days each user was active within a 7, 14, or 28-day window. A user who was active 20 out of 28 days is fundamentally different from one who was active 1 out of 28 days, but MAU treats them identically. Frequency distributions reveal the engagement spectrum that a single count obscures.

Revenue Per Active User

Divide monthly revenue by MAU to get ARPU (Average Revenue Per User). This connects activity to economic value and ensures you are not celebrating user growth that does not translate to business growth. If MAU grows 20% but ARPU drops 30%, you are adding users who generate less value. This actionable metrics framework approach keeps engagement tied to outcomes.

Tracking MAU Properly

If you are going to track MAU, do it in a way that maximizes the metric’s usefulness and minimizes the risk of self-deception.

Define “active” based on value delivery. Choose a qualifying action that indicates the user received genuine value from your product. Document this definition explicitly and enforce consistency across all teams and reports.

Segment MAU by cohort. Track MAU for each sign-up cohort separately. This reveals whether your monthly active user count is growing because of new user acquisition or because of improving retention among existing cohorts. The healthiest growth comes from both.

Track alongside retention metrics. MAU alone is not enough. Pair it with the DAU/MAU stickiness ratio, cohort retention curves, and revenue metrics. If MAU is growing but stickiness is declining, you have a problem that MAU alone would never reveal.

Decompose growth into components. Break MAU changes into new users, resurrected users (previously inactive users who returned), and churned users. MAU growth of 5% could mean strong new user acquisition with healthy retention, or it could mean massive acquisition barely outpacing massive churn. The decomposition tells you which story is true and where to focus your efforts.

Key Takeaways