Monthly Active Users
Monthly active users (MAU) counts the number of unique users who engage with your product within a 30-day window. It is the most widely used measure of product traction in SaaS and one of the first metrics investors, board members, and operators ask about. If 50,000 unique users logged in and performed at least one meaningful action in your product over the past month, your MAU is 50,000.
The definition of "active" is where MAU gets interesting and where most companies get it wrong. Simply opening the app or logging in is a low bar that inflates the number without reflecting real engagement. The best MAU definitions tie "active" to a core action that represents genuine value exchange: sending a message, creating a report, running a query, or completing a workflow. The stricter your definition of "active," the more honest your MAU number is and the more useful it becomes as a health indicator.
MAU is a stock metric, not a flow metric. It tells you how many users are engaged right now. It does not tell you whether that number is growing because of new user acquisition, improving retention, or both. And it does not tell you whether those users are deeply engaged or barely touching the product. Used in isolation, MAU is a vanity metric. Used alongside daily active users (DAU), retention cohorts, and feature adoption data, it becomes the foundation of a product health dashboard.
Why it matters for SaaS
For PLG companies, MAU is the top-of-funnel metric for the entire business model. Product-led growth works by acquiring users into a free or trial experience and converting them to paid. The size and growth rate of the active user base directly determines how many potential conversions the funnel can produce. A PLG company with stagnant MAU has a stagnant revenue ceiling, regardless of how good its conversion rate is.
MAU also serves as a leading indicator for revenue in usage-based pricing models, which have become increasingly common. Companies like Datadog, Twilio, and Snowflake price based on consumption, and their MAU trend directly predicts revenue trends. Even for seat-based SaaS, MAU indicates how many users within an account are engaged, which correlates with expansion likelihood. An account with 50 licensed seats and 45 monthly active users is far healthier than one with 50 seats and 12 active users.
The DAU-to-MAU ratio, sometimes called the "stickiness ratio," adds another layer of insight. A product with 100,000 MAU and 30,000 DAU has a stickiness ratio of 30%, meaning the average user engages about 9 days per month. Social and communication products aim for ratios above 50%. Productivity tools typically range from 20-40%. A declining stickiness ratio with flat MAU means users are visiting less frequently, which is often a precursor to retention degradation.
How it works in practice
A project management SaaS tracks MAU defined as "users who created, completed, or commented on at least one task." They have 80,000 MAU. Digging into the data, they find three distinct segments: 15,000 power users (active 20+ days per month), 30,000 regular users (active 5-19 days), and 35,000 casual users (active 1-4 days). The casual segment is the largest, and its 30-day retention rate is only 40%, meaning most casual users do not come back the following month.
This segmentation reveals that the headline MAU number is masking a churn problem in the casual tier. The company investigates and finds that casual users are primarily team members invited by a power user who never completed onboarding. They are "active" only because they clicked a notification link once. The fix is not more acquisition. It is better activation: helping invited users complete their first meaningful action so they progress from casual to regular engagement.
Cohort-based MAU analysis adds temporal depth. Plotting MAU retention by signup month shows whether newer cohorts retain better or worse than older ones. If the January cohort retains 60% at month three but the April cohort retains only 45%, something changed, maybe a product regression, a shift in acquisition channels, or a change in onboarding. Without cohort analysis, these shifts are invisible in the aggregate MAU number.
Monthly Active Users vs Daily Active Users
MAU and DAU are the same concept at different time scales, but they answer different questions. DAU measures daily engagement: is the product part of the user's daily workflow? MAU measures monthly reach: how many users engage at all? The relationship between the two is often more informative than either metric alone.
A high DAU-to-MAU ratio indicates a habit-forming product. Users come back frequently, which suggests strong retention and high switching costs. A low ratio indicates a product used episodically, which is not inherently bad but means the product must deliver high value per session to justify continued usage. A payroll product used twice a month can have low DAU-to-MAU but excellent retention because it delivers essential value on its own cadence.
The practical trap is optimizing for the wrong metric. Boosting DAU through notification spam or engagement tricks inflates the number without increasing real value delivery, and users eventually tune out or churn. Sustainable DAU growth comes from building workflows that users genuinely need to perform daily. If your product is not a daily-use tool, focus on MAU and ensure each session delivers sufficient value.
How Floe approaches this
Floe grows monthly active users by solving the two biggest leaks in the MAU funnel: users who sign up but never activate, and users who activate but gradually disengage. For the first group, Floe's AI agent provides immediate, personalized onboarding that guides new users to their first meaningful action. Instead of landing on an empty dashboard and bouncing, users complete a core workflow with the agent's help, converting them from sign-ups into genuinely active users.
For the second group, Floe drives ongoing engagement by helping users discover new capabilities as their needs evolve. A user who has been using the same three features for months might not realize the product can solve adjacent problems. The agent can introduce relevant features at natural moments, re-engaging users who would otherwise plateau and eventually drift away. Every user who stays active is a user contributing to MAU and, ultimately, to revenue.
FAQ
How should you define "active" for MAU calculations? Tie it to a core action that represents value delivery, not just presence. For a design tool, "active" might mean creating or editing a design. For a data platform, it might mean running a query or viewing a dashboard. Avoid counting passive actions like logging in or opening the app. The definition should pass the test: "if a user did only this action once, did they get real value from the product this month?"
Is MAU a useful metric for enterprise SaaS? MAU is less useful as a top-line growth metric for enterprise products with small customer counts and high contract values. However, MAU within accounts is highly valuable. Tracking how many licensed users at a customer organization are actually active reveals adoption depth and predicts renewal risk. An enterprise account where MAU is declining across the contract term is a churn risk regardless of what the executive sponsor says in quarterly business reviews.
What causes MAU to plateau? The most common cause is that new user acquisition is offsetting churning users at roughly equal rates, resulting in a flat number that feels stable but is actually a sign of a leaky bucket. Other causes include market saturation in your primary acquisition channel, a product that delivers one-time value (users accomplish their goal and leave), and seasonal patterns. Breaking MAU into new, retained, resurrected, and churned components identifies which dynamic is driving the plateau.