What is MAU and how to calculate it — a complete guide

MAU (Monthly Active Users) is the count of unique users that interact with a product or service in a given month. It’s one of the cleanest signals of engagement available in any SaaS or consumer-app dashboard, and it’s the metric every investor and operator will ask for once your product is live.

This guide covers what MAU actually means, how to calculate it without fooling yourself, what to compare it against, and where it falls short.

What MAU means (and what it doesn’t)

MAU counts unique users, not sessions or events. If the same person opens your app 30 times in a month they count once, not thirty. That makes MAU a reliable measure of reach over a 30-day window, not of intensity.

Key nuances to pin down before you start reporting it:

“Active” is your definition, not Google’s. It can mean “logged in”, “completed an event of value”, “made an API call”. The harder the definition (closer to a value-creating event), the more honest the metric.
30-day rolling vs calendar-month. Most analytics tools default to a rolling 30-day window. Dashboards meant for a board often use the calendar month so you can compare apples to apples month over month.
Identity, not seats. In B2B you usually want active accounts (logos) alongside MAU, since one account with 50 active seats is materially different from 50 accounts with 1 active seat each.

How to calculate MAU

The formula is straightforward — the work is in the definition of “user” and “active”:

MAU = count of distinct user_ids where event = <value-creating action>
      over the last 30 days (or in the calendar month)

A worked example. Imagine a B2B SaaS where “active” means opened a report:

User	Actions in May
user_001	12 reports
user_002	1 report
user_003	0 reports (only logged in)
user_004	4 reports

MAU for May = 3 (user_001, user_002, user_004). user_003 logged in but didn’t perform the value-creating action, so they don’t count.

Some teams report MAU as a percentage of total registered users:

MAU% = active users in the month / total registered users × 100

This is the “engagement rate” version of the metric and is much more useful than the absolute count for spotting trends. If your active-user count is growing but your engagement % is flat, you’re acquiring without retaining.

Why MAU matters

MAU is a leading indicator of three things any operator should care about:

Retention health. A drop in MAU month-over-month, holding new-user acquisition constant, is the cleanest early signal of churn before MRR moves.
Product-market fit. For early-stage products, MAU stagnation tells you the value proposition isn’t sticky enough to outweigh acquisition. No amount of paid traffic fixes that.
Monetisation runway. If you’re freemium or pre-revenue, MAU is the asset you’re sitting on — investors will value the company partly off MAU × an industry multiple.

MAU vs DAU vs WAU

MAU is usually paired with daily (DAU) and weekly (WAU) variants:

Metric	Window	What it tells you
DAU	last 24 h	Habit / daily-use intensity
WAU	last 7 days	Engagement frequency
MAU	last 30 days	Reach + retention

The ratio between them is itself a metric:

Stickiness = DAU / MAU

A 20% stickiness means the typical monthly user shows up about 6 days a month. For SaaS aimed at daily use (Slack-style), aim for 50%+. For tools used once a week (reporting, payroll) anything above 15% is good. For monthly-use tools, MAU alone is what matters.

We covered the calculation of active users in detail in How to calculate active users in a SaaS startup.

Where MAU falls short

Don’t use MAU as your only KPI. Common pitfalls:

It hides quality. Two users who log in for 5 seconds and one user who runs three workflows count the same. Pair MAU with feature-adoption metrics.
It can be gamed. Aggressive email reactivation campaigns or push notifications inflate MAU without genuine value being delivered.
It’s not standardised across companies. Different teams define “active” differently — be careful when benchmarking yourself against competitor disclosures.
It lags behind churn. A user who silently disengages but keeps paying still counts as a customer in MRR; they’ll drop off MAU first. That’s actually the point — but only useful if you’re tracking the trend.

How to grow MAU

Three levers, in order of long-term impact:

Improve the activation event. Users who hit the “aha moment” in their first session retain 30-50% better than those who don’t. Map the activation step, instrument it, and reduce the time-to-value.
Reactivate dormant cohorts. Users who’ve been inactive for 30-90 days but haven’t churned are the cheapest source of MAU growth. Targeted email + in-app messaging based on what they used last works better than generic “we miss you” blasts.
Earn the second visit. Onboarding shouldn’t optimise for completion; it should optimise for the second login. Track day-2, day-7 and day-30 retention separately and prioritise the lowest one.

FAQ

How is MAU different from total users?

Total users is the cumulative count of everyone who ever registered. MAU is the subset that interacted with the product in the past 30 days. Total users is a vanity metric; MAU is an engagement metric.

What’s a good MAU for a SaaS startup?

There’s no universal benchmark — it depends entirely on your TAM, pricing and acquisition strategy. What matters more is the trajectory: month-over-month growth and the DAU/MAU ratio.

Does MAU include trial users?

It should, if you’re measuring product engagement. If you’re measuring engaged paying customers, filter to active subscribers only and report it as “active customers”, not MAU.

How often should I report MAU?

Monthly to the board, weekly to the product team, daily to the data team. The data team needs to spot anomalies fast; the board only needs the trend.

NextScenario tracks MAU, DAU/MAU stickiness and product-engagement cohorts automatically by connecting to your analytics stack, Stripe and your CRM. Book a 30-min demo — we’ll show you your own retention curves with your real data.

For the Spanish version of this guide, see ¿Qué es MAU y cómo calcularlo?.