Monthly active users (MAU): Definition, formula, and 2026 benchmarks

Monthly active users (MAU) is one of the most-tracked metrics in digital products. It’s intuitive, easy to explain in a board deck, and satisfying when the line goes up and to the right. But a growing MAU number can hide more than it reveals. If the users counted in that figure aren’t experiencing meaningful value, the metric is closer to noise than signal.

In this guide, we cover everything you need to know about MAU: how to define and calculate it, what a good DAU/MAU stickiness ratio looks like for your specific vertical (with 2026 benchmark data from our State of Digital Analytics report, which analyzed 3.7 trillion events across 12,000+ companies), and why AI-powered products require us to rethink what “active” means entirely.

MAU measures the number of unique users who performed a meaningful action in your product within a 30-day window. The keyword here is meaningful: simply opening an app or logging in doesn’t cut it. Every product needs to define the specific action that signals a user has received value.

To illustrate how that definition can evolve, think about a streaming service. An active user could be defined as:

The right definition isn’t universal; rather, it reflects the specific point at which your product delivers its core value. Getting this right makes everything downstream more meaningful.

With the right analytics platform, calculating MAU is straightforward:

You can calculate MAU directly in Mixpanel—no SQL required:

From there, you can layer in cohort breakdowns, segmentation by user properties, and retention analysis, all in the same workflow. You can define active users around any event or combination of events, so your MAU figure actually reflects engagement, not just presence. And you can update the definition as your product and goals evolve, without re-instrumenting your data.

Different products live on different engagement cycles, and your active user metric should match the natural rhythm of how people get value from yours.

DAU (daily active users) is the right measure for products where daily engagement is expected and meaningful: social networks, messaging apps, productivity tools embedded in daily workflows. A DAU trend reveals habit formation and surface-level stickiness that MAU can obscure.

WAU (weekly active users) has become more useful for asynchronous products, such as content platforms, newsletter tools, scheduling software, and documentation products, where daily use isn’t the natural pattern. Measuring those users daily creates false churn signals; measuring them monthly loses resolution. WAU is often the more helpful cadence.

MAU suits products with longer natural usage intervals: project management software used in monthly review cycles, annual subscription tools, or products with inherent seasonality. It’s also the standard for investor reporting and board-level metrics.

Many products benefit from tracking all three. The relationships between them, particularly the DAU/MAU ratio, tell you more than any single figure in isolation.

The DAU/MAU ratio is the most widely used measure of product stickiness. It expresses the share of your monthly active users who engage on any given day, and it’s a useful proxy for how embedded your product is in users’ regular routines.

To calculate it: Measure your average DAU over a period, then divide by your MAU for the same period. A ratio of 20% means roughly one in five monthly users is active on a typical day. The higher the number, the stickier the product.

The short answer: it depends on your vertical. And we now have better data than ever to be specific about that.

For years, the default benchmark was a Gainsight estimate that 40% was a strong result for B2B SaaS. It was widely cited, but it was always a rough heuristic. Our 2026 State of Digital Analytics report, which analyzed data across 12,000+ companies and eight industries, gives us a more precise and current picture.

The data shows B2B SaaS stickiness averaging 31% in North America and EMEA, with APAC leading at 33%. That’s notably lower than the old 40% benchmark, suggesting many product teams have been measuring themselves against an unrealistic standard.

B2B SaaS at 31% is the new reference point. APAC leads at 33%, driven by AI and embedded fintech integrations that normalize daily product check-ins in the region. If your B2B SaaS product sits between 25% and 35%, you’re broadly in line with what we’re seeing across the market.

AI products in North America show unexpectedly low stickiness at 21%, but this number is misleading. In our analysis, mature enterprise AI users tend to accomplish more per session and return less frequently as a result. As we noted in the report, lower DAU/MAU in AI products can signals efficiency, not disengagement. (More on this in the next section.)

LATAM fintech wealth management reaches 38% stickiness, the highest figure in the table. This is driven by super apps like Nubank and Mercado Pago creating daily financial habits. If you’re building in that space, LATAM is your reference benchmark, not the global average.

Fintech banking in APAC (36%) reflects a similar pattern: Banking apps in high-mobile, highly banked populations function as daily utility products. Insurance rarely exceeds 27% anywhere, which makes sense given how infrequently people need to interact with it.

Stickiness improvement depends on diagnosing why users aren’t returning more frequently. A few things to try, depending on your product type:

Traditional active user definitions assume a human opens an app and takes an action. AI-native products break this assumption in two fundamental ways, and we’re seeing product teams grapple with this in real time.

First, autonomous agents act on a user’s behalf without a session. A workflow automation product might process hundreds of tasks overnight without the user logging in. By the conventional definition, DAU for that day is zero, but by any reasonable measure of value delivered, it was an active day.

Second, asynchronous AI features decouple usage from value. A user prompts an AI feature once, walks away, and returns to find the work done. Session frequency falls, but outcome quality rises. A standard MAU calculation captures the initial prompt while missing the compounding value being generated.

What this means practically: Teams building AI products need to define “active” around outcomes and recurring value, not logins and sessions. Some alternative metrics worth adding to your tracking:

To put this in context from our own data: AI products tracked in Mixpanel in 2025 saw a +26% year-over-year increase in total devices and a +27% YoY jump in acquisition volume—but stickiness varies sharply by region, with LATAM at 37% and North America at 21%. The gap likely reflects maturity: in more established AI markets, users have refined their habits and accomplish more per session. [Source: Mixpanel State of Digital Analytics 2026]

Measuring any of these signals requires event-level tracking that goes beyond page views or session counts. That’s exactly what Mixpanel’s platform is built for—tracking the specific events that signal value at each stage of the AI workflow, not just who logged in.

The DAU/MAU ratio gives you a single stickiness number, which is useful but blunt. The power user curve shows you the shape of engagement within your MAU, and that shape reveals things the ratio can’t.

Popularized by Andrew Chen, the power user curve plots users by the number of days they were active in a given month. On the x-axis: days active (0 to 28+). On the y-axis: the share of your MAU in each bucket. The shape tells you whether your MAU is built on deeply engaged users, casual visitors, or one-time arrivals.

A healthy curve smiles: A high proportion of users are active many days per month, with a smaller share in the low-engagement buckets. An unhealthy curve is L-shaped: Most users clustered at the low end, a small tail of power users carrying the metric.

The power user curve is more actionable than MAU alone because it shows you where to intervene. If casual users (active one to five days per month) represent a large share of your MAU, activation and onboarding improvements are the highest-leverage investment. If your power users (active 20+ days) are growing as a share, retention and feature depth become the priority.

One of the ways Matteo Borinelli, Head of Controlling at Evulpo, uses this kind of data: “Starting by looking at our daily active users, we use this data to estimate how many people are purchasing our product, and therefore what our estimated revenues will be. This data set can then be used to analyze marketing budget performance. As a result, we’ve improved the accuracy of our financial forecasts by a factor of three.” 

In Mixpanel, you can build the power user curve directly in an Insights report using a frequency histogram.

MAU is a count. Retention tells you whether that count means anything over time.

A product growing MAU at 20% month-over-month while losing 30% of those users within 60 days isn’t growing; it’s churning on a treadmill. Acquisition costs in SaaS make this particularly painful. Research has consistently shown that acquiring a new customer costs several times more than retaining an existing one, and that even small improvements in retention can drive meaningful revenue impact.

In our 2026 State of Digital Analytics report, retention emerges as the defining macro trend: With acquisition costs rising across every vertical, the teams seeing sustainable growth are shifting focus from raw MAU volume to engagement quality and retention depth. MAU growth without retention improvement is an early signal of churn risk at scale.

At Mixpanel, tracking active users and how they move through the product is central to how we think about product-led growth. By tracking MAU, DAU, and WAU and breaking down the data by cohorts—where users came from, which features they’re using, which steps they completed in onboarding—you can make better decisions about your product’s PLG motions.

When MAU is rising but free-to-paid conversion isn’t moving, for example, the issue usually sits in the activation layer: Users are getting some value but not enough of it, or not the right kind. Look at what your converted users do in their first week that unconverted users don’t. That delta is your activation opportunity.

Mixpanel makes it possible to build stickiness dashboards, power user curves, and retention cohorts without writing a single line of SQL. Try Mixpanel free, or talk to the team about what “active” should mean for your product.