MAU, WAU, and DAU: Why should we care about ‘active’ users?

A product without users is like a fish without water. Without users, your product will sink faster than a goldfish in a dry bowl. 

Of course, what makes a user has changed as the world of digital products has matured. We know these days that merely signing up for an app does not make a user very useful: To know whether your users are driving any potential revenue, you need to leverage product analytics to measure those who are actively engaging with your product. That's represented in a time-spanning metric like monthly active users (MAU). 

More advanced metrics like conversion and retention will show you how “sticky” or truly valuable a product is and whether your active users will stay for the long haul. But tracking the number of active users is foundational to measuring those more advanced metrics, which makes MAUs an important metric to track to measure product performance. Yep, getting to insightful key performance indicators (KPIs) typically starts with identifying MAUs. Here’s how it all works.

Defining active users the right way, so that the definition is meaningful to your company and product, is the first step in the process. Simply “opening the app” is not a meaningful engagement metric. Instead, individual products need to define what user action(s) are the most useful indicators for them. And what those indicators are can change over time. 

To illustrate how this definition could evolve depending on your company’s goals at a particular point and maturity, let’s take a streaming service for example. An active user could be:

Robust analytics platforms like Mixpanel give you the flexibility to customize MAU measurement by using a wide variety of variables so that it’s relevant to a specific business, product, or app.

Depending on the type of digital product, your focus might be on measuring active users in one of a few standard units of time: MAU measures how many users were active in a given month. Similarly, DAU and WAU measure how many users were active in a given day or week. For example, it could make the most sense to measure a social media app that’s designed for more frequent user engagement by DAUs.

With the right analytics tool, MAU becomes easy to calculate: 

That’s it, you have your MAU for that month. You can track changes in MAU over time, look at averages for the year, or use custom date ranges to see how seasonality affects product usage.  

Mixpanel has lots of options for user segmentation, including easy access to your MAU, WAU, and DAU. You can also track active users who have done several different actions, as opposed to just one: 

Using MAU to define and monitor active users is an important first step to lifecycle analysis. Once you’re tracking active users, you can dive further by segmenting those users into different groups and tracking the evolution of their engagement with your product over time. It’s a powerful way to deepen your understanding of your users and find new opportunities for product growth. 

Different products measure stickiness (how often users use a product) in different ways, but the most common is by far the DAU/MAU ratio, originally popularized by Facebook. 

With the DAU/MAU ratio, you’re comparing the volume of unique monthly active users to unique daily users. This gives you an idea of how many monthly users engage with your product each day. The higher the ratio, the higher your stickiness. 

The steps to calculate your DAU/MAU ratio are fairly straightforward: 

As a benchmark, 20% and above is considered a good DAU/MAU ratio, though specific industries vary—Gainsight estimates that 40% is a good benchmark for B2B SaaS products, for example. 

It’s important to note that stickiness metrics can sometimes be misleading. If you have a single monthly active user who visits your product every day for 31 days, your MAU/DAU ratio will be 100%. That’s great on paper, but we can probably all agree that building a product for one person isn’t exactly a viable growth strategy. 

Active users are important, but products need power users who love their product and have higher-than-average engagement rates to be truly successful. One common way to measure power users is the power user curve, popularized by Andrew Chen. The power user curve measures users’ engagement by the total number of days they were active in a month. 

A good power user curve smiles, showing that a higher percentage of users are active every day:

How are these “active user” measurements useful beyond their simple totals? Measuring active users and MAU/WAU/DAU is a key part of tracking more advanced product metrics like retention, engagement, and even product revenue. When you combine MAU with other engagement metrics, it can give you a more complete picture of app engagement or indicate the need for deeper analysis. 

Retention rates measure how many users stay with your product over time. Retention is one of the most important metrics for digital products and is the key to finding product-market fit. But retention doesn’t take into account active vs. inactive users.

Measuring net new users against MAUs or DAUs can give you a more complete picture of user retention and churn.

Different products will have different product usage intervals (how often users engage with your product) and there is no one-size-fits-all good engagement. That said, your product’s DAU/MAU ratio can give you a good starting point to investigate engagement and stickiness for your product and help you figure out if you need to investigate new ways of bringing retained users to the product more often.

When your number of active users is going up and retention is holding but your number of paid users isn’t budging, it could be time to look at your free-to-paid conversion flow.

Active user metrics are useful to prove business impact. MAU can give you an idea of how many people are purchasing your product, which in turn will help with revenue forecasting and securing funding. That’s one way that Matteo Borinelli, Head of Controlling at Evulpo, uses DAU and MAU. “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,” he says. 

At Mixpanel, we obviously practice what we preach and keep track of active users and how they progress through the product. This enables us to make an impact in our product-led growth (PLG) journey.

PLG is a model for capturing new customers by letting them use a product in order to find value in it themselves. More than monitoring whether users are engaged with your product, it relies on creating flows that keep users engaged with your product. By tracking MAUs, DAUs, and WAUs and breaking down the information by cohorts (segmented by things like where users came from or what features users are using in the app, etc), you can make better decisions about your product’s PLG motions.

For example, when a product’s number of MAUs is going up thanks to a great marketing campaign but high churn follows, a company going al -in on PLG would look at improving the time to value (or time to “aha”) for these new users. The same kind of PLG adjustments could be looked into for the scenario of when active users and retention are looking good but conversions from free to paid users are not.

All of this kind of calculation and requisite action starts with measuring a basic thing: how many active users are flowing through the product. From there, rates and expectations of success can be set, and the fun part of improving your product to get there can begin.