MAU, WAU, and DAU: Why should we care about ‘active’ users?
MAU stands for monthly active user. It measures the number of users that have done something meaningful in your product in the last 30 days/calendar month, but it can also power advanced analysis like conversion and retention rates that show how “sticky” or valuable a product truly is.
A product without users is like a fish without water. In order for a product to swim (be successful), it has to have users.
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: Leveraging product analytics to measure users who are actively using your product is the way to know whether they drive any potential for revenue, and that’s represented in a time-spanning metric like monthly active users (MAUs). Even more advanced are metrics like conversion and retention that show 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 at the core of higher analysis metrics like those, making it even more important than its face value for measuring product performance. Yep, getting to super insightful key performance indicators (KPIs) typically starts with identifying MAUs, DAUs (daily active users), and WAUs (weekly active users). Here’s how it all works.
Defining active users
Defining active users in the right way, such that it’s most meaningful to your company, is the first step in the process. Since “opening the app” can often be too low of engagement to be meaningful, individual products need to find what user action(s) are the most insightful indications for them. And it 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:
- Any person who “watches a video”
- Any person who “watches a video for 10+ mins”
- Any person who “watches 3+ videos in the last week”
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 users were active in the last month. Similarly, DAU and WAU measure how many users were active in the last day or last 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.
Active users power other metrics
How are these “active user” measurements useful beyond their simple totals? MAUs, DAUs, and WAUs can help indicate deeper analysis—or at least the need to investigate further.
- Active user numbers going up over time indicates, one way or another, new users are coming to your product.
- A measurement of net new users against MAUs, DAUs, or WAUs can indicate whether your rates of retention (when users stay with your product over time) or churn (when users stop using your product) are where you want them to be.
- 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.
- By comparing MAUs to DAUs, you can also define good or bad engagement and decide whether you need to investigate new ways of bringing retained user to the product more often.
Active users and their relevance in the current product-led growth era
At Mixpanel, we obviously practice what we preach and keep track of active users and how they progress through the product. This product analytics capability 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, so 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 all 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.
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