Active Users: Measuring Business Success with DAU, WAU, and MAU

Active-Users-Measure-the-Success-of-Your-Business

One of the best ways to measure the success of your business is to look at data of active users because with it you are able to understand just how sticky your product is. We know analyzing active users can get a bit complicated, so we created this article to thoroughly cover how to define active users, how to accurately track them, and how they can help your business. Let’s dive in.

What are active users?

An active user is a user who has interacted with your business during a specified period of time.
Now that’s a very broad definition, so let’s look at this in more detail. There are two main components needed to measure active users, defining a time interval, and picking an interaction.

For example, an active user can be measured as a user that has logged back into his account to interact with the product in the last 30 days.

The time interval component is crucial to have valid and actionable data that you can compare. You can only accurately analyze active user data by looking at it in time intervals.

While the time interval component is pretty straightforward, what constitutes an interaction can differ from one company to another.

Let’s use Facebook as an example, here’s how they define an active user:

Facebook describes a monthly active user as “a registered Facebook user who logged in and visited Facebook through our website or a mobile device, used our Messenger app, or took an action to share content or activity with his or her Facebook friends or connections via a third-party website or application that is integrated with Facebook, in the last 30 days as of the date of measurement.”

This means that if a user doesn’t do any of those things for 30 days, they will be considered inactive users. Logging in to Facebook, visiting Facebook on an already logged in account, using their Messenger app, sharing content, and even reacting to content on a third-party website are great examples of interactions that can define an active user.

The most common example of an interaction is a user logging in their account or visiting the website with an account that is already logged in. Defining the rest of the interactions that make up an active user will depend on how users interact with each business.

One thing that stays consistent across many companies is the way active users get reported:

Active users are normally reported as DAU, WAU, and MAU:

  • DAU meaning Daily Active Users - is typically used for businesses where users are expected to interact on a daily basis (eg. email, calendar, games)
  • WAU meaning Weekly Active Users - is typically used for businesses with weekly frequency (eg. forums and social communities, mobile apps, productivity & analytics tools).
  • MAU meaning Monthly Active Users - is typically used for B2B apps where users are expected to interact a few times a month or less (eg. accounting & bookkeeping software).

If you want to dive even deeper into analyzing active users you can even calculate the ratio of Daily Active Users to Monthly Active Users (DAU/MAU) - which ends up being the proportion of monthly active users who engage with your product in a single day window.
It’s important to note that users that visit on a daily basis will show up in the weekly and monthly reports as well.
I’ve seen many people analyze their active users report on different time intervals (daily, weekly, monthly) and think there is something wrong with the data when there’s not:

 

measure daily, weekly, monthly active users (dau, wau, mau)

If a user is visits your app every day of the week, he is counted as a single user. Not as seven different users.

If daily active users (DAU) are around 769, people usually expect weekly active users (WAU) to be 7 times that and expect monthly active users (MAU) to be around 30 times that. Simply multiplying your DAU by 7 or 30 and thinking you should have 5383‬ WAU or 23070 ‬MAU, is an incorrect approach to analyzing active users.

The report in the picture with 769 DAU, 2793 WAU, and 7264 MAU is actually correct. The report is showing the fact that most people who interact with the app are returning users with a high frequency.

This is because if a single user interacts with your app every day, you will have 1 DAU, but also 1 MAU (and not 30 MAU). That user is only being counted once because they are the same user every day.

Let me illustrate active users a little bit more with a visual example:


measure dau, mau, wauA whole month's user activity.

In the picture above, you can see user activity in a given month. The circles signify a user being active that day. Even when it’s clear that one user becomes active way more times than the other two, all three users are still counted as 1 MAU each. Totaling 3 Active Users for that month, because all of the users were active at least one time during that month. 

We can go in more detail by narrowing down into WAU and DAU. Looking at WAU, during the 1st week there is only 1 WAU but during the 2nd and 4th week, there are 3 WAU. 

The same thing goes for DAU, looking at the 4th week there’s one day with 2 DAU, another day with 1 DAU and the other five days of the week have 0 DAU. 

There Are Also 2 Types of Active Users Worth Noting: 

  • New Active Users
  • Returning Active Users

New active users are the users that, in a specified period, interact with your app for the first time. (eg. created an account). This type of active user is closely related to acquisition and activation metrics. They are often heavily influenced by the user onboarding process they experience.

While Returning active users are the users that keep coming back to your app and are closely related to the retention and recurring revenue metrics of your business. 

How can you accurately track active users?

In order to accurately track active users, you need to make sure that your analytics service recognizes a user as being yours, regardless of whether they first log in from the office and then later on from their home. 

To accurately report retention, your analytics tool also needs to be able to recognize the same user even if they log in six months later using a different browser or device. 

That’s why cookie-based web analytics tools can’t accurately track active users, unless they also offer the option to generate a unique ID for each user. The ID needs to always be tied to the user, no matter when or from what device the user logs in.

Therefore, the best way to track active users is to make sure that every action logged from their activity is attributed to an ID that is both persistent and unique to each user. 

The ID can be a unique number automatically assigned to the user’s account, but it can also be as simple as their email address or their username.

There are exceptions though. Not every logged action is a meaningful activity.

Let’s suppose you also monitor the emailing activity of your users. Your system sends an automatic email to George that was triggered by his recent inactivity. A tracking log is triggered and says that George got an email.

That log should not be counted as activity by George, and neither should be the fact that George opened the email. These are not signs of activity from George.

However, if he clicks a link in the email and lands in your app, that is an activity you want to consider.

So, to make sure you’re tracking your active users correctly, exclude crons and automatic actions that are done for your users, and not by your users.

Get deep insights into how your users behave

We know that data analytics can quickly get complicated and most analytics services give you access to data, but not insights. Fortunately, InnerTrends makes it so you don’t have to be a Data Scientist to discover the most important insights in your data, we’re all about transforming raw data into actionable insights for your business to use. 

Want to learn more about InnerTrends? Click here to schedule a demo and witness for yourself just how powerful and effective our software can be at extracting insights from your data.Get deep insights into how your customers use your product. Without being a data scientist!

This article was originally published November 2015 and was updated September 2020.

Author: Claudiu Murariu