What Are Monthly Active Users? How Do You Define MAU?
There’s a certain comfort in the firm footing of quantifiable metrics. You might think monthly active users (MAU), the number of unique visitors who perform some sort of an action in an app in a 30-day window, might fit easily into that category. But it turns out the way companies calculate the figure is complicated at best — and, in some cases, utterly baffling.
Even still, MAU remains a key performance indicator of online engagement. A number of public companies tally it in quarterly reports, and many investors look to it at as a snapshot of a company’s user growth and financial health.
But because companies treat the metric differently, tying monthly active users to revenue can be like playing a game of whack-a-mole. How “active” must a user be to be counted among monthly active users? What constitutes “use?”
“The purpose of this metric is really to show that end users are getting value from your product.”
These are not just semantic questions or fodder for cagey business practices, said Zari Zahra, chief product officer at Spekit: a sales enablement and documentation platform that can be embedded in Salesforce. Their broad latitude reflects that no two software firms are alike. They have different sizes, service models, shareholder interests and business objectives, and they need room to articulate them.
“It’s really, really critical to consider the business,” Zahra said. “The purpose of this metric is really to show that end users are getting value from your product.”
What is Monthly Active Users (MAU)?
“I care about it a lot,” he said. “As I look at stickiness, this is something we want to measure against and get better month to month as we grow our user base.”
To attempt to unpack all this, we talked to several product managers and executives about how they define MAU — and what it means to their business goals and customers.
How companies define MAU
PickFu is an e-commerce tool that allows individuals and organizations to use crowdsourced data to market test products against key demographics before they’re released. An author or publisher might want to know, say, how a book cover will fare among middle-income women ages 18-49 (and, yes, readers do judge books by their covers).
Do people prefer chocolate or flowers for Valentine’s Day? PickFu can run the numbers. And the tool allows customers to A/B test two options against a data pool of hundreds of potential users.
Before defining MAU, Chin said, PickFu had to reach internal agreement on a definition of a user. Their current definition is “any unique visitor that would like to use our products and services and fills out our survey, whether logged on or as a guest user.”
But dig a little deeper, and you’ll find the criteria for an “active user” is more selective. “They have to be a return user who has made at least a second transaction, and are considered locked onto the site; then they’re considered part of the MAU,” Chin said.
Under this definition, Chin estimates PickFu has 300 to 500 monthly active users. That is well shy of its monthly users based on page views — a figure about 10-15 times higher — but it’s a much more meaningful indicator of customer habits. Regularly returning users are key to PickFu's business model, and a high cost per acquisition and relatively low MAU figure reflects that.
“We look at monthly active users as users that are actually jumping on the app at least once per month and doing something that aids in one of our KPIs.”
By contrast, the Boston-based QuicWit, an online app targeting trivia nerds, has a more inclusive user definition. “We look at monthly active users as users that are actually jumping on the app at least once per month and doing something that aids in one of our KPIs,” said founder and CEO Stephon McCoy.
Practically speaking, that means either clicking a button leading to trivia questions and a survey of potential future prizes, or selecting a button linked to sponsoring partner sites. “Those are the two things that we care about the most because we build a lot of metrics off those behaviors,” McCoy said.
Jeopardy alumni, or those desperate to get on the show, are the app’s core users, he explained, though lately the firm has seen growing interest from casual gamers. The format is fairly simple: Trivia questions curated to defy a Google search are sent to users at random times. The first user to respond with the correctly spelled answer is mailed a prize. User responses to surveys inform future prize selections, from kitchen appliances to party games.
Of QuicWit's 1,300 total monthly users, McCoy estimates 74 percent return on a monthly basis and 60 percent click one of the two main cue buttons. He regards this as a signal the nine-month-old startup is proving its worth against online gaming competitors, such as Zynga. “The industry average [for MAU] is usually around 20 percent,” McCoy said.
Then there’s Cubii, a Chicago-based company that sells seated elliptical machines. According to co-founder and Chief Marketing Officer Shivani Jain, Cubii uses a more granular metric — daily active users (DAU) — as its preferred data marker.
“For us, we like to be a little more aggressive with what is defined as an active user,” Jain said. “One of the reasons is because our product is a lot easier to do while you’re doing other things. It’s more integrated into people’s daily lives.”
The company’s Cubii Pro machine is connected to a mobile app by Bluetooth and allows for interaction with other users and apps like Fitbit and Apple’s Health, Jain explained. But tracking daily active users is difficult. Cubii has sold some 500,000 units across diverse e-commerce sites, and many of these machines are not connected to the mobile app.
One telling indicator of active use, however, is a 20-question online survey recently sent to the company’s 30,000 mobile app users. Of the 1,642 respondents, 91 percent reported that they use the app at least once per week, a sign, Jain said, of strong customer engagement.
User personas complicate the picture
User personas make things trickier, especially for small and mid-size businesses targeting a narrow subset of consumers. Chin has spent much of the last year consulting with PickFu founders John Lyle and Justin Chen on an MAU formula that could better reflect user retention and growth capacity — metrics that are often at odds.
“We just sort of talked it out. We studied our conversion funnel and looked at how many acquisitions we’re having through organic channels, paid traffic, etc.,” Chin said. “And then we were like, OK, at the bottom of the funnel, what do we have left? We have this many return users that convert, here’s what our MAU is.”
Most of PickFu’s revenue, he said, stems from four or five user personas, with key subsets being book authors and publishers. A tight conversion funnel like PickFu’s makes it easy to write release tickets and add tightly scripted product features for dedicated users. But it can also limit growth opportunities.
“From an acquisition standpoint, or even looking at your numbers on paper, it really is a small subset of your traffic overall. And so, therefore, it isn’t really indicative of changes you should make to your brand as a whole,” Chin said.
PickFu isn’t alone in wrestling with this issue. Zahra said calculating a useful MAU formula for two very different kinds of users has led Spekit to parse the figure by persona: “active,” in other words, varies by user goals: Some Spekit users should be logging in often; others should hardly use the platform at all.
“For end users [such as sales teams using Spekit for training in Salesforce adoption], the amount of time they spend actually viewing content, reading content, absorbing content, is probably the biggest value they get from Spekit, right?” she said. “Whereas for a creator or an administrator, you might not want that number to be high. In fact, what we’re finding is for a lot of clients, the speed and ease with which they can create content using Spekit, it is what they like.”
Calculating a useful MAU formula for two very different kinds of users has led Spekit to parse the figure by persona.
In other words, if administrators can set up the tool quickly, and then use it infrequently, Spekit is giving them what they want. The MAU for this cohort may drop off in the weeks and months after a successful integration, but that’s OK. Especially if they come back later.
“We’re trying to create a habit loop,” Zahra said. “Whenever new changes happen in your organization, whether it’s your sales team changes or something important happens, a micro change you’re not going to be shouting from the rooftops. For those changes, what we want is for people to be continually creating and updating content to document them.”
Not all users are created equal
How much time a user actually spends on a site, and how much money they spend while there are important considerations that can get glossed over in MAU figures. So can the depth of their engagement.
“Say I’m a power user. I’m on Facebook, and I’m powering through my newsfeed and I’m liking a bunch of stuff and engaging with a bunch of stuff,” Chin said. “I would say that that user is a lot more valuable than a user who just slightly glances at some articles and doesn’t engage with any. Doesn’t hit any buttons, doesn’t comment. But they would probably be both classified in the same belt.”
For these reasons and others, putting too much stock in MAU can be a mistake. According to a report in Baremetrics, it is not MAU, so much as it’s the relationship to other metrics, like daily active users, that matters most. Tracking such ratios can help indicate the loyalty of users and flag user drop offs triggered by poorly performing features or technical snafus. More hopefully, it can reveal increased engagement tied to a successful upgrade.
“Essentially, there’s the dormant accounts, versus the persistently active accounts, versus reactivation rates. I think that’s a good comparison. And you have to do it by cohort,” Zahra said. “If your company is tied closely to sales cycles, tied to specific industries that have a specific fiscal year or seasonality to them, then people will be using the platform more, entering more information into Salesforce, for example. Or hiring new people.”
Beware of bad data
Just like there’s fake news, there’s fake MAUs, and product managers should be on the lookout for them. The Verge reported that Twitter’s loss of monthly active users for three consecutive quarters in 2018, and eventual decision to move to monetizable daily active users, was closely tied to “cracking down on bots and spam.” Let’s just say bots may not be the best representative sample of your user base.
“There can be a lot of hidden false positives, not just through the main product interface, but through add-ins, through APIs and through other mechanisms.”
To avoid false and misleading MAU data, Zahra said Spekit filters out spam accounts, excludes internal testers from active user numbers, and caps the number of free users on premium accounts at 10. But with increased interoperability among software applications, and embedded products, like Spekit, tethered to integrated platforms, actions can be difficult to trace to a single source.
“There can be a lot of hidden false positives, not just through the main product interface, but through add-ins, through APIs and through other mechanisms,” Zahra said. “Is every page load an action? You really have to think consciously about which of these are truly intentional, meaningful interactions.”
Should there be a new industry standard?
Some critics argue that a lack of clear industry standards can lead to unfair comparisons among similar companies. In fact, Facebook’s annual report on Form 10K declared an intention to move away from the term altogether in favor of a more comprehensive analysis based on the activity of users who visited Facebook, Instagram, Messenger or WhatsApp during an established reporting period.
“We ... are reporting our estimates of the numbers of our daily active people (DAP), monthly active people (MAP), and average revenue per person (ARPP) (collectively, our ‘Family metrics’),” the report states. “We believe our Family metrics better reflect the size of our community and the fact that many people are using more than one of our products. As a result, over time we intend to report our Family metrics as our key metrics in place of DAUs, MAUs, and ARPU in our periodic reports filed with the Securities and Exchange Commission.”
Facebook’s annual report on Form 10K declared an intention to move away from the term altogether.
Twitter’s new preferred metric, monetizable daily active usage or users (mDAU), was defined in a fourth-quarter letter to shareholders as “people, organizations, or other accounts who logged in or were otherwise authenticated and accessed Twitter on any given day through twitter.com or Twitter applications that are able to show ads.”
Naturally, a high MAU number might look enticing to investors or a company eager to acquire a smaller rival. But even without an industry standard to deter opportunistic number goosing, the practice is rare, Zahra said. Companies are accountable to customers and shareholders, who are generally shrewd enough to see through fuzzy data.
“Here’s why it’s important to be brutally honest: If we’re not honest with ourselves, we’re not fooling our users,” Zahra said. “We’re certainly not going to be fooling our admins and experts. We’re not going to be fooling our decision-makers when the time comes for renewal, whether it’s a consumer or someone else. That’s the curse and benefit of being in software. You can measure it.”
While not ready to retire the metric, she said the industry would do well to coalesce around standardization by sub industry, such as creating distinct MAU metrics for knowledge-based platforms and digital adoption services.
McCoy agrees: “I get that some companies don’t want to tip their hats. But I also think, if you have some type of standardization, it doesn’t have to be super heavyweight, but something that says your KPIs should tie to a strategy, an indicator, or revenue, etc., the industry would benefit. I hope the days of just saying, ‘my app was opened,’ and considering that an active user, are over.”
The route Twitter and Facebook have taken, assessing user activity over shorter time intervals and tying users to specific revenue streams and product integrations, may be the way forward for large multinational public companies. For smaller private firms, the term may ripe for reinterpretation.