Monthly Active Users (MAU) represents the total number of unique individuals who engage with a digital product such as a mobile app or website within a 30-day period. It is one of the most widely used metrics in product analytics, marketing, and growth measurement.
In practical terms, MAU shows how many people are actually using your product on a recurring monthly basis. It helps marketers, developers, and product managers understand engagement, retention, and the overall health of their app.
To calculate MAU, each user is counted only once within the time frame, even if they interact multiple times. Tracking MAU over time provides valuable insights into growth trends, customer behavior, and the impact of marketing efforts.
MAU is a key indicator of an app’s real reach and engagement. A high MAU typically reflects strong user interest and consistent value delivery, while a decline may point to retention issues or reduced satisfaction.
Monitoring MAU helps businesses:
By analyzing MAU alongside other metrics like DAU (Daily Active Users) and retention rate, teams can better understand whether their growth is sustainable.
MAU is calculated by counting the total number of unique users who perform at least one meaningful action within a defined 30-day period.
Formula:
MAU = Number of unique users active within a 30-day period
However, what qualifies as an “active user” can vary depending on business goals. Some examples include:
The key is to define “active” clearly and apply it consistently.
MAU alone provides a monthly snapshot, but when paired with DAU (Daily Active Users) it offers deeper insight into user loyalty and engagement.
The DAU/MAU ratio, often called the stickiness ratio, measures how frequently users return.
Formula:
DAU ÷ MAU × 100 = Stickiness (%)
For example, if your app has 8,000 MAUs and 2,000 DAUs, your stickiness rate is 25%. A rate between 20% and 25% is considered strong across most industries, meaning your users are engaging regularly.
Although MAU is a valuable metric, it should not be viewed in isolation.
1. Lack of standardization
Each company defines “active user” differently, which makes comparing MAU across products unreliable.
2. Shallow engagement
A user who logs in once is still counted as “active,” even if their interaction is minimal.
3. Inflation during marketing bursts
MAU can temporarily spike due to campaigns or app launches without reflecting genuine engagement.
4. Quality vs. quantity
MAU measures quantity of users, not the quality or depth of their activity. A smaller, loyal base can be more valuable than a large but inactive one.
To overcome these limits, MAU should always be analyzed alongside metrics such as retention, LTV (Lifetime Value), and ARPU (Average Revenue Per User).
1. Improve onboarding
A clear, engaging onboarding experience encourages users to return and explore more features.
2. Personalize communication
Use targeted messages, push notifications, or emails tailored to each user’s behavior.
3. Optimize push and in-app notifications
Send timely, valuable reminders rather than frequent, irrelevant alerts.
4. Deep linking for seamless journeys
Guide users directly to specific content or offers within your app, creating smoother experiences.
5. Continuous improvement
Regularly release updates, fix bugs, and introduce relevant features that enhance the app’s value.
6. Re-engage dormant users
Use retargeting, special offers, or reminder emails to bring inactive users back into the app.
MAU stands for Monthly Active Users. It measures the number of unique users who interact with an app or website during a 30-day period.
An active user performs at least one defined action within the app. This could be logging in, making a purchase, or completing another meaningful task.
MAU helps track engagement and retention trends, showing whether your product maintains an active user base.
MAU tracks users active in a month, while DAU focuses on those active daily. Comparing both gives insight into engagement consistency.
A ratio between 20% and 25% is considered strong. Higher percentages indicate that users return more frequently.
Not directly. However, it supports revenue analysis when combined with metrics like ARPU and LTV.
You can use analytics platforms, mobile measurement partners (MMPs), and mobile linking platforms (MLPs) to track MAU and related engagement metrics.