What is Monthly Active Users (MAU)?
The number of unique users who interact with an app within a 30-day period, measuring monthly engagement reach.
Monthly Active Users (MAU) measures the number of unique users who interact with an app within a 30-day window. It provides a broader view of user engagement compared to DAU, showing how many unique users engage with your app over a monthly period. MAU is crucial for understanding user base growth and long-term engagement patterns.
Why It Matters
MAU helps measure overall app reach and growth trajectory over time. It's essential for calculating user stickiness (DAU/MAU ratio) and understanding seasonal patterns. Investors and stakeholders often use MAU as a key indicator of product-market fit and business health. MAU also helps calculate monthly churn rates and user acquisition effectiveness.
How to Calculate
MAU counts unique users who performed any defined "active" action within a rolling 30-day period. A user is only counted once regardless of how many times they use the app. MAU/DAU ratio (stickiness) = Average DAU ÷ MAU × 100, showing what percentage of monthly users engage daily.
Industry Benchmarks
| Category | Average | Good Performance |
|---|---|---|
| Social Media Apps | 15% - 25% stickiness | 30%+ stickiness |
| Gaming Apps | 10% - 20% stickiness | 25%+ stickiness |
| Utility Apps | 5% - 15% stickiness | 20%+ stickiness |
| E-commerce Apps | 3% - 10% stickiness | 15%+ stickiness |
Best Practices
Track MAU alongside DAU to understand user behavior patterns. Monitor MAU growth rate month-over-month for trend analysis. Use cohort analysis to understand how MAU changes for different user acquisition campaigns. Focus on improving stickiness ratio (DAU/MAU) to increase user engagement frequency.
Examples
A social app with 1M MAU and 200K average DAU has 20% stickiness, indicating good daily engagement. A productivity app tracks MAU by feature usage to identify which features drive monthly retention and inform product development priorities.
Notes
MAU provides better context for seasonal businesses and apps with natural usage patterns (like tax apps or holiday shopping apps). High MAU with low DAU may indicate users find value but don't need daily engagement. MAU is less volatile than DAU, making it useful for long-term planning and trend analysis.