Definition
MAU counts unique active users over a month (or rolling 30 days). It measures reach and is often paired with DAU/WAU to measure frequency (stickiness).
Formula
MAU = unique active users in a month
How to use it
- Use a consistent window (calendar month vs rolling 30 days).
- Pair with DAU/WAU to measure usage frequency, not just reach.
Common mistakes
- Mixing rolling 30-day MAU with calendar-month MAU in trends.
- Using a low-quality 'active' definition that overcounts accidental users.
Why this matters
This term matters because small changes compound in SaaS metrics. Use consistent definitions by cohort and segment so you can diagnose retention, payback, and growth quality.
Practical checklist
- Write a 1-line definition for "MAU (Monthly Active Users)" that your team will use consistently.
- Keep the time window consistent (weekly/monthly/quarterly) when comparing trends.
- Segment results (channel/plan/cohort) before drawing big conclusions from blended averages.
- Use a calculator that references this term (e.g., DAU/MAU (Stickiness) Calculator) to sanity-check assumptions.
- Read the related guide (e.g., DAU/MAU (stickiness): definition, how to calculate, and benchmarks) for context and common pitfalls.
Where to use this on MetricKit
Calculators
- DAU/MAU (Stickiness) Calculator: Compute DAU/MAU stickiness and translate it into implied active days per month.
- WAU/MAU Calculator: Compute WAU/MAU and translate it into implied active weeks per month.
Guides
- DAU/MAU (stickiness): definition, how to calculate, and benchmarks: DAU/MAU explained: what it measures, how to compute it correctly, and how to interpret stickiness for different product cadences.
- WAU/MAU: a weekly stickiness metric for B2B and weekly workflows: WAU/MAU explained: when to use it instead of DAU/MAU, how to calculate it correctly, and how to interpret it.
- PLG metrics hub: activation, trial conversion, stickiness, and adoption: A practical hub for product-led growth metrics: activation rate, trial-to-paid, DAU/MAU and WAU/MAU stickiness, feature adoption, and PQL-to-paid conversion.