What Is DAU? WAU, MAU, and How to Interpret DAU/MAU
Shusaku Yosa
Daily Active Users, or DAU, counts unique users who meet a defined activity condition within one day. Activity might mean opening an app, logging in, reading content, or completing a core task. DAU/MAU describes activity frequency, but it does not directly tell you what percentage of users use the product every day.
Define active before counting users
A product-defined DAU metric is not automatically equivalent to GA4 Active users. Specify the qualifying event, success condition, included population, identity rule, and time zone. For some products, completing a core task is more useful than merely opening the app. Name that metric clearly, such as “core-task DAU.”
GA4 Active users qualify through an engaged session or platform-specific event or parameter conditions. They are not simply high-intent customers. The unique-user guide explains the limits of deduplicating identities.
Example activity rule | Useful for | Limitation |
|---|---|---|
Open the app or use the site | Contact with the product | Does not prove use of its main value |
View content | Media-use frequency | Views are not unique users |
Successfully finish a core task | Value-related activity | Needs a verified success event and deduplication |
Make a purchase or paid use | Revenue-related activity | A narrower population than all activity |
If page viewing defines activity, still deduplicate users. A page-view count is not DAU because one user can view repeatedly. Keep the units separate using the page-view counting guide.
Align DAU, WAU, and MAU windows
Metric | Typical window | State explicitly |
|---|---|---|
DAU | One day of unique active users | Date and time zone |
WAU | One week of unique active users | Calendar week or rolling seven days |
MAU | One month of unique active users | Calendar month or rolling window, such as 30 days |
GA4 28-day active users | A defined 28-day activity window | Not interchangeable with 30-day MAU |
A Monday–Sunday week differs from seven days ending on an arbitrary date. February’s calendar-month audience also differs from a rolling 30-day audience. The GA4 metric definitions separately describe one-, seven-, and 28-day active users and stickiness ratios.
Do not sum DAU to obtain MAU. In a fictional example, Monday has users A and B; Tuesday has A and C. DAU is two on each day, but the combined period has three unique users. Four is the sum of user-days, not the period’s unique audience.
Specify which DAU/MAU formula you use
Distinguish a selected day’s DAU divided by rolling MAU from average DAU divided by the same calendar month’s MAU. These formulas need not agree. GA4 dauPerMau describes the share of users active in the last 30 days who were also active in the last day. Do not silently substitute a monthly average DAU calculation.
Formula | Fictional example | Meaning |
|---|---|---|
Selected-day DAU / rolling 30-day MAU | 1,500 / 5,000 = 30% | Share of the 30-day audience active on that day |
Average DAU / MAU for the same 30 days | 15,000 / 50,000 = 30% | Average daily activity relative to the period audience |
User-days / MAU | 450,000 / 50,000 = 9 days | Average active days per user during that period |
The second and third rows use the same fictional dataset. A sum of 450,000 daily active users over 30 days gives average DAU of 15,000. Dividing by the period’s 50,000 unique active users gives 30%, or an average of nine active days per user. The activity, identity, and period definitions must match. A zero denominator leaves the ratio undefined.
Thirty percent does not mean 30% used the product every day. All users could have been active on nine days, or a smaller group could be much more frequent. To measure consecutive daily use, calculate that condition separately from user-level activity histories.
Judge frequency against the product’s purpose
A messaging service and a month-end invoicing tool have different natural usage cycles. Define when users should receive value before selecting a frequency target. Do not import an unsupported industry benchmark. High DAU can also reflect repeated failed attempts or unnecessary work.
Evaluate core-task completion, retention, and revenue alongside activity. If connecting frequency to revenue, align the denominator in ARPU calculations: revenue per registered user differs from revenue per active user.
Separate stickiness from cohort retention
DAU/MAU describes activity density across a period’s audience. Retention follows a defined cohort, such as users acquired on the same date, into later usage. Rapid acquisition changes MAU composition; a falling ratio does not automatically prove existing customers are becoming less retained.
Break results down by new versus existing users, acquisition source, and plan where appropriate. OS comparisons also need matched region, period, activity, and identity. Check iOS and Android distribution and measurement conditions before attributing an observed difference to the operating system itself.
Keep an evaluation record
- Define the activity event, success condition, and deduplication unit.
- Record time zone, calendar or rolling window, and the exact ratio formula.
- Document changes to consent, User-ID, and reporting identity.
- Report DAU, MAU, and the ratio together to show which component changed.
- For notification tests, check the intended outcome and guardrails such as opt-outs or churn.
- Treat before-and-after comparisons as potentially affected by seasonality and acquisition mix, rather than proof of causation.
DAU is comparable only when the activity, audience, and time window are clear. Keep absolute counts and the product’s expected usage cadence beside the ratio.