Measure monthly active users (MAU) as the number of distinct player identities that trigger at least one defined gameplay activity event during a clearly specified period. A useful figure is not just a count: publish the game and platform scope, time window, activity event, identity and deduplication rules, timezone, and treatment of delayed or offline events alongside it.
Define what your MAU counts
There is no single vendor-independent MAU implementation. Analytics products use different windows, activity signals, and identity rules, so two figures bearing the same label may not be comparable. Decide these elements before calculating the metric.
- Title and platform scope: Say which game is included and whether the figure covers one platform or several.
- Activity: Count a signal that represents playing, such as a recorded session or a specified gameplay event. An app open, login, and session are not interchangeable definitions.
- Identity: Choose the stable player, account, or analytics ID used to count unique players, and explain how guest IDs and account links are handled.
- Time: State the window, timezone, inclusive start, and exclusive end.
- Data completeness: Explain whether offline activity and events arriving late can change a reported period.
Choose a calendar month or a rolling window
Use a calendar month when reporting month-by-month totals tied to fixed dates. Use a rolling window when the audience count should update each day to cover the preceding number of days. Label the choice explicitly; “monthly” alone does not say which one you mean.
| Window | How it works | Example documented definition |
|---|---|---|
| Calendar month | Count unique qualifying players between the month’s fixed start and end boundaries. | GameAnalytics lists calendar-month unique users; PlayFab reports monthly totals for a given month’s first and last days. GameAnalytics metrics; PlayFab analytics reports. |
| Rolling 30 days | Count unique qualifying players in the 30 days before the report time; the population changes daily. | GameAnalytics documents rolling-30-day unique users, and PlayFab defines rolling-thirty-day MAU using players who logged in during the previous 30 days. GameAnalytics metrics; PlayFab analytics reports. |
| Rolling 28 days | Count unique users who meet the platform’s activity rule in the preceding 28 days. | Google Play Console defines MAU as users who opened the app in a rolling 28-day period. Google Play Console metric definitions. |
A rolling 28-day figure is not the same measure as rolling 30-day MAU. Calendar-month totals also have different boundaries and durations from a rolling window. When comparing dashboards, match or recalculate the period rather than treating the numbers as equivalent.
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Pick an activity event that answers your question
For a game’s active-player audience, a recorded gameplay session or a specific play event is generally a more direct signal than installation, registration, or a store-page view. The exact event depends on how the game is instrumented and what “active” is meant to represent.
Published platform definitions illustrate why the event must be disclosed: Google Play Console uses an app open; PlayFab’s rolling-thirty-day report uses a login; Unity Analytics describes session-based activity in its FAQ. These vendor-specific rules should not be silently substituted for one another or presented as a universal definition.
- Use a session-start event if your reporting question is whether a player began a session.
- Use a defined gameplay event if a session can begin without meaningful play and you want a stricter activity test.
- Use a login only if login is the intended criterion; it may not capture the same behavior as a gameplay session.
Document event names and any exclusions, such as test accounts or internal traffic, in the metric specification so the same rule is applied each reporting period.
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Decide how one player is identified
Count each unique identity once per game and window. A persistent account or player key is usually more suitable than a device identifier when your system can maintain it consistently. If your analytics system uses anonymous IDs, define what happens when a guest account is linked to a registered account or an ID is reset.
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Deduplicate across devices or platforms only when the game can reliably connect the activity to the same player. If it cannot, say so: one person may contribute more than one identity to the total. A Zynga SEC filing from 2012 described this as a possible issue when players used multiple games or platforms, but it is a dated, company-specific example rather than an industry-wide rule. Zynga SEC filing.
Count qualifying players consistently
Once the scope and rules are fixed, filter the event data to the chosen game, activity events, and time window, then count distinct player IDs. For a calendar or rolling interval, use a half-open range: include the start timestamp and exclude the end timestamp. This prevents an event exactly at a boundary from being counted in both adjacent periods.
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SELECT COUNT(DISTINCT player_id)
FROM events
WHERE game_id = :game
AND event_name IN (:qualifying_events)
AND event_time >= :window_start
AND event_time < :window_end;This query is an implementation pattern, not a platform-specific requirement. Ensure that the selected IDs, event names, and timestamps actually implement the definition you publish.
Standardize timezone and late events
Use one timezone and boundary convention for every report. GameAnalytics says its metrics use UTC dates and hours, and PlayFab says its reporting timestamps use UTC. UTC is a practical convention for consistent cross-region reporting, but whichever convention you choose should be stated and applied uniformly.
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Publish the definition with the number
A useful label makes the metric reproducible. Adapt this template and replace every bracket with the game’s actual instrumentation and policy:
MAU ([calendar month / rolling 30 days]) for [game and platform scope] = distinct [account/player/analytics IDs] with at least one [qualifying activity event] from [window start, inclusive] to [window end, exclusive], using [timezone]. [Cross-platform/device deduplication rule]. [Offline and late-event policy].
For example, a team could report: “Rolling 30-day active players for Game A: distinct account IDs with at least one session_start from 00:00 UTC on [start] through, but not including, 00:00 UTC on [end]. Cross-platform accounts are deduplicated; unlinked guest IDs are counted separately.” Use this wording only if it matches the actual implementation.
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Interpret MAU alongside other measures
MAU describes audience breadth over a window; it does not say how often each player returns, whether newly acquired players stay, or how much the audience spends. Pair it with DAU and a precisely defined DAU/MAU ratio when discussing activity frequency, and with cohort retention when asking whether players return after installation.
Even the ratio needs a stated denominator and period. GameAnalytics describes DAU/MAU as today’s users divided by the rolling 30-day population; Google Analytics Help describes a DAU/MAU comparison of the last 24 hours against the last 30 days. Unity describes DAU per MAU as the percentage of MAU who played on a particular day. These formulations are related, but align their windows and definitions before comparing results. GameAnalytics metrics; Google Analytics active users; Unity Analytics Data Explorer.
For retention, use cohort measures such as D1, D7, or D30 rather than inferring return behavior from MAU alone. Roblox Creator Docs discusses these retention intervals. Roblox retention documentation.
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