What is Monthly Active Users (MAU)?
– Monthly Active Users (MAU) is a key performance indicator (KPI) that counts the number of unique users who used a website, app, or service during the past 30 days (or during a given calendar month), according to the definition of “active” the business chooses. Companies typically identify users by an ID, email, username, device identifier, or an authentication token.
– Purpose: MAU gauges the size and trend of an active user base, helps evaluate marketing and product performance, and is often cited by investors as an indicator of a platform’s reach and growth.
Key takeaways
– MAU measures reach, not depth: it counts unique users without directly measuring how engaged they were.
– Definitions vary: there are no universal standards for “user” or “active,” so MAU figures are only comparable when methodologies are aligned.
– Use MAU with other metrics (DAU, retention, engagement time, ARPU, cohorts) to get a fuller view of product health.
(See Investopedia and company filings for examples of differing definitions.)
Who uses MAU, and how?
– Product teams: to track growth, seasonality, and the effect of features or experiments.
– Marketing: to measure acquisition effectiveness and campaign lifts.
– Finance & investors: to evaluate business scale and market opportunity.
– Executives: to set targets, prioritize initiatives, and communicate performance externally.
Fast fact
– Large platforms have changed how they report “users.” Meta (Facebook) defines MAU as logged-in registered users who interacted in the last 30 days; Meta later introduced “Family” metrics (monthly/daily active people) to deduplicate across platforms. X/Twitter moved to monetizable DAU (mDAU) to reflect ad-relevant users. These differences make direct comparisons difficult. (Meta Form 10‑K; X Form 10‑K; Investopedia)
Limits of MAU
– No quality signal: MAU does not reveal session length, frequency, or feature use.
– Inconsistent definitions: platforms may count logged-in users, ad-monetizable users, or any visitor, producing apples-to-oranges numbers.
– Multiple accounts and cross-platform users: one person with several accounts inflates MAU; cross-platform users can be double-counted unless reconciled.
– Susceptible to bots and fraud if not filtered.
– Can hide churn: a flat MAU might mask high churn offset by high acquisition.
Fast fact
– Meta’s switch to “Family” metrics increased their reported active-person totals (because it deduplicates users across Facebook, Instagram, WhatsApp) — showing how metric design changes can materially alter reported reach.
What is considered an “active user”?
There is no single standard. Common options include:
– Any visit or page view within the period.
– Any authenticated/login event within the period.
– Any “meaningful” action (post, comment, purchase, watch > X seconds).
– Monetizable activity only (able to be shown ads or made purchases).
Best practice: define “active” in a way that matches the business model (e.g., e‑commerce should weight purchases, social apps may weight content creation or consumption) and document it carefully.
How to calculate MAU (practical guidance and formulas)
Two common approaches:
1) Snapshot rolling 30-day MAU (most common)
– Definition: Count of unique users who met your “active” criteria during the trailing 30 days.
– Formula: MAU_t = count(unique user_id where last_active_date >= t – 29 days)
– Use for near-real-time monitoring and trend detection.
2) Monthly average MAU (reported as an annualized average)
– Definition: If you report “average MAU for the year,” compute the average of each month’s unique actives.
– Formula: Average MAU (12 months) = (U_Jan + U_Feb + … + U_Dec) / 12
– Use for annual reports and smoothing seasonal swings.
Example (simple):
– If Jan had 1,000 unique actives, Feb 900, Mar 1,100, …, then Average MAU = (sum of the 12 monthly uniques)/12.
MAU and DAU — the relationship
– DAU (Daily Active Users): the unique users who were active in a single day.
– Stickiness metric: DAU/MAU ratio measures frequency of use. Example interpretations:
– DAU/MAU ~ 0.2 (20%) = users come ~6 days/month (low stickiness for social apps).
– DAU/MAU > 0.5 = highly habit-forming product.
– Use DAU and MAU together to understand both breadth (MAU) and depth/frequency (DAU).
Practical steps — how to define, measure, and report MAU (step‑by‑step)
1) Define “active” aligned to product & business model
– Decide if active = any visit, logged-in session, core action (e.g., post, watch >10s), or monetizable event.
– Document precisely (what event(s), time window, inclusion/exclusion rules).
2) Instrument tracking reliably
– Implement stable user identifiers (auth IDs, hashed emails, device IDs) and server-side logging to avoid client-side loss.
– Log events that meet your active criteria with timestamps.
3) Deduplicate and filter noise
– De-duplicate users across devices/accounts where appropriate (use identity graphs, hashed identifiers).
– Filter bots, test accounts, and internal traffic.
4) Choose a computation method
– Rolling 30-day unique count for live monitoring.
– Calendar-month unique count if you report by month.
– Annual average of monthly uniques if smoothing is needed.
5) Segment and cohort
– Break MAU down by geography, platform (iOS/Android/web), acquisition channel, cohort by signup month, and by account type (free vs paid).
– Track new vs returning users.
6) Combine with complementary metrics
– DAU/MAU (stickiness), average session length, sessions per user, retention/ cohort analysis, churn rates, ARPU, LTV.
– Use funnels and event-level engagement (e.g., conversion from visit → key action).
7) Monitor for anomalous shifts
– Spike/decline investigation: check for tracking bugs, policy changes, UI experiments, bot activity, or acquisition campaigns.
– Version your methodology: when you change the MAU definition, disclose the change and, if possible, provide backward-adjusted series.
8) Report transparently
– Always publish the exact definition used, the date range, and any known exclusions or adjustments.
– For investors and public reporting, disclose changes in measurement methodology (as Meta and X have done in filings).
Improving MAU insights — alternatives and complements
– DAU/MAU ratio (stickiness).
– Engagement metrics: sessions per user, average session duration, time spent in app.
– Retention and cohort analysis (e.g., 1-day, 7-day, 30-day retention).
– Monetizable user metrics (mDAU) when revenue depends on ad impressions.
– “People” metrics that deduplicate across properties (Meta’s Family MAP/DAP) to avoid double-counting the same person with multiple accounts.
– Activity-weighted MAU (score users by actions to reflect quality of engagement).
How investors and analysts should treat MAU
– Ask for the definition and any changes over time.
– Compare like-with-like: only compare MAU between companies when methodologies are comparable (e.g., both report logged-in, ad-monetizable users).
– Combine with revenue per user (ARPU), retention, churn, and engagement to assess monetization and growth quality.
– Watch for metric engineering (e.g., changing the definition to boost headline MAU) and request reconciliations.
Common pitfalls and how to avoid them
– Pitfall: comparing unaligned MAU definitions. Fix: require methodology disclosure.
– Pitfall: counting bots and test accounts. Fix: invest in bot detection and filtering.
– Pitfall: multiple accounts per person inflate reach. Fix: dedupe across platforms or use “people” metrics where feasible.
– Pitfall: relying only on MAU. Fix: use it as one input in a dashboard that includes frequency and monetization metrics.
The bottom line
MAU is a useful, widely-understood KPI for tracking the size of an active user base, but its value depends entirely on definitions and context. It should be computed and reported transparently, combined with DAU and other engagement, retention, and monetization metrics, and adjusted or replaced with deduplicated “people” metrics (or monetizable-user metrics) when platform centralization or multiple accounts make plain MAU misleading.
Sources and further reading
– Investopedia: “Monthly Active Users (MAU)” (source material summarized here).
– Meta. Form 10‑K (discussion of MAU, DAU, Family MAP/DAP and methodology; see relevant annual filings).
– Meta. 2015 Annual Report (notes on MAU definition changes).
– X (formerly Twitter). Form 10‑K (discussion of mDAU definition).
If you’d like, I can:
– Produce an MAU measurement checklist you can paste into your analytics playbook.
– Create sample SQL queries for computing rolling 30‑day MAU from event logs.
– Draft a short investor-quality disclosure paragraph describing your MAU methodology. Which would you prefer?