DAU / MAU

DAUGlossaryDaily Active Users (DAU)The count of unique users who were active in the app on a single calendar day. The most-watched daily engagement signal in mobile apps. (Daily Active Users) and MAUGlossaryMonthly Active Users (MAU)The count of unique users active in the app across a 30-day window. The denominator of stickiness and the top-of-funnel reach metric. (Monthly Active Users) are the baseline engagement metrics for any mobile app — the count of unique users active in a given window. They sit alongside install volume as the canonical top-of-funnel health metrics, and feed every downstream calculation (DAU/MAU ratioGlossaryStickiness (DAU / MAU)The ratio DAU ÷ MAU — how often a typical monthly user comes back within the month. 0.5 = average user is active 15 days out of 30., ARPDAUGlossaryARPDAU (Average Revenue Per Daily Active User)Total revenue in a period divided by the average daily active users in that period — the per-day-per-active-user revenue rate., daily-cohort retentionGlossaryRetention (D1 / D7 / D30)The percentage of users from a cohort who return to the app N days after install. D1 = day-1 retention, D7 = 7-day, D30 = 30-day. The single most important non-reven...).

"Active" needs a specific definition. The most common: "opened the app at any point in the day" — a sessionGlossaryApp SessionA single period of continuous user activity in an app, from open to close (or background past an inactivity timeout) — the atomic unit on which most engagement metri... start event. Slightly stricter: "performed a meaningful action" — sent a message, completed a workout, played a level, did something the product values. The strict definition gives better signal but is harder to compare across products and categories. The lenient definition is the industry default. Pick one, document it, stick to it across dashboards — switching definitions mid-quarter breaks every trendline.

That 14% catalog median is much lower than most "good stickiness" blog posts suggest. The 30-60% numbers people quote describe top-percentile habit apps (messaging, mainstream social, banking); the typical app sees one of every seven monthly users come back on any given day. Use the distribution below — and your own category median — as the reference, not headline benchmarks from category leaders.

Reading DAU vs MAUGlossaryDAU vs MAUDAU counts unique users active in a day; MAU counts them across a month. The same product reports both — and their ratio, DAU/MAU, is the standard stickiness metric. together is the key skill:

• DAU and MAU both growing: healthy growth, new users finding the product and existing users staying engaged.
• DAU growing without MAU growing: existing users are getting more engaged. Often good (product improvements, feature adoption), sometimes warning (small core driving more usage while broader audience plateaus).
• MAU growing without DAU growing: new users coming in but not sticking past a few visits. Warning — usually means acquisition has scaled but activationGlossaryActivationThe percentage of installs who complete a defined "activation event" — the action that converts a passive install into an active user. / habit-formation hasn't kept up.
• DAU shrinking, MAU stable or growing: users are visiting less frequently. Bad — usually means product is losing relevance or notifications / re-engagementGlossaryRe-EngagementMarketing activity aimed at bringing lapsed users back to the app — through push, email, retargeting ads, deep-linked notifications. are failing.

Watch the trendline, not the absolute number. The headline DAU number depends heavily on app category, audience size, and ad spendGlossaryAd SpendThe total dollar amount deployed into paid user acquisition over a defined period — the input variable that drives all UA volume. — comparing yours to a competitor's is mostly meaningless. What matters is your own trend vs prior periods. A 5% DAU drop in a month is often more informative than absolute level.

MAU has a 28-day vs calendar-month subtlety. Calendar-month MAU varies with month length (28-31 days) and weekday alignment, which adds noise. Trailing-28-day MAU is cleaner — every data point has the same window size. Most analytics platforms expose both; trailing-28 is the more analytically rigorous choice.

The distribution shape exposes how rare daily-habit products actually are. The bulk of catalog apps sit in the 5-15% range — typical "weekly check-in" engagement. Reaching 20-30% requires structural daily anchors (notifications that work, content that refreshes, mechanics that reward returning); 30-50% requires a daily essential function; 50%+ is reserved for messaging, finance, and a handful of dominant social products.

Social leads at both ends — highest median stickiness and the widest top-decile gap. Games sit at the bottom of the median table, which is striking given they had the highest session DURATION earlier — gamers play hard but not every day. Productivity surprises by ranking second; the "weekday work tool" pattern produces real daily habit even though individual sessions are short.

Does stickiness vary by country?

You might expect daily-habit intensity to differ by market — heavier competition for attention in some regions, lighter mobile usage in others. It doesn't.

Median DAU/MAU stickiness sits at ~14% in every market measured — 13.8% in Japan and Brazil, 14.7% in Germany, with the US dead-center at 14.1%. The top-decile band is just as tight (31-34% everywhere). Like [[retention]] and [[churnGlossaryChurnThe rate at which users stop using an app (general churn) or cancel a subscription (subscription churn) over a time window — retention's mathematical complement.]], engagement intensity is a structural property of the product category, not the country: what moves stickiness is the app's daily-anchor design — notifications that land, content that refreshes, mechanics that reward returning — not the geography of its users.