DAU/MAU:
Define Active Before You Calculate

A product dashboard shows 1,000 monthly active users and 100 daily active users. The DAU/MAU ratio is 10%. Is that a warning? For a team messaging product used throughout the working day, perhaps. For software that helps a manager close the books once a month, the same number could describe normal, successful use. The arithmetic is simple. The decision depends on what a user came to do, how often that need occurs, and what the dashboard calls “active.”

DAU means daily active users; MAU means monthly active users. Each is a count of distinct users who qualify as active during a stated window. Dividing DAU by MAU produces a measure often called user stickiness: the share of the broader active population present in the narrower window. Google Analytics describes DAU/MAU alongside DAU/WAU and WAU/MAU as comparisons between shorter and longer periods. The ratio answers a useful frequency question, but only after the activity, identity, and windows behind both counts are clear.

A sensible reading begins with the job the product performs. If the job is genuinely daily, a low ratio may point to weak repeat use. If the job occurs around a weekly order or monthly review, pushing the ratio upward can reward visits that add little value. I would choose the expected usage interval first, define a meaningful action second, and then use DAU/MAU to see whether observed behavior fits that expectation.

The formula is easy; the population needs a definition#

For one observation day, the calculation is DAU ÷ MAU × 100. Suppose 120 distinct users performed the chosen action in the most recent day and 1,000 distinct users performed it at least once in the corresponding 30-day window. The result is 12%. In this illustration, 12% of the 30-day active population was active on that particular day. It does not say that every monthly user was active on 12% of days.

Google Analytics defines the windows in its user stickiness report as the last 24 hours for DAU, the last seven days for WAU, and the last 30 days for MAU. Its DAU/MAU reading therefore compares a current 24-hour count with a rolling 30-day count, rather than necessarily comparing a calendar day with a calendar month. Those are Google Analytics’ stated report definitions. A team calculating its own metric might instead use calendar days inside a completed calendar month. Both can be useful, but a percentage from one method should not be treated as identical to a percentage from the other.

The numerator and denominator must describe the same kind of user and action. If DAU counts people who completed a transaction today while MAU counts everyone who opened the app during the month, the quotient has a mathematical value but no clean frequency interpretation. The daily users are selected by a harder test than the monthly users. Similarly, a customer account counted once in MAU cannot be compared straightforwardly with individual employees counted in DAU. Amplitude’s product metrics guide defines active-user counts around unique users who perform a specified event and defines stickiness as daily active users divided by monthly active users.

When the daily window lies inside the monthly window and both counts use the same identity and activity rule, DAU cannot exceed MAU: everyone counted today also belongs to the longer window. A result above 100% is a prompt to check the query, not a sign of extraordinary engagement. It may mean the periods do not nest, the filters differ, or the numerator and denominator use different user identifiers. If MAU is zero, the ratio is undefined; recording it as 0% would imply a measured absence of daily activity among monthly users when there is no monthly population to divide by.

A day’s ratio and a month’s average answer different questions#

A single DAU/MAU reading tells you what fraction of the recent monthly population was present on that day. To describe a typical day across a completed period, calculate DAU separately for every day, average those daily counts, and divide by the number of unique active users in that same period. Mixpanel describes this average-DAU-over-MAU calculation for a period, and its discussion of stickiness walks through the daily counts before dividing by the period’s unique users.

For an illustrative 30-day period, imagine 1,000 unique users completed the qualifying action at least once. The sum of the 30 daily active-user counts is 6,000, so average DAU is 200. Average DAU divided by the period’s 1,000 MAU is 20%. That means 200 of the 1,000 monthly users were active on an average day. The same arithmetic also means the population generated an average of six active days per monthly user: 6,000 user-days divided by 1,000 users. It does not mean each person used the product on exactly six days.

This second interpretation is available only when the numerator sums days from the same fixed period whose unique users form the denominator. A snapshot using today’s DAU and a rolling 30-day MAU cannot tell you the average number of active days per user across those 30 days. It can move sharply with the day of the week even if the underlying monthly habit has not changed. A weekday-focused product, for example, may show a lower reading on Sunday simply because Sunday is outside the usual workflow. The inference follows from the window arithmetic; it is not a diagnosis of that product’s customers.

Even a period average hides the shape of use. In the 1,000-user, 6,000-user-day illustration, all 1,000 people might be active on six days each. Or 200 might be active on 22 days while the other 800 are active on two days each: 4,400 plus 1,600 still equals 6,000 user-days and the same 20% ratio. A third possibility is that everyone is active on the same first six days and nobody returns for the rest of the period. The headline is unchanged, yet the product questions are very different. Mixpanel uses contrasting activity patterns to show how the same stickiness figure can mask clustered usage and different user groups.

That is why I would read a monthly average next to the daily series and a distribution of active days per user. The average says how much activity occurred relative to the monthly population. The series shows when it occurred. The distribution shows whether repeat use is broad or concentrated in a small group. These views turn a plausible headline into a question a product team can actually answer.

“Active” should mean progress on the user’s job#

The most consequential choice is often the event that qualifies a user as active. Counting an app open is easy. It may also count someone who arrived through an accidental tap, looked at an error, or left before doing anything useful. Amplitude recommends selecting a critical event that reflects use of the product’s value rather than treating an app open as sufficient. Mixpanel likewise frames MAU around unique users performing a meaningful action.

For an illustrative team messaging product, sending or reading a message in a work conversation could be a stronger qualifying action than opening the home screen. For an illustrative invoice tool, creating, reviewing, or sending an invoice might be closer to the job than merely signing in. Those are proposed definitions, not universal standards: a person may receive value from reading without sending, and a login may be a necessary step in a workflow but weak evidence that the workflow succeeded. The product team should choose the event from the value the user seeks, then check whether the event really captures that value.

The definition should also be stable enough to compare periods. Suppose a dashboard counted logins for six months and then switches to completed invoices. A fall in DAU or MAU after the change may reflect the narrower rule, even if user behavior is unchanged. Keep a record of the event name, any filters, the first date the event was collected, and the date the reporting definition changed. Where possible, recalculate earlier periods using the new rule before calling the new chart a trend. If that cannot be done, mark the break plainly.

A single qualifying action may still be too broad. A workflow may include both an intended outcome and repeated attempts caused by failure. If a customer must reopen a page each day to retry an unsuccessful submission, daily activity could rise while the experience deteriorates. A stickiness ratio counts days with qualifying activity; it does not tell you whether the activity was welcome. The accompanying question is whether people completed the task they came for. This is especially important when an intervention meant to raise DAU, such as a notification or reminder, can create an extra visit without making the core task easier.

An off-the-shelf report also has its own definition. Google Analytics’ “active users” metric follows its published engagement rules, including engaged sessions and certain collected events; it is not automatically the same as a product team’s custom “completed invoice” event. Google describes how it populates its active-user metric. If the question is how often people complete invoices, I would calculate that action’s active-user counts rather than relabel a general engagement count as completed work.

Decide how often success ought to recur#

DAU/MAU is most informative when the product offers a reason to return on many days. A communication tool used to coordinate ongoing work is a plausible candidate. A tool used primarily for a monthly close is not. Amplitude advises choosing a stickiness interval from the product’s expected usage frequency and gives weekly-to-monthly measurement as an option for a less daily workflow. The choice should be made from the user’s task, not from which ratio makes the dashboard look strongest.

For a product used weekly, WAU/MAU asks what share of the recent monthly population was active in the recent week. DAU/WAU asks what share of the weekly population was active in the recent day. Google Analytics reports both ratios alongside DAU/MAU. A team that expects a single useful visit each week may prefer WAU/MAU to DAU/MAU; a high daily ratio is not necessary to fulfill that job. If users are supposed to act only once a month, even weekly activity may be an awkward success target. Track whether the monthly task is completed and whether users return when the next cycle arrives.

The denominator is also a product decision. An account-based service may have many licensed seats but only a subset with work to do in a particular month. MAU measures the users who met the activity rule in the window; it is not the number of subscribers, paid seats, registered accounts, or potential users. Treating a high DAU/MAU ratio as proof that most customers are engaged can therefore be a serious mistake. Ten daily users out of ten monthly users is 100% stickiness, but it says nothing about the thousands of people who never became monthly active. Mixpanel’s examples make the same point by contrasting a 100% ratio for one user with a 100% ratio for a much larger population.

A practical cadence choice has a cost. If you switch the headline from DAU/MAU to WAU/MAU, you give up the simple daily frequency signal. In return, you get a measure closer to the behavior the product actually invites. I would keep the underlying DAU and MAU counts available, but judge the product by the interval that matches the job. A daily ratio can still help with staffing, load, or release monitoring even when it is a poor score for user benefit.

A rising ratio is not automatically better#

DAU/MAU can rise because more monthly users come back today, but it can also rise because MAU shrinks. Imagine a product with 200 DAU and 1,000 MAU, a 20% ratio. Next month it has 180 DAU and 600 MAU, a 30% ratio. The headline improved by ten percentage points while both active-user counts fell. The arithmetic alone cannot tell whether occasional users left, a campaign ended, an identity rule changed, or a smaller committed group remained. I would inspect the counts before celebrating the percentage.

The reverse deserves care as well. Suppose a campaign brings in many first-time users, lifting MAU immediately, while only some have had time or reason to return on the observation day. DAU/MAU may fall even while the product reaches more people. The crucial follow-up is whether those new users later perform the meaningful action again at the expected interval. Segmenting new and established users helps keep a change in audience mix from being mistaken for a change in each user’s habit.

Here is a compact way to read the direction of the component counts. These are possible interpretations, not diagnoses of any particular product.

This is also why DAU/MAU and retention should not be treated as synonyms. Stickiness describes frequency within the windows you chose. Retention asks whether a defined group returns in a later period. Two consecutive months can each have 1,000 active users and the same 20% average DAU/MAU while containing entirely different people. In that illustration, within-month frequency is identical, but none of the first month’s users returned in the second. To learn whether customers stay, follow a cohort of the same users across periods and ask whether they perform the meaningful action again. The ratio by itself cannot answer that question.

Compare like with like before borrowing a benchmark#

“What is a good DAU/MAU ratio?” has no useful single-number answer. A daily communication product, a weekly grocery ordering service, and a monthly reporting tool offer different reasons to come back. Even two products in the same category can use different qualifying events, user identifiers, and time windows. Amplitude explicitly ties the preferred interval to expected product use, while Mixpanel cautions that a stickiness figure needs the activity pattern and population behind it.

Published category averages may be useful as background, but I would not adopt one as a target without matching its definition to the product. Mixpanel’s MAU guide, for instance, reports different DAU/MAU figures across industries and regions; those numbers are descriptions of its reported data, not a threshold every product should meet. The guide presents its figures by vertical and region. A better comparison begins within the same product: the same event, the same identity policy, the same calendar treatment, and similar user groups over time. If any of those changes, explain the change before attributing movement to the product.

Identity matters because active users are unique users, not events. If one person is counted under two device identifiers, the dashboard may see two users; if several people share one account identifier, it may see one. Either choice can change DAU, MAU, and their ratio. A documented identity rule is therefore part of the metric’s meaning. Likewise, recording the timezone and the boundary between days matters for a user who completes work near midnight: the same session might be placed on different dates under different reporting settings. Those are consequences of counting unique users in time windows, and they matter most when the team compares small changes.

When comparing features, the denominator needs equal care. A feature used by only a subset of customers should not be judged solely by a whole-product DAU/MAU ratio. A feature-specific count can show whether eligible users return to that feature, but the eligibility rule must be visible. Amplitude’s guide notes that stickiness can be examined for specific features as well as a product. If an invoicing feature is available only to account administrators, including every invited employee in its comparison population would make useful administration work appear artificially rare.

Use the ratio to choose a product question, then a change#

When the ratio is lower than expected for a genuinely daily job, I would first locate the missing repeat behavior. Do users complete the qualifying action once and stop? Do they return to the product but fail before the useful step? Are new users being mixed with people who have had weeks to form a routine? These are different problems. A broad campaign to increase logins is a poor response until the team knows which one it faces. It can raise a superficial DAU count while leaving the user’s work unfinished.

For users who return but fail to complete the task, the next change should address the obstacle in that task: an unclear step, a missing input, or a slow handoff, if the product data and user feedback point there. For users who complete the task successfully and have no reason to return until next week, the answer may be to stop asking for daily use. In each case, I would watch the count of completed tasks alongside active users. If the completion count improves while DAU/MAU stays flat, that may be a better outcome than a ratio increase built on extra openings.

A high ratio deserves the same scrutiny. It can reflect a product that fits a daily routine, but it can also reflect repeated low-value actions or a small surviving audience. Check the number of monthly users, the distribution of active days, and the completion of the core task. If only a small set of customers uses the product every day while most newly acquired customers never return, improving the experience for that group might be valuable, but calling the whole product sticky would overstate what the ratio says. Mixpanel’s stickiness examples show how a high figure can arise from a tiny population and how averages can conceal sharply different groups.

Once an actual problem is identified, make a change that matches it and read the same measures again. For an illustrative invoice product, a reminder timed to a pending approval could help someone finish an already intended task; a daily generic reminder would merely create more chances to open the app. For an illustrative messaging product, removing a recurring obstacle to reading or responding may matter more than prompting extra visits. These are choices to test against task completion and repeat use, not claims that any particular intervention will work.

The trade-off is that a more meaningful activity definition and a more appropriate interval may produce a less flattering headline. That is a reasonable cost. DAU/MAU is useful when it states exactly who returned, for what action, and over which windows. With those terms fixed, the ratio can show whether frequent use is spreading or fading. Without them, a number that looks precise can lead a team to optimize visits while missing the work users came to finish.