TL;DR
DAU (daily active users) is the number of unique users who perform at least one qualifying action in a product within a single calendar day. WAU (weekly active users) is the number of unique users who perform at least one qualifying action in a product within a seven-day window. MAU (monthly active users) is the number of unique users who perform at least one qualifying action in a product within a calendar month or a rolling 28- to 30-day window.
DAU, WAU, and MAU count how many unique users actually use your product in a day, a week, or a month. They are the baseline active-user metrics behind almost every engagement and growth conversation. This guide defines each one, gives the formula behind it, explains the DAU/MAU stickiness ratio with benchmarks, and shows how to decide which metric should lead for your product.
DAU/MAU ratio: divide average daily active users by monthly active users and multiply by 100. The result, often called stickiness, is the share of your monthly users who show up on a typical day. A product with 3,000 average DAU and 12,000 MAU has a DAU/MAU ratio of 25%, so its average monthly user is active on roughly 7 or 8 days a month.
DAU, WAU, MAU glossary: what each term stands for
| Term | Stands for | Formula | When to use it |
|---|---|---|---|
| DAU | Daily active users | Distinct users with at least one qualifying event in one day | Products built for daily use: messaging, social, games, collaboration |
| WAU | Weekly active users | Distinct users with at least one qualifying event in a 7-day window | Products used a few times a week, and to smooth out weekday and weekend swings |
| MAU | Monthly active users | Distinct users with at least one qualifying event in a calendar month or a rolling 28- to 30-day window | Reach and billing-level reporting, and products with occasional but regular use |
| DAU/MAU ratio | Daily active users divided by monthly active users (stickiness) | (Average DAU ÷ MAU) × 100 | Measuring how often monthly users come back |
| DAU/WAU ratio | Daily active users divided by weekly active users | (Average DAU ÷ WAU) × 100 | Checking how many days a week weekly users show up |
| WAU/MAU ratio | Weekly active users divided by monthly active users | (Average WAU ÷ MAU) × 100 | Checking whether monthly users return at least weekly |
Every row depends on the same two choices: what counts as a qualifying event, and how a user is identified. Change either one and all six numbers move, so write both down next to the metric. In each formula "users" means unique people, counted once per window however many sessions or events they generate.
What is DAU?
DAU (Daily Active Users) is the number of unique users who perform at least one qualifying action in your product within a single day.
Formula: DAU = count of distinct users with at least one qualifying event during one calendar day (in a fixed timezone).
A single "DAU" is simply one unique user counted as active that day — if the same person opens your app five times on Tuesday, they still count once. Deduplication per window is what separates active-user metrics from raw event or session counts.
The definition hinges on what counts as "active." A session start is the loosest version; a meaningful action — sending a message, running a report, completing a task — is a far better signal. Whatever you pick, keep it consistent, because every other active-user metric in this guide inherits it.
What is MAU?
MAU (Monthly Active Users) is the number of unique users who perform at least one qualifying action in your product within a month — either a calendar month or a rolling 28–30-day window.
Formula: MAU = count of distinct users with at least one qualifying event during the month. A rolling 28-day window avoids the distortion of months having different lengths and different weekday counts.
Because it uses the widest window, MAU is the most forgiving of the three metrics — a user who shows up once a month counts the same as one who shows up daily. That makes it a good measure of reach and a poor measure of habit, which is exactly why the DAU/MAU ratio below exists.
DAU vs MAU: what the pair tells you that neither does alone
DAU and MAU together tell you how often your monthly audience shows up, which neither number can show alone. DAU alone is a daily headcount that swings with weekdays, launches, and campaigns. MAU alone is reach: it counts a user who came once the same as one who came every day. Read as a pair, DAU and MAU become a frequency measure, and the way the two lines move against each other shows whether growth comes from new reach or from a deeper habit.
Take two products that each report 100,000 MAU. The first averages 40,000 DAU and the second 8,000. Their reach is identical, but the first product's monthly users are active about 12 days a month and the second's about 2.4. Same MAU, very different businesses.
| What you see | What it usually means | What to check next |
|---|---|---|
| MAU up, DAU flat | Reach is growing, but new users are not forming a habit; the DAU/MAU ratio falls | Activation and first-week retention of the newest cohorts |
| DAU up, MAU flat | The same audience is returning more often; the ratio rises | Which feature or change raised frequency, so you can protect it |
| Both up, ratio steady | Growth with stable engagement | Whether the newest acquisition channels retain as well as the old ones |
| DAU down, MAU flat | Users still visit monthly but less often: an early warning before MAU drops | Frequency by cohort and by platform |
| Both down | Churn is outpacing acquisition | Cohort retention curves, to find where users leave |
Two rules keep the pair honest. Use the same definition of an active user for both numbers, and compute MAU over a window that contains the days you average for DAU. A DAU counted on "opened the app" set against a MAU counted on "completed a key action" produces a ratio that means nothing.
What is the DAU/MAU ratio?
The DAU/MAU ratio — often called stickiness — is the share of your monthly active users who show up on a typical day, calculated by dividing DAU by MAU.
Formula: DAU/MAU ratio = (average DAU over the month ÷ MAU) × 100. A ratio of 20% means the average monthly user is active about 6 days out of 30.
Commonly cited benchmark bands: under 10% is normal for low-frequency products (tax software, travel booking); 10–20% is a typical range for SaaS and consumer apps; 20%+ signals a genuine habit; 50%+ is rare, messaging-app territory. Compare against your own category and cadence rather than a single universal target — a 12% ratio can be excellent for a product designed for weekly use.
One caveat: use an averaged DAU (not a single day's) and note whether MAU is calendar-month or rolling, since weekday/weekend swings and month length both move the ratio.
DAU, WAU, and MAU formulas at a glance
| Metric | Window | Formula |
|---|---|---|
| DAU | One day | Distinct users with ≥1 qualifying event that day |
| WAU | Seven days | Distinct users with ≥1 qualifying event in the 7-day window |
| MAU | Calendar month or rolling 28–30 days | Distinct users with ≥1 qualifying event in the window |
| DAU/MAU ratio | One month | (average DAU ÷ MAU) × 100 — the stickiness percentage |
DAU vs WAU vs MAU: which to use when
Use DAU when your product is meant to be a daily habit, WAU when the natural cadence is a few times a week, and MAU when usage is occasional but should be consistent month over month.
The three metrics nest: everyone counted in DAU is also in that week's WAU, and everyone in WAU is in that month's MAU, so DAU ≤ WAU ≤ MAU by definition. That nesting is what makes the ratios between them meaningful — DAU/MAU measures daily stickiness, and WAU/MAU tells you whether your monthly audience shows up at least weekly. Watching the gaps between the three lines is often more informative than watching any single line.
Choosing DAU as the primary metric
DAU is crucial for products that rely on frequent, daily engagement. If your product is something users interact with multiple times a day - like social media apps or messaging platforms - DAU is a key metric. It shows how often users return and engage on a daily basis. But if you offer a product that doesn't require frequent use (like a task manager or a tool for occasional use), DAU might not be as critical.
Choosing WAU as the primary metric
WAU (Weekly Active Users) counts unique users active within a seven-day window, and it is particularly useful for products where engagement is expected a few times per week. This metric is great for understanding short-term user behavior. For instance, a mobile games app might want to track how often users engage with the app throughout the week, as regular check-ins (3-4 times a week) may signal successful engagement. However, users may only need to engage weekly with other businesses, such as project management tools, so WAU can be more relevant than DAU.
Choosing MAU as the primary metric
MAU helps you track longer-term engagement. If your product is something people use occasionally but consistently such as a calendar app, subscription-based service, or even e-commerce sites - MAU is critical to measure. It shows whether users stick around over time, even if they don't engage daily or weekly. If you're in a business that isn't based on daily engagement (such as SaaS, where users only log in when they need to complete a task), MAU will likely be more meaningful for gauging user retention.
Why active-user metrics matter?
Ultimately, which metric matters most depends on your product and what you're trying to measure. Active user metrics are more than just numbers, they provide a snapshot of your product's success in engaging users. DAU, WAU, and MAU help you understand user retention and the effectiveness of your product. For example, if your DAU is increasing, it indicates that users are returning regularly, a good sign of engagement. If the numbers are declining, it may suggest friction points or declining interest.
How to measure DAU, WAU, and MAU?
To effectively measure DAU, WAU, and MAU, you need to define the specific actions that constitute an active product user, whether logging in, making a purchase, or engaging with key features.
Three pitfalls account for most inflated or noisy active-user numbers. First, identity: the same person on a phone and a laptop becomes two users unless device IDs resolve to one user ID. Second, timezones: computing "a day" in UTC while your users live elsewhere shifts activity across day boundaries and distorts DAU. Third, internal and bot traffic: employees, test accounts, and crawlers should be excluded from the qualifying-event definition.
Settle all three once, in your warehouse's event model, so every team computes the same numbers from the same tables.
Using product analytics tools like Mitzu, you can track DAU, WAU, and MAU with greater accuracy and granularity. These tools allow you to segment users based on behavior, demographics, or acquisition channels, helping you identify which user groups are most engaged and which need re-engagement. For example, you can track a specific cohort's weekly interactions to see how their engagement evolved comparing to the previous month.
Mitzu is an agentic product analytics platform that runs on your data warehouse: you ask for DAU, WAU, or stickiness in plain language, and a deterministic query engine — grounded in a semantic layer of your events — generates the SQL. The same question always produces the same SQL, and the SQL stays reviewable, so your active-user metrics are computed on live warehouse data without copying it anywhere.
FAQ
What is the difference between DAU and MAU?
DAU (daily active users) counts the unique users who are active in one day; MAU (monthly active users) counts the unique users who are active at least once in a calendar month or a rolling 28–30-day window. Each person counts once per window. When both metrics use the same definition of an active user and the MAU window includes the measured day, DAU cannot exceed MAU. Dividing average DAU by MAU gives the DAU/MAU stickiness ratio.
What is a good DAU/MAU ratio?
A DAU/MAU ratio of 10–20% is typical for most SaaS and consumer products, 20%+ indicates a strong daily habit, and 50%+ is exceptional, seen mostly in messaging and social apps. Low-frequency products can be healthy well under 10% — judge the ratio against your product's intended cadence.
What does MAU stand for?
MAU stands for monthly active users. It is the number of unique users who perform at least one qualifying action in a product during a month, measured either as a calendar month or as a rolling 28- to 30-day window. DAU stands for daily active users and WAU for weekly active users; all three count each person once per window.
What is WAU?
WAU, or weekly active users, counts the distinct people who did at least one qualifying action in a product during a seven-day window. Because a week always contains the same mix of weekdays and weekend days, WAU is steadier than DAU, which makes it the usual lead metric for products people use a few times a week, such as project management or B2B tools.
What is a DAU metric?
A DAU metric is a daily count of unique active users: the number of distinct people who performed at least one qualifying action in a product on a given day. Teams usually report it as a daily series or a 7-day average and use it to judge daily engagement. The metric is only as good as its definition of active, so state which event qualifies and which timezone defines the day.
When should I prioritize DAU over WAU or MAU?
Prioritize DAU when your product depends on daily habits, such as messaging, social, or collaboration flows. If your product has naturally lower interaction frequency, WAU or MAU may be a better primary metric. The right choice depends on expected user cadence.
Is a rising MAU always a good sign?
Not necessarily. MAU can rise while activation, depth, or retention quality declines. Always pair active-user counts with funnel conversion and cohort retention to understand whether growth is meaningful.
How can I compare DAU/WAU/MAU across segments?
Segment active users by acquisition channel, plan type, region, or device class to identify engagement differences hidden in aggregates. This reveals which user groups are healthy versus at-risk. Warehouse-native analysis makes these cuts easier to maintain.
How does Mitzu help track active-user metrics?
Mitzu runs active-user queries directly on your warehouse and lets teams define active events visually. You can monitor DAU, WAU, and MAU on live data without exporting to third-party stores. SQL remains transparent for validation.
