DAU, WAU and MAU are the three most quoted product metrics, and the three most often misread. Daily, weekly and monthly active users each count the unique people who were active in a period. Which one you should watch depends on how often your product is naturally used, and every one of them depends on a definition of "active" that most teams never write down.
This guide covers what each metric means, how to calculate them correctly, which one fits your product, the DAU/MAU stickiness ratio, and the mistakes that make active user counts misleading.
What are DAU, WAU and MAU?
- DAU (daily active users): unique users active on a given day.
- WAU (weekly active users): unique users active in a seven-day period.
- MAU (monthly active users): unique users active in a 30-day period or calendar month.
"Unique" is the important word. A person active on 20 days of a month counts 20 times across daily figures but once in MAU. That's why you can't add up DAU to get MAU, and why active user metrics need a tool that counts distinct people, not events. See the glossary entries for daily active users and monthly active users.
What counts as "active"?
This is the decision that matters most, and there are three common choices:
- Any event. Anyone who did anything, including opening the app. Easy, and generous.
- A meaningful event. Anyone who did something that reflects value: sent a message, edited a document, ran a report. Harder to define, and far more honest.
- A core action. Only people who did the thing your product exists for. Strict, and the best predictor of retention.
A product where people log in to check a notification and leave will look healthy under definition 1 and worrying under definition 3. Pick the definition that matches value, write it down, and use it everywhere. The active users glossary entry has more examples.
How to calculate DAU, WAU and MAU
The calculation is the same for all three: count distinct user IDs with at least one qualifying event in the window.
Example. Over a seven-day week:
| Day | Active users |
|---|---|
| Mon | Ana, Ben, Cai |
| Tue | Ana, Ben |
| Wed | Ana, Dee |
| Thu | Ana, Ben, Eli |
| Fri | Ana |
| Sat | – |
| Sun | Ben |
- DAU on Thursday is 3.
- The average DAU over the week is (3 + 2 + 2 + 3 + 1 + 0 + 1) ÷ 7 = 1.7.
- WAU is 5: Ana, Ben, Cai, Dee and Eli, each counted once.
Two practical details:
- Use your project's timezone. A "day" in UTC splits an evening in Asia or the Americas across two days. LogZuno groups by your project's timezone so "today" means your today.
- Rolling or calendar periods. A rolling 30-day MAU moves smoothly every day; a calendar-month MAU resets on the 1st. Rolling is better for trends, calendar is easier for reports. Be consistent, and label which one a chart uses so nobody compares a rolling figure with a calendar one in the same meeting.
Which one should you watch?
Match the metric to your product's natural frequency: how often a healthy user would use it.
| Product type | Natural frequency | Primary metric |
|---|---|---|
| Messaging, social, daily tools | Daily | DAU |
| Project management, B2B SaaS | Several times a week | WAU |
| Accounting, travel, tax, reporting | Monthly or less | MAU |
Watching DAU for a monthly product makes every weekend look like a crisis. Watching MAU for a daily product hides a habit that's quietly breaking. Most B2B SaaS products are best served by WAU, with DAU as a secondary signal.
The DAU/MAU ratio (stickiness)
DAU divided by MAU estimates how many days a month the average monthly user is active. A ratio of 0.2 means the typical monthly user shows up on about 6 days out of 30.
- Daily-habit products often aim higher.
- Monthly-use products will naturally sit lower, and that's fine.
Stickiness is most useful as a trend in your own product, not as a benchmark against others. If it rises after a release, people are coming back more often. See DAU/MAU ratio.
A richer view of the same question is the frequency chart, or power-user curve: how many users were active on 1, 2, 3 and up to 30 days in a month. A second hump on the right is a group of habitual users. Our article on power users explains how to read it.
Break active users down
A single active user line hides the story. Three breakdowns usually explain it:
- By plan. If active users on paid plans are flat while free or trial users grow, revenue won't follow the headline number.
- By acquisition source. Users from some channels stay active far longer than others. That's a marketing budget decision hiding in a product chart. For visitors from AI assistants such as ChatGPT, see AI visibility.
- By signup cohort. Stack WAU by the month people signed up. If last quarter's cohorts shrink faster than older ones, something in onboarding changed.
Each breakdown is one property on the same events, which is why it pays to send plan and source with your events from the start. If you're a growth team, growth analytics in LogZuno shows how these charts fit together.
Mistakes that make active users misleading
- Counting bots. Automated traffic that runs your scripts can inflate active users, especially if "any event" counts. Count people, not page loads.
- Counting anonymous visitors as users. A visitor on two devices is two visitors and one user. Identify people when they sign in.
- Changing the definition mid-year. Every chart before the change stops being comparable.
- Reporting the number without the trend. 1,204 DAU means nothing alone; up 8% on the previous 30 days means something.
- Ignoring accounts. In B2B, 1,000 active users across 10 accounts and across 500 accounts are very different businesses. Track active accounts too.
Active users and the rest of your metrics
Active users are an output, not a lever. To move them, look upstream at activation rate and downstream at retention by cohort. A falling DAU is usually a retention problem in an older cohort or an activation problem in a newer one, and a cohort chart tells you which. Our guides to user engagement metrics and SaaS KPIs put DAU, WAU and MAU in context.
Tracking DAU, WAU and MAU in LogZuno
LogZuno calculates active users directly from your events:
- Choose weekly active users or monthly active users as the calculation on any trend, or count unique users for any event and interval.
- Restrict "active" to the events that represent value, so your definition is built into the chart.
- Count unique groups to see active accounts alongside active users.
- See the frequency chart for the power-user curve, in your project's timezone.
Put DAU, WAU and stickiness on a dashboard with a note that states your definition, and add a threshold alert so you hear about a drop the day it happens. Learn more about product analytics in LogZuno.
Key takeaways
- DAU, WAU and MAU count unique active users per day, week and month.
- Define "active" as a meaningful action, write it down and keep it stable.
- Pick the metric that matches how often your product is naturally used.
- Read the DAU/MAU ratio and the frequency chart as trends, and track active accounts in B2B.