DAU is the number of unique users who opened the product in one day; WAU counts the same over a seven-day window, MAU over thirty days. All three count the same event — only the window length changes. The meaningful information is hidden in the ratio between them.
Why "active user" depends on the product
There's no universal definition of an active user. In a banking app, logging in is enough; in a task-management product, the only honest definition might be creating or completing a task. Google Analytics also calculates active users on its own terms, based on engaged sessions.
When the definition isn't written down, two teams look at the same chart and draw different conclusions. A product team's first job is to pin that definition in a single sentence.
What the DAU-over-MAU ratio tells you
This ratio shows, on average, how many days a month someone who visited at least once actually came back.
- Around 0.10 means users visit about three days a month
- Above 0.50 means the product has become a daily habit
- If the ratio is falling while MAU is rising, marketing is working but the product isn't sticking
That last line is the critical one. If total users are growing while daily usage stays flat, the problem isn't in acquisition.
A DAU chart read without separating new users from returning ones is also misleading. The same number could be made of new people arriving every day, or a loyal core returning every day. Both possibilities produce the same line — and demand completely different decisions.
Where Lodos stands in this measurement
A product manager doesn't have to join the engineering queue to reach these three numbers. Data Analyst answers a plain-language question against the connected database and returns the result as a table or chart, and the finding becomes an item on the Task Management board. The module list for product work is on the Product Managers page.
Here's the limit: Data Analyst doesn't offer a ready-made product analytics dashboard — it queries your database. In a product where no events are logged at all, there's no answer to the active-user question either; an event schema has to be built first.
For page-side measurement thresholds, see the Web Analyzer breakdown.
FAQ
Is MAU thirty days or a calendar month? Both methods are used. A rolling thirty days smooths fluctuation; a calendar month makes reporting easier. Whichever you pick, it should be written down.
What should the DAU-over-MAU ratio be? It varies by sector. It's naturally high for messaging products and low for products used a few times a year. The comparison is made against your own historical data.