DAU/MAU stickiness ratio
The DAU/MAU stickiness ratio divides daily active users by monthly active users. It approximates how many days in a month a typical active user shows up, making it a habit and engagement signal for apps and products. Its value hinges entirely on how 'active' is defined and on the DAU/MAU averaging method, so the underlying definitions must travel with the number.
What this means
Stickiness is DAU ÷ MAU. If a product's average DAU is a quarter of its MAU, the ratio is 0.25, loosely read as 'the average monthly-active user is present about a quarter of the days.' It is a coarse engagement gauge: useful for tracking a product's own trend, but only meaningful when the active-user definitions on both sides are identical and stable.
Definitions govern the ratio
There is no universal 'active user' definition — it might mean any session, a meaningful action, or a specific key event. DAU is also often an average over the period, computed several valid ways. Because the numerator and denominator both depend on these choices, two products' stickiness numbers are not comparable unless they define activity the same way, and a single product's ratio is only trustworthy when the definition is held constant.
Read stickiness alongside retention curves rather than as a standalone verdict.
- Ratio = daily active users ÷ monthly active users
- Approximates active days per month per user
- Wholly dependent on the 'active' definition
How it appears in analytics and logs
A higher stickiness ratio implies users return more days per month. But the ratio moves if the activity definition or the DAU averaging window changes, so a shift can reflect a definition change rather than real behavior.
Diagnostic use case
Gauge how habitual a product is by comparing daily to monthly active users, complementing retention curves with a single recurring-use signal.
What WebmasterID can help detect
WebmasterID measures active users over time windows first-party, so daily and monthly active counts can be compared without third-party cross-site identifiers.
Common mistakes
- Comparing stickiness across products with different active definitions.
- Changing the activity definition and reading the ratio shift as behavior.
- Treating stickiness as a substitute for retention analysis.
Privacy and accuracy notes
Stickiness is a ratio of aggregate active-user counts. Counting unique active users requires identifiers; aggregate and minimize them. This is educational, not legal advice.
Related pages
- Active users over 1, 7, and 28 days
Active users is the count of distinct users with an engagement signal in a window. The window is the whole story: 1-day, 7-day, and 28-day active users (DAU/WAU/MAU) count different things, and GA4 reports rolling versions of each. They overlap rather than add up, and the DAU/MAU ratio is read as a 'stickiness' signal — but all of it inherits the identifier limits of any user count.
- App retention rate
App retention rate measures how much of an install or first-use cohort comes back after a number of days. Definitions vary: day-N retention counts users active exactly on day N, while rolling or range retention counts users active on or after day N. Because these methods produce different curves from the same data, the retention definition must be stated for the number to mean anything.
- Retention rate
Retention rate measures how many users from a starting cohort come back in a later period. It depends entirely on definitions: what counts as 'returning', over what window, and which cohort. A 7-day and a 30-day retention rate answer different questions, and neither is comparable to a churn figure computed a different way.
- Web analytics
Track active-user windows first-party.
Sources and verification notes
- Google — GA4 active users metric and time windowsDefines active users over 1-day and 30-day windows used for DAU/MAU.
Last reviewed 2026-06-24. Facts are checked against primary/official sources where available; uncertain specifics are marked “Data not yet verified” rather than guessed.