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Calculate Daily-to-Monthly Active User Stickiness

Normalize daily active users against the monthly active audience.

DAU
MAU

DAU/MAU stickiness

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Inputs modeled

2

10% more first input

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Processing

Browser only

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How the calculation works

Observed inputsYour own periodTransparent formulaEditable assumptionsDecision outputDAU/MAU stickinessCompare like-for-like periods before acting on the result.

How to Use This Tool

Normalize daily active users against the monthly active audience. Growth in monthly users can hide weakening everyday utility when daily engagement is never normalized against the larger base.

Why App DAU/MAU needs more than a raw total

DAU and MAU must use the same identity and activity definition; mixing devices with accounts invalidates the ratio. For this page, the useful comparison is dau/mau stickiness, not whichever input happens to be largest. The App DAU/MAU result answers the decision in the heading and should not be reused as a score for a different workflow.

Entered Daily active usersSame input plus 10%compare
App DAU/MAU changes daily active users alone for the secondary result, leaving every other entered value fixed.

The exact App DAU/MAU formula

DAU/MAU stickiness equals daily active users divided by monthly active users × 100. The visible fields are Daily active users and Monthly active users. For App DAU/MAU, read each printed unit before entry and make the values describe one transaction, cohort or reporting window. If those scopes differ, the displayed dau/mau stickiness may be arithmetically valid but operationally meaningless.

Interpreting dau/mau stickiness

Compare consistent calendar windows and segment by platform or acquisition source before assigning a product target. The ten-percent comparison is deliberately narrow: it tests the influence of daily active users and is neither a forecast nor a confidence interval. Preserve the values used, their dates and the resulting decision so a later reviewer can reproduce why App DAU/MAU supported the choice.

What this App DAU/MAU model leaves out

The ratio is not retention, does not reveal visit frequency distribution and can be distorted by seasonality. That is where App DAU/MAU stops being trustworthy. If an excluded factor could reverse dau/mau stickiness, extend the model explicitly or use the authoritative account system instead of hiding the factor inside an unexplained adjustment.

Evidence and independent verification

The reference reviewed for App DAU/MAU is Apple Developer — App Store Connect Analytics. Apple Developer — App Store Connect Analytics supports the named definition or rule but does not supply private values for dau/mau stickiness. Before acting on the result, reconcile the worked example with the relevant dashboard, invoice, export or measurement.

Private, reproducible calculation

App DAU/MAU runs its arithmetic in the current browser tab and requests no login or API key. That keeps the App DAU/MAU inputs away from the site's calculation server, while leaving the user responsible for detecting stale data or a changed platform rule. When an assumption changes, reopen Apple Developer — App Store Connect Analytics and rerun the saved App DAU/MAU scenario.

Sources & assumptions

Tool Spec v2 · verified 2026-08-22. Platform rules and fees can change; the editable inputs remain authoritative for your account.

Official references

Model assumptions

  • Every input covers the same reporting period or cohort.
  • The ratio is not retention, does not reveal visit frequency distribution and can be distorted by seasonality.
  • The calculator uses only the visible fields and does not fetch account data.
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Frequently Asked Questions

What exactly does App DAU/MAU return?
App DAU/MAU returns dau/mau stickiness from the displayed formula: DAU/MAU stickiness equals daily active users divided by monthly active users × 100. No hidden account field participates in this result.
Which input should I verify first for App DAU/MAU?
Start App DAU/MAU with Daily active users. Growth in monthly users can hide weakening everyday utility when daily engagement is never normalized against the larger base. Confirm the remaining App DAU/MAU fields use the same scope and reporting window.
What does the Daily active users sensitivity result mean?
It raises daily active users by ten percent while holding the other fields fixed. Compare consistent calendar windows and segment by platform or acquisition source before assigning a product target. It is not a probability or forecast.
When should I reject the App DAU/MAU result?
Reject or extend the model when this limitation matters: The ratio is not retention, does not reveal visit frequency distribution and can be distorted by seasonality.
Which evidence was reviewed for App DAU/MAU?
App DAU/MAU cites Apple Developer — App Store Connect Analytics for the current definition; use your own source system for the account-specific values behind dau/mau stickiness.
Where does App DAU/MAU process my inputs?
The calculation for dau/mau stickiness runs in browser JavaScript and requests no account credential or calculation API.

What people usually need next

Picked by hand, not by algorithm.

💻App Retained UsersStickiness is broad engagement; inspect a fixed install cohort next.

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