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DAU/MAU: how to measure stickiness and when the ratio misleads

Use DAU/MAU to check whether monthly actives come back often enough for your product cadence. This guide explains how to define active users, when WAU/MAU is the better lens, and what not to infer from a single benchmark.

Updated 2026-03-31
Best for

Product, growth, and lifecycle teams evaluating engagement quality and product cadence.

Decision

Whether the product behaves like a daily habit, a weekly habit, or a lower-frequency workflow that needs a different stickiness lens.

Use it when

Monthly actives look healthy but you still need to judge engagement depth, active-user quality, and benchmark relevance.

Reviewed by

MetricKit editorial review for SaaS engagement metrics.

Reviewed to keep active-user definitions, WAU/MAU comparisons, and benchmark cautions consistent with the linked stickiness calculators.

Try it in a calculator

DAU/MAU (Stickiness) Calculator
Example: 1,200 DAU, 8,000 MAU (target 20%)
If your product is used weekly rather than daily, compare this result with WAU/MAU before drawing a conclusion.

What DAU/MAU measures

DAU/MAU is a stickiness metric: how many monthly active users show up on a typical day. It is most useful when the product should be used frequently, but it becomes misleading if your active event is weak or your product's natural cadence is weekly rather than daily.

Formula

DAU/MAU = DAU / MAU

How to use it

  • Keep a stable 'active' definition (e.g., key event) for comparability.
  • Track by segment (persona/plan) to avoid blended averages hiding issues.
  • Use WAU/MAU for weekly cadence products; DAU/MAU can be too noisy otherwise.

How to improve stickiness

  • Shorten time-to-value so new users return quickly.
  • Make core workflows habitual with reminders, templates, and defaults.
  • Remove friction in repeat actions (speed, saved state, permissions).

Benchmarks and caveats

  • Daily cadence products expect higher DAU/MAU than weekly or monthly tools.
  • Use a consistent active window (calendar day vs rolling 24 hours).
  • Compare within the same segment before comparing across products.

Stickiness QA checklist

  • Ensure DAU and MAU use the same event definition and filters.
  • Exclude internal users and test accounts from both numerators.
  • Deduplicate users across devices if possible.

Common mistakes

  • Comparing DAU/MAU across products with different usage frequency expectations.
  • Using DAU and MAU from different date ranges or definitions.
  • Treating stickiness as the only goal (retention and revenue still matter).

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