DAU/MAU Ratio

Category: Growth & Engagement · Level: Mid · Also called: DAU to MAU, Stickiness ratio

TL;DR

The ratio of daily to monthly active users — a measure of how many days per month the average user shows up.

DAU/MAU divides daily actives by monthly actives. A ratio of 0.5 means the average monthly user is active 15 days a month. Consumer social apps often cite ratios above 0.5; productivity tools sit around 0.3; weekly-cadence tools under 0.2.

The ratio is the most common stickiness benchmark. It's easy to game in the short term (push notifications) but hard to fake long-term — sustainable high stickiness requires a product that earns daily attention.

Formula

Stickiness = DAU ÷ MAU

  • DAU — Daily Active Users
  • MAU — Monthly Active Users

0.5 means the average user is active half the month; 0.2 is typical for B2B tools.

Worked example

A productivity SaaS has 320k DAU and 950k MAU. Stickiness = 320k ÷ 950k = 33.7%. That's roughly 'the average user shows up 10 of 30 days,' on the high end for non-messaging B2B tools.

Common pitfalls

  • Comparing DAU/MAU across products with different intended use cadences.
  • Inflating the ratio with notifications that don't drive real value.
  • Ignoring ratio decline as the user base broadens.

When this shows up in a pitch deck

Engagement-focused B2C decks lead with DAU/MAU. SaaS decks usually show stickiness in a different format.

Related terms

  • DAU — Daily Active Users — the count of unique users who took a meaningful action in the product on a given day.
  • MAU — Monthly Active Users — the count of unique users who took a meaningful action in the product within a given month.
  • Stickiness — A qualitative term for how habitual a product is, often quantified as the DAU/MAU ratio or session frequency.
  • Retention Curve — A chart showing what fraction of a cohort is still active week-by-week or month-by-month after sign-up.
  • Cohort Analysis — Grouping users by sign-up period and tracking each group's behavior over time to spot trends invisible in aggregate metrics.

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