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What is DAU/MAU Ratio (Stickiness)?

How often monthly users engage daily.

How to calculate it

Calculate DAU/MAU Ratio (Stickiness) as: Daily active users / Monthly active users × 100. Pull the inputs from your connected data and track the trend over time in your dashboard.

Examples

Example 1

8,000 DAU / 30,000 MAU = 27% stickiness, solid daily habit formation.

Example 2

8,000 daily active users against 30,000 monthly -> 27% stickiness, healthy for a workplace tool used several times a week.

Why it matters

The DAU/MAU ratio, or stickiness, measures how often monthly users engage on a daily basis and is a proxy for habitual product usage. A high ratio indicates the product has woven itself into users' routines, which strongly correlates with retention. Not every product warrants daily use, so the benchmark depends on the product's intended cadence.

Benchmark context

20%+ is solid and 50%+ is exceptional for daily-use products; tools used weekly or monthly will naturally and acceptably score lower.

Common pitfalls

Not all products warrant daily use.

The complete guide

Understanding DAU/MAU Ratio (Stickiness)

The DAU/MAU ratio, or stickiness, measures how often monthly users engage on a daily basis and is a proxy for habitual product usage. A high ratio indicates the product has woven itself into users' routines, which strongly correlates with retention. Not every product warrants daily use, so the benchmark depends on the product's intended cadence. For context, 20%+ is solid and 50%+ is exceptional for daily-use products; tools used weekly or monthly will naturally and acceptably score lower. Treat an external benchmark as a reference point rather than a universal target: company stage, sector, customer mix, pricing model and measurement definitions can materially change what “good” looks like. Read DAU/MAU Ratio (Stickiness) alongside Retention Rate and Activation Rate; together these measures provide a more balanced view and reduce the risk of optimizing one number at the expense of the wider outcome.

Make sure the numerator and denominator cover compatible populations and periods, and avoid dividing by a partial or unusually small base. Deduplicate users, define what qualifies as active or successful behavior, and version event tracking when the product changes. Define the qualifying user and meaningful product event before calculating the metric, then compare cohorts by signup date, plan, persona and acquisition source. A dashboard should show the current value, prior-period comparison and a trend rather than presenting one isolated reading.

Use cohort analysis and experiments to connect the metric to specific product behaviors, then remove friction around the actions that most reliably predict activation, repeat value and retention. Common pitfalls: not all products warrant daily use. Record the definition beside the dashboard, review it when systems or processes change, and annotate unusual events so future readers can distinguish a genuine performance shift from a data or timing effect.

Sources and further reading

  1. A guide to product metrics

    Mixpanel Accessed

  2. Important product metrics to track

    Amplitude Accessed

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