Digital Health Metrics

Topic 6 of 31· Patient engagement and access

DAU/MAU Stickiness Ratio

DAU/MAU stickiness ratio compares daily active users (DAU) against monthly active users (MAU) — the same underlying measure used for weekly active users (WAU) against MAU — to express what share of a product's broader user base engages with it on any given day. It is the standard product-analytics measure of engagement intensity, distinct from whether a user is retained at all (see user retention rate) or how consistently one specific enrolled patient engages over time (see patient engagement consistency rate): stickiness describes the population-level rhythm of use, not any one individual's pattern.

Why it matters

Two digital health products can report an identical monthly active user count while having very different underlying engagement intensity: one where most of those users open the app almost daily, and another where most open it once a month right before it would otherwise count as inactive. DAU/MAU stickiness ratio distinguishes these two very different situations with a single, simple, well-understood benchmark figure that product and clinical teams can track over time and compare against known industry ranges — a ratio around 20% is a commonly cited reasonable benchmark for many consumer apps, while daily-habit products (a food or symptom diary a patient is expected to use every day) should be judged against a meaningfully higher bar. Because stickiness is sensitive to how "active" is defined, it is most useful as a trend for one product over time, and as a comparison against products built for a similar use pattern, rather than as an absolute cross-industry benchmark.

How it's calculated

DAU/MAU stickiness ratio = average daily active users in period /
                            monthly active users in same period × 100

WAU/MAU ratio (weekly, same principle) is a softer variant, more
appropriate for products expected to be used a few times per week
rather than daily.

"Active" must be defined precisely and consistently (e.g. a completed
qualifying action, not a passive app open) across both the numerator
and denominator.

Worked example

A digital diabetes management app has 10,000 monthly active users in a given month, defined as any user completing at least one qualifying action (a glucose log, a meal log, or a medication check-off) in that month. Averaging daily active user counts across the 30 days of that month gives an average DAU of 2,200. The DAU/MAU stickiness ratio is 2,200 / 10,000 × 100 = 22%, indicating that on a typical day, about 22% of the app's monthly user base engages with it — a reasonable figure for a daily-habit chronic-condition tool, though one the product team would want to see trending upward over time as the ideal behaviour (daily logging) becomes more habitual for enrolled patients.

Data sources and caveats

DAU, WAU, and MAU are all calculated from the same underlying event logs, using one consistent definition of a "qualifying active" event across every window; changing that definition between the numerator and denominator calculations (for example, counting any app open for DAU but only a completed action for MAU) will produce a distorted ratio that does not reflect real engagement intensity. The appropriate benchmark for stickiness depends heavily on the product's intended use pattern: a tool meant to be used once a week (a weekly symptom check-in) will and should have a lower DAU/MAU ratio than a tool meant to be used daily (a continuous glucose monitor companion app), so stickiness should always be interpreted against the product's own intended cadence of use, not a single universal target.

Pitfalls

  • Comparing stickiness ratios across products with different intended use frequency: a weekly-use tool will structurally show a lower DAU/MAU ratio than a daily-use tool even if both are performing exactly as intended for their respective use cases; benchmark against the product's own intended cadence, not a single universal target.
  • Using inconsistent activity definitions across the numerator and denominator: this can produce a stickiness ratio that does not reflect genuine engagement intensity and cannot be meaningfully compared over time or against other products.
  • Treating a rising stickiness ratio as unambiguously positive without checking overall MAU trend: a rising ratio driven by a shrinking, more habitual core user base while overall MAU declines is a very different — and more concerning — situation than one driven by genuinely increasing daily engagement across a stable or growing user base.
  • Ignoring day-of-week and seasonal effects on DAU: DAU can vary substantially by day of week (weekday versus weekend) or season for many health products; average DAU over a period that captures a full natural cycle rather than a short window that could be skewed.

Sources

  • Peer-reviewed and industry literature on mobile and digital product engagement metrics, widely used benchmarking frameworks from mobile analytics platforms
  • Digital Therapeutics Alliance, best-practice guidance on engagement measurement for digital therapeutics
  • Peer-reviewed literature on digital health engagement measurement, for example studies published in the Journal of Medical Internet Research (JMIR mHealth and uHealth)

See also: user retention rate and patient engagement consistency rate, the two related engagement metrics this ratio is most often confused with.