Analytics & Retention

Stickiness (DAU / MAU)

Also known asDAU/MAU RatioEngagement Ratio

The ratio DAU ÷ MAU — how often a typical monthly user comes back within the month. 0.5 = average user is active 15 days out of 30.

MWM data

State of April 2026

Median DAU / MAU stickiness

14.1%

Half of measurable apps have stickiness below this — the median app is far from "daily habit"

Top-25% stickiness

20.1%

Above-average stickiness — strong consumer apps

Top-10% stickiness

31.0%

Strong daily-habit tier — chat, social, games with daily loops

Top-1% stickiness

57.7%

Genuine daily-essential apps — messaging, banking, mainstream social

Key takeaways

  1. 01Stickiness = DAU ÷ MAU. A 0.5 stickiness means the average monthly user is active 15 days per month.
  2. 02Industry baselines: social/messaging 0.4-0.7, productivity 0.3-0.5, casual games 0.15-0.35, utilities 0.05-0.15.
  3. 03A 5-point stickiness lift (e.g., 0.20 → 0.25) typically lifts revenue 20-30% — more sessions, more touchpoints, more monetization opportunities.
  4. 04Push notifications, daily-streak mechanics, and content freshness are the primary stickiness levers.

Stickiness is the DAU ÷ MAU ratio — how often a typical monthly user comes back within the month. A stickiness of 0.5 means the average monthly user is active 15 days out of 30. A stickiness of 0.1 means they're active 3 days per month. It's the single cleanest summary of "do users habitually return", and one of the highest-leverage metrics in any consumer app.

The 14% median is the most uncomfortable truth in this metric: half the measurable catalog has stickiness below 14%. The headline "stickiness 30-50%" numbers most playbooks quote describe the top decile, not the typical app. Use the distribution and category breakdown below to find your real baseline.

Industry benchmarks by category

Compare to your own category and your own historical baseline — cross-category comparisons mislead.

Why stickiness lifts revenue

The primary stickiness levers

  1. Push notifications done right — relevant, timely, well-throttled. Done wrong (spammy, irrelevant) they crater stickiness fast.
  2. Daily-streak mechanics — Duolingo's streak counter is the canonical example. Aligned-incentives gamification that rewards daily return.
  3. Content freshness — for media, social, and content apps, the rate of new content matters enormously. Users return when they expect something new.
  4. Personalization — surfacing relevant content drives higher next-session probability.
  5. Social / network effects — products where other users' actions notify you (messages, mentions, comments) have structurally higher stickiness.

Don't game stickiness at the expense of total value. Adding noisy push notifications can lift DAU short-term (more opens) but trigger uninstalls and crater MAU. Track stickiness alongside MAU and DAU absolute trends — high stickiness on a shrinking MAU is a warning, not a victory.

Stickiness lift levers — ranked by impact

LeverTypical liftRisk if done poorly
Push notifications (relevant + timed)+0.05-0.15 stickiness ptsSpammy push → opt-outs + uninstalls
Daily-streak mechanic+0.05-0.20 (habit-formation apps)Punishing-streak loss causes churn at streak break
Content freshness (media / social)+0.03-0.10Stale content → users wait between visits
Personalization (relevant feed)+0.05-0.12Bad recommendations → users see irrelevant content + leave
Social / network effects+0.10-0.30 (structural)Hard to add post-launch; either built-in or not
Frequency capping on pushProtects MAU (prevents loss)Too aggressive → DAU drops short-term

Lifts compound but with diminishing returns. The first 5-10 stickiness points come from getting push + content cadence right; the next 5-10 require structural design (network effects, streaks, gamification). Track stickiness alongside MAU absolute trend — high stickiness on shrinking MAU is bad, not good.

DAU / MAU stickiness distribution across the catalogDistribution of DAU/MAU ratio (US, iOS) across catalog apps. Most apps cluster below 15% — the "weekly visitor" zone. The 20-50% range is where strong consumer apps live; only daily-essential products clear 50%.02.5K5K7.5K10K<5%: 7015-10%: 4,30710-15%: 5,50715-20%: 3,76820-30%: 2,77430-50%: 1,69950%+: 409Strong-app tier<5%5-10%10-15%15-20%20-30%30-50%50%+DAU / MAU ratio
DAU / MAU stickiness distribution across the catalog — US-market iOS apps with ≥1,000 d30 downloads and ≥100 daily active users, MWM May 2026, State of April 2026.

The shape is the surprise: the histogram peaks in the 10-15% bucket and decays from there. Most consumer apps live in the single-digit-percent range; the "good app" territory above 20% is reached by only the top quartile. If your stickiness sits in the 5-10% band, you're in the broad majority — not failing. The lever question is what realistic +5 looks like for your category, not whether you can chase a 30% target.

DAU / MAU stickiness — median and top-decile by category (US, May 2026)

CategoryMedian stickinessTop-10% stickiness
Social & Communication21.2%40.9%
Productivity & Tools17.5%38.5%
Lifestyle & Well-being15.2%30.4%
Media & Entertainment15.0%30.3%
Education & Knowledge12.6%25.4%
Game11.8%23.9%

One striking thing in the category breakdown: Games have the lowest median stickiness (11.9%) despite the highest session length seen earlier. Gamers play long, but not every day. Social has both the highest median and the highest top-decile gap (21.9% / 43.8%) — the daily-anchor effect of messaging products. Productivity beats Lifestyle and Media at median thanks to the weekday work-tool habit.

Quick answers

What is stickiness in mobile app analytics?

**Stickiness = DAU ÷ MAU** — the ratio of daily active users to monthly active users. It measures how often a typical monthly user comes back within the month. A stickiness of 0.5 means the average monthly user is active 15 days out of 30. The single cleanest summary of habitual return behavior.

What is a good stickiness ratio?

Category-dependent. Social / messaging 0.4-0.7. Productivity 0.3-0.5. Casual games 0.15-0.35. Streaming / media 0.15-0.30. Utilities 0.05-0.15 (rare-use products by design). The right benchmark is your own historical trend and category peers — cross-category comparisons mislead because category usage patterns differ structurally.

How can I improve my app's stickiness?

Five main levers. (1) **Push notifications** done right — relevant, timely, well-throttled. (2) **Daily-streak mechanics** — Duolingo-style gamification rewarding daily return. (3) **Content freshness** — for media / social apps, the rate of new content matters. (4) **Personalization** surfacing relevant content. (5) **Social / network effects** — products where other users' actions notify you have structurally higher stickiness.

Why does stickiness matter so much?

Three big reasons. (1) **Revenue lift** — more sessions = more monetization opportunities. A 5-point stickiness lift typically lifts revenue 20-30%. (2) **Lower churn** — habitual users are dramatically harder to lose. (3) **Better long-tail LTV** — the asymptotic retention rate (the "permanent user" fraction) correlates strongly with stickiness. Lifting stickiness 5 points often drops 90-day churn by 10-20 points.

What is the formula for app stickiness?

Stickiness = DAU ÷ MAU, expressed as a percentage — the share of your monthly active users who open the app on an average day. 20,000 DAU against 100,000 MAU is 20% stickiness. Use trailing-28-day MAU for a cleaner denominator, since calendar months vary in length. See [[dau-mau]] for the full breakdown.

Is stickiness the same as retention?

No. Stickiness (DAU/MAU) measures how often your current monthly users return on an average day; [[retention]] measures whether a specific install cohort comes back N days after installing. An app can have decent retention but low stickiness (people return weekly, not daily) — stickiness is about frequency, retention is about cohort survival.

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