DAU/MAU measures how essential your product is. A high ratio means people use it most days, not just once a month — the strongest signal of retention and habit.
Example: 8,000 DAU ÷ 40,000 MAU × 100 = 20% stickiness. Benchmarks vary by category — daily tools aim for 50%+, monthly-use products far less.
20%+ is solid; 50%+ is an exceptional daily-use product.
Example — DAU/MAU in practice
Imagine Dubizzle's app has 900,000 monthly active users across the UAE, and on an average day 270,000 of them open it to browse listings or check messages from a buyer. Dividing daily by monthly users gives a DAU/MAU stickiness score of 30% — high for a classifieds app, and a sign the product team is right to invest more in daily habits like saved-search alerts.
لنفترض أن تطبيق دوبيزل لديه 900,000 مستخدم نشط شهريًا في الإمارات، وفي المتوسط يفتحه 270,000 مستخدم يوميًا لتصفح الإعلانات أو مراجعة رسائل المشترين. بقسمة عدد المستخدمين النشطين يوميًا على عدد المستخدمين النشطين شهريًا ينتج معدل الالتصاق (DAU/MAU) يبلغ 30% — وهو معدل مرتفع لتطبيق إعلانات مبوّبة، ويشير إلى أن فريق المنتج محق في الاستثمار أكثر في عادات يومية مثل تنبيهات البحث المحفوظ.
DAU/MAU, properly understood
DAU and MAU are both counted by unique user IDs active in their respective window — a day, a trailing 30 days — and stickiness is simply DAU divided by MAU. What counts as 'good' depends entirely on the product category: a daily-use product like messaging, social, or delivery should score much higher than a low-frequency product like travel booking or insurance by design, so there's no universal healthy number to chase.
Super-apps and delivery apps common across the Gulf naturally aim for high stickiness because ordering or browsing is an everyday behavior for their category. Ramadan often produces a temporary stickiness spike for food, grocery, and entertainment apps as routines shift and evening screen time rises, followed by a reversion after Eid — worth reading as seasonal rather than a permanent product improvement. Apps with a large expatriate user base can also see stickiness dip around common regional travel periods, like summer or Eid holidays, as users leave the country temporarily but stay counted in MAU.
Stickiness can be inflated by push notifications or auto-refresh behavior that counts as an 'open' without real engagement — define 'active' consistently (a meaningful action, not just a launch) or the ratio ends up measuring notification effectiveness rather than habit. Comparing stickiness across different product categories is close to meaningless, and a rising stickiness ratio on a shrinking MAU — churned users leaving first — can look like a win while the business is actually contracting.
Read stickiness alongside the MAU trend itself (a rising ratio on a shrinking base is a warning, not a win), retention curves (D1/D7/D30), and session depth or duration — stickiness tells you how often people come back, not what they actually do once they're there.
For genuinely lower-frequency products, a WAU/MAU (weekly rather than daily active users over monthly) cut is often the more honest ratio, since forcing a daily lens onto a product people reasonably use two or three times a week just manufactures a permanently low, discouraging number that doesn't reflect real engagement. Day-of-week baselines also matter more in the Gulf than the DAU definition usually accounts for — the regional weekend falls on Friday-Saturday rather than the Saturday-Sunday most analytics tooling defaults to, so a 'daily' pattern built around a Western week can misread which days are genuinely peak usage versus quiet ones for a local audience. Rebuild the analytics calendar around the actual regional week before trusting any day-level stickiness comparison.
Put it to work
- Define 'active' consistently — a meaningful action, not just an app open triggered by a push — before trusting the ratio.
- Read stickiness alongside the MAU trend line — a rising ratio on a shrinking user base is a warning, not a win.
- Treat stickiness moves through Ramadan and Eid as seasonal, not structural, and don't over-credit a feature launch landing in the same window.
- Compare stickiness only against your own product category's own history, never against a different type of app.
- Watch for expatriate-heavy user bases skewing MAU during regional travel seasons, and investigate dips before reacting.
- Pair stickiness with session depth or feature usage so daily visits register as meaningful, not just a habitual check-in.
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