DAU/MAU Ratio (Stickiness)
Customer GrowthDAU/MAU, often called stickiness, is the percentage of a product's monthly active users who use it on any given day. It is the cleanest single metric for habituation: how many days in the month does a typical user open the product?
It is most useful as a relative metric, tracked over time, rather than an absolute. Different product categories have very different ceilings. A daily messaging product can hit 70% DAU/MAU; a weekly reporting tool will not exceed 25% no matter how well it is built. Compare the metric to its category benchmark, not to a universal target.
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Key takeaways
- DAU/MAU = (Daily Active Users ÷ Monthly Active Users) × 100. A user counted once per day in DAU; once per month in MAU.
- Benchmarks: above 50% is exceptional and rare (Slack, WhatsApp, gaming). 20 to 30% is solid for productivity SaaS. Below 10% means most monthly users only show up once or twice.
- The ratio caps at the natural usage frequency of the product. A weekly-cadence product can never have a DAU/MAU above ~25%, regardless of how good it is.
What is the DAU/MAU ratio?
DAU/MAU divides daily active users on a representative day by monthly active users for the surrounding 30 days. The result is the share of monthly users who used the product on that day. Mathematically, it is also the average number of active days per month divided by 30.
The term "stickiness" stuck because the metric captures how habit-forming a product is. Products that are part of a daily routine (Slack at work, WhatsApp socially) post very high ratios. Products that are episodic by nature (tax software, project setup tools) post low ratios even when they are excellent at what they do.
How do you calculate DAU/MAU?
The formula:
DAU/MAU = (Daily Active Users ÷ Monthly Active Users) × 100
Worked example: A productivity SaaS reports 6,000 DAU on a typical Tuesday and 24,000 MAU for the trailing 30 days. DAU/MAU = (6,000 ÷ 24,000) × 100 = 25%. The average user opens the product 7 to 8 days a month.
Three definitions to lock down:
- 1.What counts as "active." Login is too low a bar for most SaaS; most teams require at least one meaningful action (created an item, sent a message, viewed a report).
- 2.The day chosen for DAU. A weekday for B2B SaaS; a weekend for consumer apps. Stick to the same day-of-week to compare across periods.
- 3.Whether to count by user or by account. For B2B, per-account stickiness is the more revealing metric for retention work.
DAU/MAU benchmarks by category
Category-specific bands (per-user DAU/MAU):
- Daily-routine consumer apps (messaging, social, news): 50 to 80%. WhatsApp and Facebook historically reported in this range.
- Workflow B2B SaaS (CRM, project management, communication): 25 to 50%. Slack famously hit 50%+ at peak.
- Analytics or reporting B2B SaaS: 10 to 25%. Most users check in episodically, not daily.
- Episodic tools (tax, accounting close, hiring): 2 to 10%. The product is excellent if used at the right moment; daily usage isn't the goal.
- B2C subscription content (streaming, fitness): 20 to 40%, varies wildly by content cycle.
A falling DAU/MAU over time is a stronger churn predictor than churn rate itself. Users typically reduce their usage frequency 1 to 3 months before they cancel.
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Frequently asked questions
Should I use 30 days or a calendar month for MAU?
Trailing 30 days is more accurate. Calendar months produce comparison artefacts because months have different lengths. Trailing 30 days smooths out and matches the day-by-day rolling cadence most analytics tools default to.
Is DAU/MAU still relevant for B2B SaaS?
Yes, for products with daily-relevant workflows. For products that are inherently weekly, monthly, or episodic, it is less useful. Match the metric cadence to the product cadence: WAU/MAU for weekly products, MAU/QAU for quarterly ones.
Can I improve DAU/MAU?
Within the natural ceiling of the product, yes. The highest-yield levers are notifications and triggered re-engagement (only when they add value), reducing the value-of-each-session and increasing the value-of-streaks, and integrating into the user's existing daily tools so the product surfaces in moments they already have.