TL;DR
DAU/MAU ratio measures daily active users as a percentage of monthly active users — the canonical product-engagement metric. A 50% DAU/MAU means the average user opens the product on roughly half the days of the month. Consumer best-in-class (Spotify, Instagram, Snapchat): 50–70%. Productivity SaaS (Slack, Figma, Notion during workdays): 60–80%. Below 15% indicates weak engagement for products positioning as daily-use.
What is DAU/MAU ratio?
DAU/MAU ratio measures how frequently the average monthly active user returns to a product within a month. Formula: Daily Active Users ÷ Monthly Active Users × 100. A DAU of 200,000 and MAU of 500,000 produces a DAU/MAU of 40% — meaning the average monthly user opens the product on roughly 12 of 30 days.
The metric was popularized by Facebook in the early 2010s and remains the dominant engagement metric for consumer apps. It's an intuitive proxy for product "stickiness" — high ratios indicate users return often, low ratios indicate sporadic use.
DAU/MAU is the right metric for products where daily use is the goal (consumer social, music, productivity SaaS used by office workers). It's the wrong metric for transactional products (banking, ridesharing) or for B2B SaaS used weekly rather than daily — for those, WAU/MAU is the better fit.
Why DAU/MAU matters
For consumer apps, DAU/MAU is the single strongest predictor of long-term retention and monetisation. The mechanism is mechanical: a user with 60% DAU/MAU has 18 product touches per month vs. 6 for a 20% DAU/MAU user — three times as many opportunities to drive engagement, monetisation, and habit formation. Once habits form, retention compounds.
For B2B SaaS, DAU/MAU correlates strongly with NRR and contract expansion. A team using a workflow tool 25 days/month is structurally more likely to expand seats, adopt additional modules, and renew than a team using it 8 days/month. Sales tools that build a habit (Slack, Figma) drive 130%+ NRR; tools used sporadically rarely exceed 110%.
For product teams, DAU/MAU is a leading indicator of feature impact. A feature that increases daily return rate by 5 percentage points within 60 days of launch is doing the most important job in product. A feature that increases MAU but not DAU is acquiring users without retaining them.
DAU/MAU formula
DAU/MAU = (Daily Active Users / Monthly Active Users) × 100 Where: - DAU (average) = mean of DAU counts over the month - MAU = distinct users active at least once in the month Example: - Average DAU (last 30 days): 240,000 - MAU (last 30 days): 600,000 DAU/MAU = 240,000 / 600,000 = 40% Interpretation: average user opens the product on ~12 of 30 days (40% × 30 = 12 days) Active = your defined "qualifying event" — open, session > 30s, key action completed. Always document the definition.
Benchmarks
| Product type | Best-in-class | Median | Below average |
|---|---|---|---|
| Consumer social (IG, Snap) | 55–70% | 30–50% | <20% |
| Music streaming (Spotify) | 45–65% | 25–40% | <15% |
| Productivity SaaS (Slack, Figma) | 60–80% (workdays) | 30–55% | <20% |
| B2B analytics dashboards | 20–40% | 10–20% | <8% |
| E-commerce | 8–18% | 4–8% | <3% |
| Banking / fintech | 15–35% | 8–18% | <5% |
| Transactional (ride share) | 5–15% | 3–8% | <2% |
Benchmarks compiled from a16z Growth Benchmarks 2025, Reforge Engagement Benchmarks 2025, and Amplitude Product Benchmarks 2025.
Common mistakes
- Using DAU/MAU for products not meant for daily use. A B2B HR tool used twice per month will always have low DAU/MAU — but that's not a problem if the product is designed for periodic use. Use the right cadence metric for the product (WAU/MAU for weekly tools, MAU growth for monthly tools).
- Inconsistent "active" definition. "Opened the app" produces different numbers from "completed a session > 30 seconds" or "performed a key action". Pick one definition, document it, and don't change it without retroactively recalculating history.
- Confusing DAU/MAU with retention. DAU/MAU is an engagement metric, not a retention metric. A product can have high DAU/MAU among current users but be losing users at the top of the funnel. Pair with retention curves for the full picture.
- Reporting only the average. DAU/MAU distribution often has a long tail. A 40% average might mean half your users are at 70%+ and half at 10% — a very different operational story than uniform 40%. Segment by cohort and persona.
- Comparing across product types. A 30% DAU/MAU is great for banking, bad for music streaming. Benchmark within product category, not across the industry.
Related metrics
DAU/MAU sits inside the engagement stack with WAU/MAU (weekly cadence), engagement rate, activation rate, and retention curve. For B2B SaaS, pair with NRR and customer health score — DAU/MAU is one of the strongest single inputs to a health-score model.
At a glance
- Category
- Sales Forecasting
- Related
- 4 terms
Frequently asked questions
What is a good DAU/MAU ratio?
Depends on product type. Consumer social: 50–70% best-in-class. Music streaming: 45–65%. Productivity SaaS used on workdays: 60–80%. B2B analytics: 20–40%. Banking: 15–35%. Always benchmark within product category — a 30% DAU/MAU is great for banking but weak for a music app.
What's the difference between DAU/MAU and WAU/MAU?
DAU/MAU = daily active / monthly active (% of days a user is active). WAU/MAU = weekly active / monthly active (% of weeks a user is active). DAU/MAU is the right metric for daily-use products (social, music, productivity SaaS). WAU/MAU is better for B2B SaaS used a few times per week — Figma reports WAU/MAU specifically because designers don't work in it every single day.
How do you calculate DAU/MAU?
Average daily active users over a 30-day window divided by monthly active users (distinct users active at least once in the same 30-day window). Multiply by 100 for the percentage. Both DAU and MAU should use the same definition of 'active' — typically a key action, not just an app open.
Why is DAU/MAU a 'stickiness' metric?
Because it measures how often the average monthly user returns within the month. A 50% DAU/MAU means the average monthly user opens the product on ~15 of 30 days — high return frequency. Low DAU/MAU means users open the product once or twice a month, which historically correlates with high churn risk.
Does DAU/MAU predict retention?
Indirectly. DAU/MAU measures current engagement of active users; it doesn't directly measure whether users stay over months. But high DAU/MAU is strongly correlated with high retention because daily habits are sticky. For accurate retention prediction, pair DAU/MAU with cohort retention curves.
Sources
- Andreessen Horowitz. Growth Benchmarks 2025, 2025. a16z.com
- Reforge. Engagement Benchmarks Report 2025, 2025. reforge.com
- Amplitude. Product Benchmarks Report 2025, 2025. amplitude.com
- Sequoia. The Arc Engagement Framework, 2024. sequoiacap.com
Fairview integrates DAU/MAU with NRR, expansion, and customer health scoring in one operating view — see the operating intelligence overview for the broader category.
Definitions and benchmarks reviewed by Siddharth Gangal, Founder, Fairview.
See it in Fairview
Track DAU/MAU Ratio automatically.
25-minute live demo. Tailored to your stack. See your operating view live.