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How to calculate DAU/MAU ratio

Formula, stickiness benchmarks by product type, and how to query it from event data

Definition

What it is

DAU/MAU ratio (Daily Active Users divided by Monthly Active Users) is a product engagement metric that measures "stickiness" — what percentage of your monthly active users return on any given day. A ratio of 0.50 means that on an average day, 50% of your monthly actives are using the product. It is widely used in SaaS, consumer apps, and analytics platforms as a proxy for habit formation and retention quality.

Formula

DAU/MAU Ratio Formula

Formula

DAU/MAU = (Daily Active Users on a given day ÷ Monthly Active Users in that month) × 100

"Active" must be defined consistently — typically a user who triggers at least one qualifying event (login, session start, or a meaningful in-app action). The ratio is most meaningful as a 30-day rolling average rather than a single day snapshot, which can be affected by day-of-week patterns.

Business impact

Why it matters

DAU/MAU is a leading indicator of long-term retention and revenue sustainability for subscription and usage-based products. A ratio above 20% is generally considered good; above 50% (Facebook historically benchmarked here) indicates exceptional stickiness. A declining DAU/MAU ratio is an early warning sign of engagement erosion — often visible weeks before it shows up in churn or revenue metrics, giving product teams time to respond.

The challenge

How most teams track this today

DAU/MAU is typically calculated from event tracking data stored in a data warehouse (Snowflake, BigQuery, Redshift, PostgreSQL). It requires two aggregations — unique active users per day and unique active users per month — then a division. This is straightforward SQL but requires access to the raw event tables rather than a pre-built analytics dashboard.

Run it on your data

Calculate this automatically with Taptic Data

Connect your Snowflake account and Taptic generates this calculation from plain English against your actual data — no Excel exports, no manual joins. The SQL runs against your real schema, your real tables, your real numbers.

SQL query for DAU/MAU ratio from Snowflake event data →Connect Snowflake →Try Free — $29.99/mo

FAQ

Common questions

What is a good DAU/MAU ratio?

Benchmarks vary by product type. Consumer social apps: 40–60% is excellent. B2B SaaS tools used daily (Slack, Notion): 25–50%. Analytics and reporting tools (used weekly): 15–25%. Anything below 10% in a daily-use product suggests a retention or engagement problem worth investigating.

How is DAU/MAU different from retention rate?

Retention rate measures whether a user returns in a subsequent period (D1, D7, D30 retention). DAU/MAU measures the intensity of engagement within a period — how many of your monthly actives are active on any given day. Both measure engagement health but from different angles. A product can have decent D30 retention but low DAU/MAU if users only log in once a month.

Can I calculate DAU/MAU in Taptic from Snowflake data?

Yes. If your event data is in Snowflake, connect your Snowflake account to Taptic and the AI generates schema-aware SQL against your actual events table. The DAU/MAU query uses window functions to calculate daily and monthly active user counts and outputs the stickiness ratio by day.

What if my events table has a different schema than the query expects?

Taptic reads your live schema before generating SQL — it does not use a fixed template. If your events table is called "user_events" with a "user_id" column instead of "events" with "uid", the generated SQL will use your actual column names. You can also ask the Data Partner AI to adjust the query for your schema in plain English.

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