DAU/MAU is the standard measure of product stickiness. This query calculates it from your Snowflake event data month by month, so you can track engagement trends over time.
The question
The SQL Taptic generates
WITH daily_active AS ( SELECT DATE_TRUNC('month', event_date)::date AS month, event_date::date AS day, COUNT(DISTINCT user_id) AS dau FROM user_events WHERE event_date >= DATEADD('month', -6, CURRENT_DATE) GROUP BY 1, 2 ), monthly_active AS ( SELECT DATE_TRUNC('month', event_date)::date AS month, COUNT(DISTINCT user_id) AS mau FROM user_events WHERE event_date >= DATEADD('month', -6, CURRENT_DATE) GROUP BY 1 ), avg_dau AS ( SELECT month, ROUND(AVG(dau), 0) AS avg_dau FROM daily_active GROUP BY month ) SELECT TO_CHAR(m.month, 'YYYY-MM') AS month, a.avg_dau, m.mau, ROUND(a.avg_dau * 100.0 / NULLIF(m.mau, 0), 1) AS dau_mau_ratio_pct FROM monthly_active m JOIN avg_dau a ON a.month = m.month ORDER BY month ASC
This query was generated by Taptic Data from plain English against a real Snowflake schema. In Taptic, you type the question — the AI writes the SQL, runs it, and returns the result. You can edit the SQL, ask for explanations, and save it as a refreshable report. This type of analysis is commonly used in ai business analytics and text to sql tool.
How this query works
What it returns
Monthly table with average DAU, MAU, and DAU/MAU ratio as a percentage for the last 6 months — ready to trend as a line chart.
Why it matters
All queries and use cases on this page relate to analyzing your Snowflake data in Taptic. To see the full analytics workflow, explore ai business analytics, text to sql tool.
FAQ
Generate this automatically
In Taptic Data, you type "Show me daily active users and monthly active users ratio fo..." and this SQL runs automatically against your real Snowflake data.
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