DAU/MAU Ratio
The ratio of daily active users to monthly active users, expressing what percentage of monthly users engage on any given day. A higher ratio indicates stickier product engagement and stronger habitual usage patterns.
The DAU/MAU ratio, also called stickiness, measures how many of your monthly users come back on a daily basis. A ratio of 50% means that on any given day, half of your monthly active users are using the product. Social media platforms might target 50%+ while SaaS tools might aim for 20-30%, depending on natural usage frequency.
For growth teams, DAU/MAU is a powerful signal of product-market fit and engagement health. AI can enhance this metric's utility by decomposing it into contributing factors: is stickiness driven by a small group of power users or broad habitual usage? Are specific features or use cases driving daily return visits? Growth engineers should track DAU/MAU alongside complementary metrics like weekly active users and engagement depth to build a complete picture of usage patterns. The ratio alone can be misleading since a high DAU/MAU could reflect a small, intensely engaged user base rather than broad daily adoption. Segmenting the ratio by user type, acquisition source, and tenure reveals which user segments drive stickiness and which need engagement improvement. Teams should also track the ratio's trend over time, as declining stickiness in a growing user base often signals impending retention problems.
Related Terms
Event Tracking
The practice of recording specific user interactions within a digital product, such as clicks, form submissions, page views, and feature usage, as structured data events that can be analyzed to understand user behavior.
Event Taxonomy
A structured naming convention and classification system for analytics events that ensures consistency, discoverability, and usability of tracking data across teams, platforms, and analysis tools.
Funnel Analysis
The process of tracking and measuring user progression through a defined sequence of steps toward a conversion goal, identifying where users drop off and quantifying the conversion rate between each stage.
Conversion Rate Analytics
The systematic measurement and analysis of the percentage of users who complete a desired action out of the total who had the opportunity, applied across multiple conversion points throughout the user journey.
Drop-Off Rate
The percentage of users who leave a process or sequence at a specific step without completing the next step, the inverse of step-level conversion rate, used to identify friction points in user flows.
Cohort Analysis
A technique that groups users by a shared characteristic or experience within a defined time period and tracks their behavior over subsequent periods, revealing how user behavior evolves and differs across groups.