Daily active users divided by monthly active users, measuring product stickiness and engagement depth.
Daily Active Users / Monthly Active Users
Higher is better · Unit: ratio
How to Improve
Design "hook model" features that create daily habits around your product. Build a newsfeed or dashboard that provides fresh, relevant content daily. Implement push notifications and email triggers based on real‑time events. Create team collaboration features that require daily coordination. Launch a mobile app or browser extension that lowers the barrier to daily usage.
Ehsan's Analysis
AI tool DAU/MAU is rapidly segmenting into two tiers: "daily driver" tools (GitHub Copilot 55%+, Grammarly 50%+, ChatGPT 35-40%) and "occasional use" tools (Midjourney 15%, Jasper 20%, most AI writing tools 10-20%). The daily drivers share one characteristic: they are embedded in a daily workflow (IDE, email, browser). The occasional tools require a separate tab, a separate intent, a separate workflow. No amount of notification engineering will push an occasional tool to daily-driver status — the architecture of how and where the tool is accessed determines the ceiling. The AI DAU/MAU lesson: if your tool is not currently achieving 30%+ DAU/MAU, the fix is not "better engagement features." The fix is embedding your tool into a workflow the user already performs daily. Grammarly's browser extension strategy was the right move a decade ago and remains the correct playbook for AI tools today.
EJ
Ehsan Jahandarpour
AI Growth Strategist & Fractional CMO
Forbes Top 20 Growth Hacker · TEDx Speaker · 716 Academic Citations · Ex-Microsoft · CMO at FirstWave (ASX:FCT) · Forbes Communications Council
Frequently Asked Questions
What is a good DAU/MAU Ratio for AI/ML companies at Series A stage?
The median DAU/MAU Ratio for AI/ML companies at the Series A stage is 0.27. Top‑quartile companies (75th percentile) significantly outperform this baseline. The most important factor is consistent improvement over time rather than hitting any single target number.
How does DAU/MAU Ratio differ by company stage in AI/ML?
DAU/MAU Ratio typically improves as AI/ML companies mature from seed through growth stage. Earlier‑stage companies should benchmark against stage‑appropriate peers rather than comparing themselves to mature companies.
How often should AI/ML companies measure DAU/MAU Ratio?
AI/ML companies at the Series A stage should track DAU/MAU Ratio monthly with quarterly deep‑dive analysis. Set up automated dashboards and alerts for significant deviations from your baseline.
What factors most impact DAU/MAU Ratio in the AI/ML sector?
In AI/ML, the primary factors impacting DAU/MAU Ratio include product‑market fit maturity, competitive landscape intensity, customer segmentation strategy, pricing optimization, and operational efficiency. Series A‑stage companies should focus on the one or two highest‑leverage factors rather than trying to optimize everything simultaneously.
How does DAU/MAU Ratio for AI/ML compare to cross‑industry benchmarks?
AI/ML DAU/MAU Ratio benchmarks can differ significantly from cross‑industry averages due to factors specific to the AI/ML vertical including customer acquisition dynamics, competitive intensity, and typical deal sizes. Always compare against industry‑specific benchmarks rather than broad averages for meaningful insights.