Explore Monthly Active Users (MAU) using your PostHog data
Monthly Active Users (MAU) in PostHog
Monthly Active Users (MAU) represents the number of unique users who engage with your product within a 30-day period, making it a critical growth metric for PostHog users tracking product adoption and user engagement trends. PostHog’s rich event data—including user sessions, feature interactions, page views, and custom events—provides the foundation for sophisticated MAU analysis that goes beyond simple user counts to understand engagement patterns, feature adoption, and user lifecycle stages.
For PostHog users, MAU analysis can inform crucial decisions about product development priorities, marketing campaign effectiveness, and user retention strategies. By segmenting MAU data across different user cohorts, geographic regions, or feature usage patterns, teams can identify which product areas drive sustained engagement and where users might be dropping off.
However, calculating and analyzing MAU manually presents significant challenges. Spreadsheet analysis becomes unwieldy when exploring multiple user segments, time periods, and event combinations—leading to formula errors and outdated insights. PostHog’s built-in reporting tools, while useful for basic MAU tracking, offer limited flexibility for deep-dive analysis. They can’t easily answer follow-up questions like “Which features correlate with higher MAU retention?” or “How does MAU vary across different user acquisition channels?”
Count transforms your PostHog data into an interactive analytics environment where you can explore MAU trends dynamically, segment users across multiple dimensions, and uncover actionable insights that drive sustainable growth.
Questions You Can Answer
What’s my current monthly active users count from PostHog?
This gives you the baseline MAU metric to understand your current user engagement level and establish a foundation for growth tracking.
How has my monthly active users trend changed over the past 6 months?
Reveals growth patterns and seasonal variations in user engagement, helping you identify whether your product adoption is accelerating or declining.
What’s the monthly active users definition and how does my MAU compare to industry benchmarks?
Understanding the monthly active users definition ensures accurate measurement, while benchmark comparisons help contextualize your performance against similar products.
Which PostHog events contribute most to my monthly active user count?
Identifies the key user actions driving engagement, revealing which features or workflows are most critical for retention and helping you understand how to increase monthly active users.
How do my monthly active users break down by PostHog user properties like device type, location, or acquisition channel?
Uncovers which user segments are most engaged, enabling targeted optimization strategies for different cohorts.
What’s the overlap between my monthly active users and users who completed key conversion events in PostHog?
Connects engagement metrics to business outcomes, showing how MAU correlates with revenue-driving behaviors and identifying high-value user patterns.
How Count Analyses Monthly Active Users (MAU)
Count transforms your PostHog MAU analysis from basic metric tracking into deep, actionable intelligence. Unlike rigid dashboard templates, Count’s AI agent writes custom SQL and Python logic specifically for your monthly active users definition and unique business context.
When you ask about MAU trends, Count runs hundreds of queries in seconds, automatically segmenting your PostHog user data by acquisition channel, feature usage, geography, and device type to uncover hidden patterns. It might discover that your mobile users have 40% higher retention rates, or that users from organic search convert to active status faster than paid traffic.
Count handles PostHog’s event-based data structure seamlessly, cleaning inconsistencies like duplicate user IDs or timezone variations that could skew your MAU calculations. The platform provides complete transparency—showing exactly how it defined “active,” which events it counted, and any data transformations applied.
Your analysis becomes presentation-ready instantly, with clear visualizations showing not just current MAU, but cohort breakdowns, seasonal trends, and correlation with product releases. Count connects your PostHog data with other sources like your CRM or support tickets, revealing how to increase monthly active users by identifying which onboarding sequences drive long-term engagement.
The collaborative workspace lets your team dive deeper together—asking follow-up questions like “Which features predict MAU growth?” or “How does MAU vary by user segment?” Each query builds on previous insights, creating a comprehensive understanding of your user engagement patterns.