Monthly Active Users (MAU)

Monthly Active Users (MAU) is a key performance metric that measures the number of unique users who engage with your product or service within a 30-day period. This metric provides crucial insight into your user base’s size and engagement level, helping businesses understand their reach and growth trajectory. The MAU formula is straightforward: count each unique user only once during the monthly period, regardless of how many times they interact with your product.

MAU serves as a critical decision-making tool for product development, marketing investments, and business strategy. It helps companies assess whether their user acquisition efforts are working and if they’re successfully retaining customers over time. A high MAU indicates strong user engagement and market traction, while a declining MAU may signal product-market fit issues or increased competition requiring immediate attention.

This metric works best when analyzed alongside related engagement metrics like Daily Active Users (DAU), Weekly Active Users (WAU), and User Retention Rate. Understanding the relationship between these metrics through Cohort Analysis can reveal deeper insights into user behavior patterns and help identify potential Churn Rate issues before they impact long-term growth.

Monthly Active Users (MAU) is calculated by counting the number of unique users who performed at least one meaningful action in your product during a 30-day period. The basic formula is straightforward:

Formula:
Monthly Active Users (MAU) = Count of Unique Active Users in 30-Day Period

The numerator represents the total number of distinct users who engaged with your product during the measurement window. “Engagement” typically means performing a core action that demonstrates value - such as logging in, making a purchase, posting content, or using a key feature. The key word here is “unique” - if a user is active multiple times during the month, they’re only counted once.

You’ll typically gather this data from your analytics platform, user database, or product tracking tools by filtering for users who completed defined actions within your chosen 30-day window.

Worked Example

Let’s say you’re analyzing MAU for a social media app in March:

• Total registered users who logged in during March: 45,000
• Total registered users who posted content during March: 32,000
• Total registered users who engaged (liked, commented, shared) during March: 38,000

Since a user might have done multiple activities, you need to count unique users across all these actions. After deduplicating, you find 42,000 unique users performed at least one of these activities in March.

Therefore: MAU = 42,000

Variants

Rolling 30-day MAU measures users active in the past 30 days from any given date, providing a more dynamic view than calendar month MAU. This variant is particularly useful for tracking trends and seasonal patterns.

Segmented MAU breaks down active users by cohorts, geographic regions, subscription tiers, or user types. For example, you might track “Premium MAU” separately from “Free MAU” to understand engagement across different user segments.

Feature-specific MAU focuses on users engaging with particular product features, helping teams understand which capabilities drive the most engagement.

Common Mistakes

Inconsistent activity definitions plague many MAU calculations. Teams often change what constitutes “active” behavior over time, making historical comparisons meaningless. Establish clear, stable criteria for what actions qualify as “active.”

Double-counting across platforms occurs when users access your product via multiple channels (web, mobile app, API). Ensure your calculation methodology properly deduplicates users across all touchpoints.

Ignoring data quality issues can significantly skew results. Bot traffic, test accounts, employee usage, and spam accounts should be filtered out to maintain accurate MAU measurements that reflect genuine user engagement.

It’s natural to want benchmarks for Monthly Active Users, but context matters enormously. While industry benchmarks can guide your thinking and help identify when something might be off, they shouldn’t be treated as strict rules or targets to hit at all costs.

MAU Benchmarks by Industry and Stage

Sources: Industry estimates from OpenView SaaS Benchmarks, Bessemer Venture Partners State of the Cloud

Understanding Benchmarks in Context

Benchmarks help establish whether your MAU performance is in a reasonable range, but they’re most valuable for spotting potential issues rather than setting absolute targets. Your specific market, product complexity, pricing model, and customer acquisition strategy all influence what constitutes a “good” MAU for your business.

More importantly, metrics exist in tension with each other. Optimizing MAU in isolation can lead to poor outcomes elsewhere. You might inflate your MAU by loosening your product’s value proposition or reducing pricing, but this could hurt revenue per user or long-term retention.

How MAU Interacts with Related Metrics

Consider how MAU connects to other key metrics in your business. If you’re moving upmarket to higher-value enterprise customers, your MAU growth might slow as you focus on fewer, more valuable accounts. Conversely, if you’re seeing strong MAU growth but declining user retention rate, you might be acquiring users who don’t find lasting value in your product. Similarly, rapid MAU growth paired with increasing churn rate often signals product-market fit issues that need addressing before scaling further.

The key is monitoring MAU alongside metrics like Daily Active Users (DAU), Weekly Active Users (WAU), and cohort analysis to understand the full picture of user engagement and business health.

When your MAU starts dropping, it’s rarely a single issue—it’s usually a combination of factors creating a downward spiral. Here’s how to diagnose what’s happening.

Poor User Onboarding Experience
Look for high drop-off rates in your first-week retention metrics. If new users aren’t reaching their “aha moment” quickly, they’ll never become active monthly users. Check your User Retention Rate for new cohorts—if Day 1 and Day 7 retention are declining, your onboarding needs work. The fix involves streamlining your initial user experience and reducing time-to-value.

Increasing Churn Rate
Rising Churn Rate directly erodes your MAU base. Monitor your monthly churn trends—if more users are leaving than joining, MAU will inevitably decline. Pay special attention to early-stage churn (users leaving within their first month) versus mature user churn. Use Cohort Analysis to identify which user segments are churning most.

Declining User Engagement
Even retained users might be becoming less active. Check if your Daily Active Users (DAU) and Weekly Active Users (WAU) are dropping faster than MAU—this indicates existing users are engaging less frequently. Look for changes in feature usage patterns, session duration, and core action completion rates.

Acquisition Channel Problems
Your user acquisition might be bringing in lower-quality users who don’t stick around. Analyze MAU by acquisition channel—if certain channels show declining contribution or poor retention rates, your acquisition strategy needs adjustment.

Product-Market Fit Erosion
Sometimes external factors—new competitors, changing user needs, or market shifts—can cause previously engaged users to become inactive. Monitor user feedback, support tickets, and competitive landscape changes to identify if fundamental product issues are driving MAU decline.

Fix Your Onboarding Flow
Start by analyzing where users drop off during their first session using cohort analysis. Identify the specific steps where engagement falls and streamline your onboarding to get users to their first “aha moment” faster. A/B test different onboarding sequences to validate which approach drives higher activation rates. Track how changes impact both immediate engagement and 30-day retention.

Re-engage Dormant Users with Targeted Campaigns
Segment your user base to identify those who were active but haven’t engaged recently. Create personalized re-engagement campaigns based on their previous behavior patterns—if they used feature X heavily, highlight updates to that feature. Use Explore Monthly Active Users (MAU) using your PostHog data | Count to identify which user segments have the highest reactivation potential.

Improve Core Product Value Delivery
Examine your User Retention Rate alongside MAU trends to understand if users are getting ongoing value. Focus on the features that correlate strongest with long-term engagement. Remove friction from your most valuable user flows and ensure key features are discoverable. Track feature adoption rates to validate improvements.

Optimize Your Activation Funnel
Look at the relationship between Daily Active Users (DAU) and Weekly Active Users (WAU) to understand usage patterns. If DAU/MAU ratio is low, focus on building habits through push notifications, email sequences, or in-app prompts that bring users back regularly. Test different cadences and messaging to find what drives consistent engagement.

Address Churn Root Causes
Use your Churn Rate data to identify why users leave, then build retention features targeting those specific issues. Whether it’s missing functionality, poor performance, or competition, addressing churn directly impacts MAU growth by reducing the number of users you need to replace each month.

Stop calculating Monthly Active Users (MAU) in spreadsheets. Connect your data source and ask Count to calculate, segment, and diagnose your Monthly Active Users (MAU) in seconds—no complex queries or manual analysis required.