Active User Metrics (HAU, DAU, WAU, MAU)

Metrics represent the count of unique users who engage with a digital product or service within a specific timeframe. These metrics are the primary way to quantify the reach, engagement, and recurring usage of an application.

The Temporal Hierarchy

The power of these metrics lies in their ability to capture usage behaviour across different periods. Every metric is calculated as the count of distinct user IDs that have triggered a predefined event within the following time frames:

Critical Technical Considerations

1. Defining the event to count

A common pitfall in interpreting these metrics is uncertainty. Activeness is not a universal constant. Organisations define an active user based on their specific goals:

• Minimalist definition: A user who simply opens the application or faces a landing page.
• Substantive definition: A user who performs a valuable action, like creating a post, completing a transaction, or watching a video.
• Result: Comparing the active users of two different platforms is only valid if both entities define active users in the same way.

2. Unique user duplicates clean up

The unique aspect is a critical technical constraint. These metrics must be cleaned up of duplicates. If a user logs in five times a day, they still have to be counted as one DAU. Systems typically handle this by tracking unique identifiers, like UserID, device ID, or hashed email, within the database logs for the specified query range.

3. Meaningful Insights: The Ratio Analysis

Analysts use these metrics to derive behavioural trends:

• Stickiness (also known as engagement rate): It is calculated as MAUDAU​. This represents the percentage of monthly users who return to the product on any given day. A higher ratio indicates that the product has successfully woven itself into the user's daily habits.
• Retention velocity: By comparing WAU to MAU, it is possible to estimate how frequently users fall out of the active user base, helping to identify potential churn issues.

Why Context Matters

These metrics are  dependent on the natural frequency of the product. An application designed for filing taxes has a high MAU but a naturally low DAU. Conversely, a news aggregator would be considered failing if its DAU was not significantly higher than its MAU.