WAU/MAU Ratio¶

Definition¶

The WAU/MAU Ratio compares the number of Weekly Active Users (WAU) to Monthly Active Users (MAU). It represents the percentage of users who engage with your product weekly out of those who are active within a month.

Description¶

WAU/MAU Ratio is a key indicator of product stickiness and habitual usage, reflecting how many monthly users return on a weekly basis.

The relevance and interpretation of this metric shift depending on the model or product:

In SaaS, it highlights feature frequency and user reliance
In Consumer apps, it reflects recurring value delivery
In PLG tools, it surfaces depth of adoption and activation success

A higher ratio means your product is becoming part of a weekly routine, while a lower one may flag engagement drop-off or limited utility. By segmenting by feature usage, role, or region, you uncover how behavior varies—and where to reinforce habits.

WAU/MAU Ratio informs:

Strategic decisions, like retention roadmap priorities and engagement bets
Tactical actions, such as push reminders, email nudges, or weekly-use incentives
Operational improvements, including feature discoverability and usage onboarding
Cross-functional alignment, helping product, lifecycle, and growth teams target habitual use at scale

Key Drivers¶

These are the main factors that directly impact the metric. Understanding these lets you know what levers you can pull to improve the outcome

Use Case Frequency: If your product supports weekly workflows, WAU/MAU should be ~0.5–0.7+.
Recurring Engagement Prompts: Email digests, usage reminders, and calendar-based nudges help.
UX Quality and Workflow Efficiency: If users have a hard time completing tasks, they won’t come back.

Improvement Tactics & Quick Wins¶

Actionable ideas to optimize this KPI, from fast, low-effort wins to strategic initiatives that drive measurable impact.

If stickiness is low, build recurring prompts tied to use-case timing (weekly reports, reviews, etc.).
Add value loop features that nudge return behavior (“Your dashboard updated — check it out”).
Run segmentation on power users to model ideal return paths.
Refine time-based workflows for roles that work in bursts or project cycles.
Partner with lifecycle and PM to automate re-engagement nudges.

Required Datapoints to calculate the metric
- WAU (Weekly Active Users): Unique users interacting with your product during a 7-day period.
- MAU (Monthly Active Users): Unique users interacting with your product during a 30-day period.
Example to show how the metric is derived

A task management app tracks WAU and MAU in March:
- WAU = 20,000
- MAU = 50,000
- WAU/MAU Ratio = (20,000 / 50,000) × 100 = 40%

Formula¶

Formula

\[ \mathrm{WAU/MAU\ Ratio} = \left( \frac{\mathrm{WAU}}{\mathrm{MAU}} \right) \times 100 \]

Data Model Definition¶

How this KPI is structured in Cube.js, including its key measures, dimensions, and calculation logic for consistent reporting.

UserActivity

cube('UserActivity', {
  sql: `SELECT * FROM user_activity`,

  measures: {
    weeklyActiveUsers: {
      sql: `user_id`,
      type: 'countDistinct',
      title: 'Weekly Active Users',
      description: 'Unique users interacting with the product during a 7-day period.'
    },
    monthlyActiveUsers: {
      sql: `user_id`,
      type: 'countDistinct',
      title: 'Monthly Active Users',
      description: 'Unique users interacting with the product during a 30-day period.'
    },
    wauMauRatio: {
      sql: `100.0 * ${weeklyActiveUsers} / NULLIF(${monthlyActiveUsers}, 0)`,
      type: 'number',
      title: 'WAU/MAU Ratio',
      description: 'Percentage of users who engage weekly out of those active within a month.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true
    },
    userId: {
      sql: `user_id`,
      type: 'string',
      title: 'User ID'
    },
    activityDate: {
      sql: `activity_date`,
      type: 'time',
      title: 'Activity Date'
    }
  }
});

Note: This is a reference implementation and should be used as a starting point. You’ll need to adapt it to match your own data model and schema

Positive & Negative Influences¶

Negative influences

Factors that drive the metric in an undesirable direction, often signaling risk or decline.
- Complex User Interface: A complex or confusing user interface can deter users from engaging frequently, negatively affecting the WAU/MAU Ratio.
- Lack of Engagement Prompts: The absence of reminders or prompts can lead to decreased weekly engagement, reducing the WAU/MAU Ratio.
- Low Use Case Frequency: If the product does not support frequent use cases, users may not engage weekly, lowering the WAU/MAU Ratio.
- Technical Issues: Frequent technical issues or downtime can frustrate users, leading to reduced weekly engagement and a lower WAU/MAU Ratio.
- Poor Customer Support: Inadequate customer support can result in unresolved user issues, discouraging frequent use and negatively impacting the WAU/MAU Ratio.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Use Case Frequency: A higher frequency of use cases that align with weekly workflows increases the WAU/MAU Ratio, as users are more likely to engage with the product on a weekly basis.
- Recurring Engagement Prompts: Effective use of email digests, usage reminders, and calendar-based nudges can increase weekly engagement, thereby improving the WAU/MAU Ratio.
- UX Quality: High-quality user experience encourages users to return more frequently, positively impacting the WAU/MAU Ratio.
- Workflow Efficiency: Efficient workflows that allow users to complete tasks easily and quickly can lead to more frequent usage, boosting the WAU/MAU Ratio.
- Feature Updates: Regular updates that introduce new features or improve existing ones can re-engage users and increase weekly activity, enhancing the WAU/MAU Ratio.

Involved Roles & Activities¶

Involved Roles

These roles are typically responsible for implementing or monitoring this KPI:

Data & Analytics
Product Management (PM)
Product Marketing (PMM)
Activities

Common initiatives or actions associated with this KPI:

Product Adoption and Use
Retention Strategies
Habit Formation Campaigns

Funnel Stage & Type¶

AAARRR Funnel Stage

This KPI is associated with the following stages in the AAARRR (Pirate Metrics) funnel:

Activation
Retention
Type

This KPI is classified as a Lagging Indicator. It reflects the results of past actions or behaviors and is used to validate performance or assess the impact of previous strategies.

Supporting Leading & Lagging Metrics¶

Leading

These leading indicators influence this KPI and act as early signals that forecast future changes in this KPI.
- Weekly Active Users: WAU is the numerator of the WAU/MAU Ratio and directly determines its value. Rising WAU compared to MAU signals higher weekly engagement and user stickiness, providing an early indicator of active usage patterns.
- Monthly Active Users: MAU is the denominator of the WAU/MAU Ratio and shifts in MAU (with static or fluctuating WAU) can significantly impact the ratio. Tracking MAU alongside WAU gives a fuller picture of engagement health and user retention.
- Stickiness Ratio: The DAU/MAU stickiness ratio is conceptually similar and correlated with WAU/MAU. Monitoring both ratios can help triangulate user engagement frequency and identify changes in usage cadence.
- Activation Rate: Higher activation rates suggest more users are reaching key onboarding milestones, increasing the likelihood of those users becoming weekly and monthly actives. This helps forecast future improvements in the WAU/MAU Ratio.
- Product Qualified Leads: Growth in PQLs indicates more users are achieving high-value product engagement, which often translates into more consistent weekly and monthly activity, lifting the WAU/MAU Ratio.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Customer Engagement Score: This score aggregates usage frequency and depth, confirming whether high WAU/MAU Ratio translates to sustained, quality engagement and can be used to recalibrate or validate the predictive power of WAU/MAU.
- Churn Risk Score: Elevated churn risk among users often follows periods of declining engagement frequency. Analyzing correlation with WAU/MAU Ratio trends helps refine how leading engagement signals forecast retention risk.
- Customer Feedback Retention Score: If feedback-engaged users have higher retention, this can validate whether a high WAU/MAU Ratio is predictive of long-term loyalty or if certain engagement types are more meaningful.
- Customer Downgrade Rate: Spikes in downgrade rate may be preceded by drops in WAU/MAU Ratio. Reviewing this relationship provides feedback to adjust the importance or thresholds of the leading metric.
- Net Revenue Retention: Strong WAU/MAU ratios should, over time, contribute to higher net revenue retention. Comparing lagging revenue retention with engagement patterns helps recalibrate WAU/MAU as a forecasting tool for account health.