Weekly Active Users (WAU)¶

Definition¶

Weekly Active Users (WAU) measures the total number of unique users who engage with your product, service, or platform at least once during a specific week. It reflects the breadth of active engagement within a weekly timeframe.

Description¶

Weekly Active Users (WAU) is a key indicator of short-term engagement and product utility, reflecting how many unique users actively engage with your platform each week.

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

In SaaS, it highlights workflow adoption and habitual use
In Mobile apps, it reflects feature appeal and daily-use utility
In Media platforms, it surfaces content stickiness

A steady or growing WAU trend signals reliable usage and market fit, while dips may flag disengagement or feature fatigue. By segmenting by behavior, feature, or persona, you uncover which users are consistently finding value—and why.

Weekly Active Users informs:

Strategic decisions, like engagement roadmap and retention investment
Tactical actions, such as re-engagement campaigns or feature spotlights
Operational improvements, including load balancing, support readiness, and adoption tracking
Cross-functional alignment, syncing growth, product, and lifecycle teams around weekly engagement health

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

Core Use Case Frequency: If your product supports weekly (or more frequent) workflows, WAU should closely follow MAU. If not, your usage patterns may be too shallow.
Feature Adoption and Stickiness: Deeper feature use = more reasons to return weekly. Shallow users often ghost.
Engagement Nudges and Lifecycle Touchpoints: Emails, in-app prompts, and calendar-based workflows can all pull users back.

Improvement Tactics & Quick Wins¶

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

If WAU is low, identify your top-returning user segment and map their behavior. Replicate that journey for others.
Add recurring nudges (weekly digests, reminders, alerts) to encourage habitual usage.
Run feature adoption campaigns tied to core workflows used weekly (e.g., reporting, updates, planning).
Refine onboarding and activation to emphasize repeat-use features and sticky use cases.
Partner with lifecycle marketing and product to create behavior-based re-engagement flows (e.g., "You haven’t logged in this week — here’s what you missed").

Required Datapoints to calculate the metric
- Unique User Identifiers: Email, user ID, or cookies to track distinct users.
- Engagement Criteria: Actions or behaviors that define "active" (e.g., logins, purchases, uploads).
- Weekly Timeframe: A defined seven-day period for measuring activity.
Example to show how the metric is derived

A streaming platform measures WAU for a specific week:
- Total Unique Users Engaged: 25,000
- The platform launches a new content series, boosting WAU to 30,000 the following week.

Formula¶

Formula

\[ \mathrm{Weekly\ Active\ Users\ (WAU)} = \mathrm{Number\ of\ Unique\ Users\ Performing\ at\ Least\ One\ Active\ Action\ Within\ a\ Week} \]

Data Model Definition¶

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

UserEngagement

cube('UserEngagement', {
  sql: `SELECT * FROM user_engagements`,

  measures: {
    weeklyActiveUsers: {
      type: 'countDistinct',
      sql: `user_id`,
      title: 'Weekly Active Users',
      description: 'The total number of unique users who engage with the product at least once during a specific week.'
    }
  },

  dimensions: {
    userId: {
      sql: `user_id`,
      type: 'string',
      primaryKey: true,
      title: 'User ID',
      description: 'Unique identifier for each user.'
    },

    engagementTime: {
      sql: `engagement_time`,
      type: 'time',
      title: 'Engagement Time',
      description: 'The time when the user engaged with the product.'
    }
  },

  segments: {
    activeUsers: {
      sql: `${CUBE}.engagementTime >= DATE_SUB(CURRENT_DATE, INTERVAL 7 DAY)`
    }
  }
});

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.
- Customer Churn Rate: A high churn rate directly decreases the number of Weekly Active Users as users stop engaging with the product.
- Technical Issues: Frequent technical issues or downtime can frustrate users, leading to decreased weekly engagement and lower Weekly Active Users.
- Complexity of Use: If the product is perceived as complex or difficult to use, it can deter users from engaging weekly, reducing Weekly Active Users.
- Lack of New Features: A lack of new features or updates can lead to user boredom and decreased weekly engagement, negatively impacting Weekly Active Users.
- Poor Onboarding Experience: A poor onboarding experience can result in users not fully understanding the product's value, leading to lower weekly engagement and fewer Weekly Active Users.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Core Use Case Frequency: A higher frequency of core use case engagement leads to an increase in Weekly Active Users as users find more value and reasons to return weekly.
- Feature Adoption and Stickiness: Increased adoption of features and stickiness results in more consistent weekly engagement, boosting Weekly Active Users.
- Engagement Nudges: Effective use of engagement nudges such as emails and in-app prompts can remind and encourage users to return, positively impacting Weekly Active Users.
- Lifecycle Touchpoints: Strategically timed lifecycle touchpoints can re-engage users and increase their weekly activity, thus increasing Weekly Active Users.
- User Satisfaction: High user satisfaction leads to more frequent use and recommendations, which can increase Weekly Active Users.

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
Community Building
Engagement Campaigns

Funnel Stage & Type¶

AAARRR Funnel Stage

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

Activation
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.
- Monthly Active Users: MAU is a broader engagement metric that contextualizes WAU, helping to identify trends in user stickiness and overall platform health. Spikes or drops in MAU often precede or magnify movements in WAU, forming a multi-signal early warning system for changes in user engagement habits.
- DAU/WAU Ratio: This ratio measures how many weekly active users are also daily active, offering insight into the habitual use of the product. A rising DAU/WAU ratio can signal increased engagement depth and forecast future increases in WAU.
- WAU/MAU Ratio: This metric shows the proportion of monthly active users who are active weekly, directly relating to WAU. An increase in this ratio predicts stickier engagement and helps foresee sustained or growing WAU.
- Unique Visitors: Unique Visitors measures the inflow of potential users; a surge in unique visitors often precedes increases in WAU by feeding the top of the engagement funnel.
- Product Qualified Leads: PQLs are users demonstrating high-value engagement, often converting to repeat activity. Growth in PQLs signals a pipeline of engaged users likely to become weekly active users, forecasting WAU increases.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Activation Cohort Retention Rate (Day 7/30): This measures how many activated users remain engaged after a week or a month. High retention rates validate that WAU is capturing meaningful, lasting engagement and can recalibrate expectations for future weekly activity trends.
- Customer Feedback Retention Score: Retention among users who provide feedback highlights loyalty and satisfaction. Analyzing this metric in conjunction with WAU can reveal if weekly engagement is translating into sustained customer loyalty, leading to better forecasting and targeted engagement strategies.
- Churn Risk Score: Elevated churn risk among active users suggests that high WAU may not be sustainable. This lagging insight helps refine how WAU is interpreted and prompts adjustments to engagement strategies to improve prediction accuracy.
- Customer Engagement Score: This composite lagging metric quantifies the depth and consistency of user interactions. It can help recalibrate the significance of WAU, revealing whether increases in WAU are driven by meaningful engagement or superficial activity.
- Activation Rate by Source: By assessing how well new users from different sources reach activation, this metric can help refine WAU forecasting by linking acquisition channel quality to the likelihood of users becoming weekly active.