Daily, Weekly, Monthly Active Users (DAU, WAU, MAU) - Cube

Use case

We want to know the customer engagement of our store. To do this, we need to use an Active Users metric.

<Note>

This recipe defines a separate measure for each fixed window (daily, weekly, monthly). To let a data consumer choose the window at query time instead, see Configurable rolling windows.

</Note>

Data modeling

Daily, weekly, and monthly active users are commonly referred to as DAU, WAU, MAU. To get these metrics, we need to use a rolling time frame to calculate a daily count of how many users interacted with the product or website in the prior day, 7 days, or 30 days. Also, we can build other metrics on top of these basic metrics. For example, the WAU to MAU ratio, which we can add by using already defined weekly_active_users and monthly_active_users.

To calculate daily, weekly, or monthly active users we’re going to use the rolling_window measure parameter.

yaml

cubes:
  - name: active_users
    sql: |
      SELECT user_id, created_at FROM public.orders

    measures:
      - name: monthly_active_users
        type: count_distinct
        sql: user_id
        rolling_window:
          trailing: 30 day
          offset: start

      - name: weekly_active_users
        type: count_distinct
        sql: user_id
        rolling_window:
          trailing: 7 day
          offset: start

      - name: daily_active_users
        type: count_distinct
        sql: user_id
        rolling_window:
          trailing: 1 day
          offset: start

      - name: wau_to_mau
        title: WAU to MAU
        type: number
        sql:
          "1.0 * {weekly_active_users} / NULLIF({monthly_active_users}, 0)"
        format: percent

    dimensions:
      - name: created_at
        type: time
        sql: created_at

javascript

cube(`active_users`, {
  sql: `SELECT user_id, created_at
    FROM public.orders`,

  measures: {
    monthly_active_users: {
      sql: `user_id`,
      type: `count_distinct`,
      rolling_window: {
        trailing: `30 day`,
        offset: `start`
      }
    },

    weekly_active_users: {
      sql: `user_id`,
      type: `count_distinct`,
      rolling_window: {
        trailing: `7 day`,
        offset: `start`
      }
    },

    daily_active_users: {
      sql: `user_id`,
      type: `count_distinct`,
      rolling_window: {
        trailing: `1 day`,
        offset: `start`
      }
    },

    wau_to_mau: {
      title: `WAU to MAU`,
      sql: `1.0 * ${weekly_active_users} / NULLIF(${monthly_active_users}, 0)`,
      type: `number`,
      format: `percent`
    }
  },

  dimensions: {
    created_at: {
      sql: `created_at`,
      type: `time`
    }
  }
})

</CodeGroup>

Result

Query all four measures with a timeDimensions date range to get the active user counts for any given day. For example, for a single day in 2020:

MAU	WAU	DAU	WAU/MAU
22	4	0	18.18%