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

Use case

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

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.

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:
          "100.000 * {weekly_active_users} / NULLIF({monthly_active_users}, 0)"
        format: percent
 
    dimensions:
      - name: created_at
        type: time
        sql: created_at
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: `100.000 * ${weekly_active_users} / NULLIF(${monthly_active_users}, 0)`,
      type: `number`,
      format: `percent`
    }
  },
 
  dimensions: {
    created_at: {
      sql: `created_at`,
      type: `time`
    }
  }
})

Query

We should set a timeDimensions with the dateRange.

curl cube:4000/cubejs-api/v1/load \
  'query={
    "measures": [
      "active_users.monthly_active_users",
      "active_users.weekly_active_users",
      "active_users.daily_active_users",
      "active_users.wau_to_mau"
    ],
    "timeDimensions": [
      {
        "dimension": "active_users.created_at",
        "dateRange": [
          "2020-01-01",
          "2020-12-31"
        ]
      }
    ]
  }'

Result

We got the data with our daily, weekly, and monthly active users.

{
  "data": [
    {
      "active_users.monthly_active_users": "22",
      "active_users.weekly_active_users": "4",
      "active_users.daily_active_users": "0",
      "active_users.wau_to_mau": "18.1818181818181818"
    }
  ]
}

Source code

Please feel free to check out the full source code  or run it with the docker-compose up command. You’ll see the result, including queried data, in the console.

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