> For the complete documentation index, see [llms.txt](https://til.duyet.net/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://til.duyet.net/db/google-bigquery/bigquery-sample-queries-for-audiences-based.md).

# Bigquery - Sample queries for audiences based

Source: <https://support.google.com/firebase/answer/9037342?hl=en>

### N-day active users <a href="#ndayactives" id="ndayactives"></a>

```sql
/**
 * Builds an audience of N-Day Active Users.
 *
 * N-day active users = users who have logged at least one user_engagement
 * event in the last N days.
*/
SELECT
  COUNT(DISTINCT user_id) AS n_day_active_users_count
FROM
  -- PLEASE REPLACE WITH YOUR TABLE NAME.
  `YOUR_TABLE.events_*`
WHERE
  event_name = 'user_engagement'
  -- Pick events in the last N = 20 days.
  AND event_timestamp >
      UNIX_MICROS(TIMESTAMP_SUB(CURRENT_TIMESTAMP, INTERVAL 20 DAY))
  -- PLEASE REPLACE WITH YOUR DESIRED DATE RANGE.
  AND _TABLE_SUFFIX BETWEEN '20180521' AND '20240131';

```

### N-day inactive users <a href="#ndayinactives" id="ndayinactives"></a>

```sql
/**
 * Builds an audience of N-Day Inactive Users.
 *
 * N-Day inactive users = users in the last M days who have not logged a
 *   user_engagement event in the last N days where M > N.
 */
SELECT
  COUNT(DISTINCT MDaysUsers.user_id) AS n_day_inactive_users_count
FROM
  (
    SELECT
      user_id
    FROM
      /* PLEASE REPLACE WITH YOUR TABLE NAME */
      `YOUR_TABLE.events_*`
    WHERE
      event_name = 'user_engagement'
      /* Has engaged in last M = 7 days */
      AND event_timestamp >
          UNIX_MICROS(TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY))
      /* PLEASE REPLACE WITH YOUR DESIRED DATE RANGE */
      AND _TABLE_SUFFIX BETWEEN '20180521' AND '20240131'
  ) AS MDaysUsers
-- EXCEPT ALL is not yet implemented in BigQuery. Use LEFT JOIN in the interim.
LEFT JOIN
  (
    SELECT
      user_id
    FROM
      /* PLEASE REPLACE WITH YOUR TABLE NAME */
      `YOUR_TABLE.events_*`
    WHERE
      event_name = 'user_engagement'
      /* Has engaged in last N = 2 days */
      AND event_timestamp >
          UNIX_MICROS(TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 2 DAY))
      /* PLEASE REPLACE WITH YOUR DESIRED DATE RANGE */
      AND _TABLE_SUFFIX BETWEEN '20180521' AND '20240131'
  ) AS NDaysUsers
  ON MDaysUsers.user_id = NDaysUsers.user_id
WHERE
  NDaysUsers.user_id IS NULL;
```

### Frequently active users <a href="#frequentactives" id="frequentactives"></a>

```sql
/**
 * Builds an audience of Frequently Active Users.
 *
 * Frequently Active Users = users who have logged at least once
 * 'user_engagement' event on N of the last M days where M > N.
 */
SELECT
  COUNT(DISTINCT user_id) AS frequent_active_users_count
FROM
  (
    SELECT
      user_id,
      COUNT(DISTINCT event_date)
    FROM
      -- PLEASE REPLACE WITH YOUR TABLE NAME.
      `YOUR_TABLE.events_*`
    WHERE
      event_name = 'user_engagement'
      -- User engagement in the last M = 10 days.
      AND event_timestamp >
          UNIX_MICROS(TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 10 DAY))
      -- PLEASE REPLACE YOUR DESIRED DATE RANGE.  For optimal performance
      -- the _TABLE_SUFFIX range should match the INTERVAL value above.
      AND _TABLE_SUFFIX BETWEEN '20180521' AND '20240131'
    GROUP BY 1
    -- Having engaged in at least N = 4 days.
    HAVING COUNT(event_date) >= 4
  );

```

### Highly active users <a href="#highactives" id="highactives"></a>

```sql
/**
 * Builds an audience of Highly Active Users.
 *
 * Highly Active Users = users who have been active for more than N minutes
 * in the last M days where M > N.
*/
SELECT
  COUNT(DISTINCT user_id) AS high_active_users_count
FROM
  (
    SELECT
      user_id,
      event_params.key,
      SUM(event_params.value.int_value)
    FROM
      -- PLEASE REPLACE WITH YOUR TABLE NAME.
      `YOUR_TABLE.events_*` AS T
    CROSS JOIN
      T.event_params
    WHERE
      event_name = 'user_engagement'
      -- User engagement in the last M = 10 days.
      AND event_timestamp >
          UNIX_MICROS(TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 10 DAY))
      AND event_params.key = 'engagement_time_msec'
      -- PLEASE REPLACE YOUR DESIRED DATE RANGE.
      AND _TABLE_SUFFIX BETWEEN '20180521' AND '20240131'
    GROUP BY 1, 2
    HAVING
      -- Having engaged for more than N = 0.1 minutes.
      SUM(event_params.value.int_value) > 0.1 * 60 * 1000000
  );

```

### Acquired users <a href="#acquired" id="acquired"></a>

```sql
/**
 * Builds an audience of Acquired Users.
 *
 * Acquired Users = users who were acquired via some Source/Medium/Campaign.
 */
SELECT
  COUNT(DISTINCT user_id) AS acquired_users_count
FROM
  -- PLEASE REPLACE WITH YOUR TABLE NAME.
  `YOUR_TABLE.events_*`
WHERE
  traffic_source.source = 'google'
  AND traffic_source.medium = 'cpc'
  AND traffic_source.name = 'VTA-Test-Android'
  -- PLEASE REPLACE YOUR DESIRED DATE RANGE.
  AND _TABLE_SUFFIX BETWEEN '20180521' AND '20240131';
```

### Cohorts with filters <a href="#cohorts" id="cohorts"></a>

```sql
/**
 * Builds an audience composed of users acquired last week
 * through Google campaigns, i.e., cohorts with filters.
 *
 * Cohort is defined as users acquired last week, i.e. between 7 - 14
 * days ago. The cohort filter is for users acquired through a direct
 * campaign.
 */
SELECT
  COUNT(DISTINCT user_id) AS users_acquired_through_google_count
FROM
  -- PLEASE REPLACE WITH YOUR TABLE NAME.
  `YOUR_TABLE.events_*`
WHERE
  event_name = 'first_open'
  -- Cohort: opened app 1-2 weeks ago. One week of cohort, aka. weekly.
  AND event_timestamp >
      UNIX_MICROS(TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 14 DAY))
  AND event_timestamp <
      UNIX_MICROS(TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY))
  -- Cohort filter: users acquired through 'google' source.
  AND traffic_source.source = 'google'
  -- PLEASE REPLACE YOUR DESIRED DATE RANGE.
  AND _TABLE_SUFFIX BETWEEN '20180501' AND '20240131';
```