Halaman ini diterjemahkan oleh Cloud Translation API.

Kueri untuk ekspor data pengguna Google Analytics 4

Contoh kueri di halaman ini berlaku untuk ekspor data pengguna ke BigQuery untuk Google Analytics 4. Ekspor data pengguna ke BigQuery membuat dua tabel untuk setiap hari:

Tabel users_YYYYMMDD yang berisi satu baris untuk setiap ID pengguna yang berubah.
Tabel pseudonymous_users_YYYYMMDD yang berisi satu baris untuk setiap ID pseudonim yang berubah.

Lihat skema data pengguna BigQuery Export untuk mendapatkan detail selengkapnya.

Membuat kueri untuk rentang tanggal tertentu

Untuk membuat kueri yang mencakup rentang tanggal tertentu dari set data ekspor data pengguna ke BigQuery, gunakan kolom pseudo _TABLE_SUFFIX di klausa WHERE dalam kueri Anda.

Misalnya, kueri berikut menghitung jumlah pengguna unik yang diperbarui antara 1 Agustus 2023 dan 15 Agustus 2023 dengan engagement sepanjang waktu selama minimal lima menit.

users

-- Example: Query a specific date range for users meeting a lifetime engagement criterion.
--
-- Counts unique users that are in the BigQuery user-data exports for a specific date range and have
-- a lifetime engagement of 5 minutes or more.

SELECT
  COUNT(DISTINCT user_id) AS user_count
FROM
  -- Uses a table suffix wildcard to define the set of daily tables to query.
  `PROJECT_ID.analytics_PROPERTY_ID.users_202308*`
WHERE
  -- Filters to users updated between August 1 and August 15.
  _TABLE_SUFFIX BETWEEN '01' AND '15'
  -- Filters by users who have a lifetime engagement of 5 minutes or more.
  AND user_ltv.engagement_time_millis >= 5 * 60 * 1000;

pseudonymous_users

-- Example: Query a specific date range for users meeting a lifetime engagement criterion.
--
-- Counts unique pseudonymous users that are in the BigQuery user-data exports for a specific date
-- range and have a lifetime engagement of 5 minutes or more.

SELECT
  COUNT(DISTINCT pseudo_user_id) AS pseudo_user_count
FROM
  -- Uses a table suffix wildcard to define the set of daily tables to query.
  `PROJECT_ID.analytics_PROPERTY_ID.pseudonymous_users_202308*`
WHERE
  -- Filters to users updated between August 1 and August 15.
  _TABLE_SUFFIX BETWEEN '01' AND '15'
  -- Filters by users who have a lifetime engagement of 5 minutes or more.
  AND user_ltv.engagement_time_millis >= 5 * 60 * 1000;

Setiap contoh membatasi data dari 1 Agustus 2023 hingga 15 Agustus 2023 menggunakan dua fitur:

Karakter pengganti 202308* di klausa FROM.
Syarat _TABLE_SUFFIX di klausa WHERE yang memfilter tabel berdasarkan bagian karakter pengganti pada nama tabel. Untuk 202308*, bagian karakter penggantinya adalah hari.

Anda dapat menggunakan pendekatan yang mirip untuk membuat kueri yang mencakup data dari beberapa bulan. Misalnya, untuk membuat kueri yang mencakup data dari Januari hingga Oktober 2023, ubah kuerinya sehingga berisi:

Karakter pengganti 2023*.
Syarat _TABLE_SUFFIX berupa _TABLE_SUFFIX BETWEEN '0101' AND '1031'.

Anda juga dapat membuat kueri yang mencakup data dari beberapa tahun. Misalnya, untuk membuat kueri yang mencakup data dari Oktober 2022 hingga Februari 2023, ubah kuerinya sehingga berisi:

Karakter pengganti 202*.
Kondisi _TABLE_SUFFIX berupa _TABLE_SUFFIX BETWEEN '21001' AND '30331'.

ID pengguna untuk perubahan properti pengguna baru-baru ini

Kueri berikut menunjukkan cara mengambil user_id dan pseudo_user_id semua pengguna yang baru-baru ini mengubah suatu properti pengguna.

users

-- Example: Get the list of user_ids with recent changes to a specific user property.
DECLARE
  UPDATE_LOWER_BOUND_MICROS INT64;

-- Replace timezone. List at https://en.wikipedia.org/wiki/List_of_tz_database_time_zones.
DECLARE
  REPORTING_TIMEZONE STRING DEFAULT 'America/Los_Angeles';

-- Sets the variable for the earliest update time to include. This comes after setting
-- the REPORTING_TIMEZONE so this expression can use that variable.
SET UPDATE_LOWER_BOUND_MICROS = UNIX_MICROS(
    TIMESTAMP_SUB(
      TIMESTAMP_TRUNC(CURRENT_TIMESTAMP(), DAY, REPORTING_TIMEZONE),
      INTERVAL 14 DAY));

-- Selects users with changes to a specific user property since the lower bound.
SELECT
  users.user_id,
  FORMAT_TIMESTAMP('%F %T',
    TIMESTAMP_MICROS(
      MAX(properties.value.set_timestamp_micros)),
      REPORTING_TIMEZONE) AS max_set_timestamp
FROM
  -- Uses a table prefix to scan all data for 2023. Update the prefix as needed to query a different
  -- date range.
  `PROJECT_ID.analytics_PROPERTY_ID.users_2023*` AS users,
  users.user_properties properties
WHERE
  properties.value.user_property_name = 'job_function'
  AND properties.value.set_timestamp_micros >= UPDATE_LOWER_BOUND_MICROS
GROUP BY
  1;

pseudonymous_users

-- Example: Get the list of pseudo_user_ids with recent changes to a specific user property.
DECLARE
  UPDATE_LOWER_BOUND_MICROS INT64;

-- Replace timezone. List at https://en.wikipedia.org/wiki/List_of_tz_database_time_zones.
DECLARE
  REPORTING_TIMEZONE STRING DEFAULT 'America/Los_Angeles';

-- Sets the variable for the earliest update time to include. This comes after setting
-- the REPORTING_TIMEZONE so this expression can use that variable.
SET UPDATE_LOWER_BOUND_MICROS = UNIX_MICROS(
    TIMESTAMP_SUB(
      TIMESTAMP_TRUNC(CURRENT_TIMESTAMP(), DAY, REPORTING_TIMEZONE),
      INTERVAL 14 DAY));

-- Selects users with changes to a specific user property since the lower bound.
SELECT
  users.pseudo_user_id,
  FORMAT_TIMESTAMP('%F %T',
    TIMESTAMP_MICROS(
      MAX(properties.value.set_timestamp_micros)),
      REPORTING_TIMEZONE) AS max_set_timestamp
FROM
  -- Uses a table prefix to scan all data for 2023. Update the prefix as needed to query a different
  -- date range.
  `PROJECT_ID.analytics_PROPERTY_ID.pseudonymous_users_2023*` AS users,
  users.user_properties properties
WHERE
  properties.value.user_property_name = 'job_function'
  AND properties.value.set_timestamp_micros >= UPDATE_LOWER_BOUND_MICROS
GROUP BY
  1;

Ringkasan pembaruan

Gunakan kueri ini untuk memahami alasan ekspor data pengguna menyertakan atau mengecualikan berbagai kategori pengguna.

users

-- Summarizes data by change type.

-- Defines the export date to query. This must match the table suffix in the FROM
-- clause below.
DECLARE EXPORT_DATE DATE DEFAULT DATE(2023,6,16);

-- Creates a temporary function that will return true if a timestamp (in micros) is for the same
-- date as the specified day value.
CREATE TEMP FUNCTION WithinDay(ts_micros INT64, day_value DATE)
AS (
  (ts_micros IS NOT NULL) AND
  -- Change the timezone to your property's reporting time zone.
  -- List at https://en.wikipedia.org/wiki/List_of_tz_database_time_zones.
  (DATE(TIMESTAMP_MICROS(ts_micros), 'America/Los_Angeles') = day_value)
);

-- Creates a temporary function that will return true if a date string in 'YYYYMMDD' format is
-- for the same date as the specified day value.
CREATE TEMP FUNCTION SameDate(date_string STRING, day_value DATE)
AS (
  (date_string IS NOT NULL) AND
  (PARSE_DATE('%Y%m%d', date_string) = day_value)
);

WITH change_types AS (
SELECT user_id,
  WithinDay(user_info.last_active_timestamp_micros, EXPORT_DATE) AS user_activity,
  WithinDay(user_info.user_first_touch_timestamp_micros, EXPORT_DATE) AS first_touch,
  SameDate(user_info.first_purchase_date, EXPORT_DATE) as first_purchase,
  (EXISTS (SELECT 1 FROM UNNEST(audiences) AS aud
           WHERE WithinDay(aud.membership_start_timestamp_micros, EXPORT_DATE))) AS audience_add,
  (EXISTS (SELECT 1 FROM UNNEST(audiences) AS aud
           WHERE WithinDay(aud.membership_expiry_timestamp_micros, EXPORT_DATE))) AS audience_remove,
  (EXISTS (SELECT 1 FROM UNNEST(user_properties) AS prop
           WHERE WithinDay(prop.value.set_timestamp_micros, EXPORT_DATE))) AS user_property_change
FROM
  -- The table suffix must match the date used to define EXPORT_DATE above.
  `project_id.analytics_property_id.users_20230616`
)
SELECT
  user_activity,
  first_touch,
  first_purchase,
  audience_add,
  audience_remove,
  user_property_change,
  -- This field will be true if there are no changes for the other change types.
  NOT (user_activity OR first_touch OR audience_add OR audience_remove OR user_property_change) AS other_change,
  COUNT(DISTINCT user_id) AS user_id_count
FROM change_types
GROUP BY 1,2,3,4,5,6,7;

pseudonymous_users

-- Summarizes data by change type.

-- Defines the export date to query. This must match the table suffix in the FROM
-- clause below.
DECLARE EXPORT_DATE DATE DEFAULT DATE(2023,6,16);

-- Creates a temporary function that will return true if a timestamp (in micros) is for the same
-- date as the specified day value.
CREATE TEMP FUNCTION WithinDay(ts_micros INT64, day_value DATE)
AS (
  (ts_micros IS NOT NULL) AND
  -- Change the timezone to your property's reporting time zone.
  -- List at https://en.wikipedia.org/wiki/List_of_tz_database_time_zones.
  (DATE(TIMESTAMP_MICROS(ts_micros), 'America/Los_Angeles') = day_value)
);

-- Creates a temporary function that will return true if a date string in 'YYYYMMDD' format is
-- for the same date as the specified day value.
CREATE TEMP FUNCTION SameDate(date_string STRING, day_value DATE)
AS (
  (date_string IS NOT NULL) AND
  (PARSE_DATE('%Y%m%d', date_string) = day_value)
);

WITH change_types AS (
SELECT pseudo_user_id,
  WithinDay(user_info.last_active_timestamp_micros, EXPORT_DATE) AS user_activity,
  WithinDay(user_info.user_first_touch_timestamp_micros, EXPORT_DATE) AS first_touch,
  SameDate(user_info.first_purchase_date, EXPORT_DATE) as first_purchase,
  (EXISTS (SELECT 1 FROM UNNEST(audiences) AS aud
           WHERE WithinDay(aud.membership_start_timestamp_micros, EXPORT_DATE))) AS audience_add,
  (EXISTS (SELECT 1 FROM UNNEST(audiences) AS aud
           WHERE WithinDay(aud.membership_expiry_timestamp_micros, EXPORT_DATE))) AS audience_remove,
  (EXISTS (SELECT 1 FROM UNNEST(user_properties) AS prop
           WHERE WithinDay(prop.value.set_timestamp_micros, EXPORT_DATE))) AS user_property_change
FROM
  -- The table suffix must match the date used to define EXPORT_DATE above.
  `PROJECT_ID.analytics_PROPERTY_ID.pseudonymous_users_20230616`
)
SELECT
  user_activity,
  first_touch,
  first_purchase,
  audience_add,
  audience_remove,
  user_property_change,
  -- This field will be true if there are no changes for the other change types.
  NOT (user_activity OR first_touch OR audience_add OR audience_remove OR user_property_change) AS other_change,
  COUNT(DISTINCT pseudo_user_id) pseudo_user_id_count
FROM change_types
GROUP BY 1,2,3,4,5,6,7;