Query per l'esportazione dei dati utente di Google Analytics 4

Le query di esempio in questa pagina si applicano all'esportazione dei dati utente di BigQuery per Google Analytics 4. L'esportazione dei dati utente di BigQuery crea due tabelle per ogni giorno:

1. Una tabella users_YYYYMMDD contenente una riga per ogni ID utente modificato.
2. Una tabella pseudonymous_users_YYYYMMDD, che contiene una riga per ogni identificatore pseudonimo modificato.

Per ulteriori dettagli, consulta lo schema dei dati utente di BigQuery Export.

Suggerimento: per utilizzare al meglio le query di esempio in questa guida, imposta le variabili PROJECT_ID e PROPERTY_ID con i valori del progetto e della proprietà. Recupera il PROJECT_ID dalla dashboard del progetto e l'PROPERTY_ID dal selettore delle proprietà di Google Analytics.

Query su un intervallo di date specifico

Per eseguire query su un intervallo di date specifico da un set di dati per l'esportazione dei dati utente di BigQuery, utilizza la pseudo colonna _TABLE_SUFFIX nella clausola WHERE della query.

Ad esempio, la seguente query conteggia il numero di utenti unici aggiornati tra il 1° agosto 2023 e il 15 agosto 2023 con un coinvolgimento complessivo di almeno cinque minuti.

utenti

-- 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;

Ogni esempio limita i dati dal 1° agosto al 15 agosto 2023 utilizzando due funzionalità:

1. Il carattere jolly 202308* nella clausola FROM.
2. Una condizione _TABLE_SUFFIX nella clausola WHERE che filtra le tabelle in base alla parte con caratteri jolly del nome della tabella. Per il carattere jolly 202308*, la parte del carattere jolly è il giorno del mese.

Puoi utilizzare un approccio simile per eseguire query su più mesi di dati. Ad esempio, per eseguire query da gennaio a ottobre 2023, modifica la query in modo da avere:

1. Il carattere jolly 2023*.
2. Una condizione _TABLE_SUFFIX di _TABLE_SUFFIX BETWEEN '0101' AND '1031'.

Puoi anche eseguire query su più anni di dati. Ad esempio, per eseguire query da ottobre 2022 a febbraio 2023, modifica la query in modo che abbia:

1. Il carattere jolly 202*.
2. Una condizione _TABLE_SUFFIX di _TABLE_SUFFIX BETWEEN '21001' AND '30331'.

ID utente per le modifiche recenti alle proprietà utente

La seguente query mostra come recuperare i user_id e i pseudo_user_id di tutti gli utenti che di recente hanno modificato una proprietà utente specifica.

utenti

-- 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;

Riepilogo degli aggiornamenti

Utilizza questa query per capire perché l'esportazione dei dati utente includeva o escludeva diverse categorie di utenti.

utenti

-- 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;