Consultas avanzadas

Las consultas avanzadas de esta página se aplican a los datos de exportación de eventos de BigQuery para Google Analytics Consulta la guía de soluciones de BigQuery para Universal Analytics si tienes lo siguiente: que buscan el mismo recurso para Universal Analytics. Prueba las consultas básicas antes de probar las más avanzadas.

Productos adquiridos por clientes que compraron un producto determinado

La siguiente consulta muestra qué otros productos compraron los clientes que adquirieron un producto específico. En este ejemplo, no se da por sentado que los productos se compraron en el mismo pedido.

El ejemplo optimizado se basa en las funciones de secuencia de comandos de BigQuery para definir una variable que declara qué elementos filtrar. Si bien esto no mejora el rendimiento, este es un enfoque más legible para definir variables que crear un una sola tabla de valores con una cláusula WITH. La consulta simplificada usa la última con la cláusula WITH.

La consulta simplificada crea una lista separada de "compradores del producto A" y hace un unir con esos datos. En cambio, la consulta optimizada crea una lista de todos los elementos que el usuario compró en todos los pedidos con la función ARRAY_AGG. Luego, con externa WHERE, se filtran las listas de compras de todos los usuarios según target_item y solo se muestran elementos relevantes.

Simplificado

-- Example: Products purchased by customers who purchased a specific product.
--
-- `Params` is used to hold the value of the selected product and is referenced
-- throughout the query.

WITH
  Params AS (
    -- Replace with selected item_name or item_id.
    SELECT 'Google Navy Speckled Tee' AS selected_product
  ),
  PurchaseEvents AS (
    SELECT
      user_pseudo_id,
      items
    FROM
      -- Replace table name.
      `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
    WHERE
      -- Replace date range.
      _TABLE_SUFFIX BETWEEN '20201101' AND '20210131'
      AND event_name = 'purchase'
  ),
  ProductABuyers AS (
    SELECT DISTINCT
      user_pseudo_id
    FROM
      Params,
      PurchaseEvents,
      UNNEST(items) AS items
    WHERE
      -- item.item_id can be used instead of items.item_name.
      items.item_name = selected_product
  )
SELECT
  items.item_name AS item_name,
  SUM(items.quantity) AS item_quantity
FROM
  Params,
  PurchaseEvents,
  UNNEST(items) AS items
WHERE
  user_pseudo_id IN (SELECT user_pseudo_id FROM ProductABuyers)
  -- item.item_id can be used instead of items.item_name
  AND items.item_name != selected_product
GROUP BY 1
ORDER BY item_quantity DESC;

Optimizado

-- Optimized Example: Products purchased by customers who purchased a specific product.

-- Replace item name
DECLARE target_item STRING DEFAULT 'Google Navy Speckled Tee';

SELECT
  IL.item_name AS item_name,
  SUM(IL.quantity) AS quantity
FROM
  (
    SELECT
      user_pseudo_id,
      ARRAY_AGG(STRUCT(item_name, quantity)) AS item_list
    FROM
      -- Replace table
      `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`, UNNEST(items)
    WHERE
      -- Replace date range
      _TABLE_SUFFIX BETWEEN '20201201' AND '20201210'
      AND event_name = 'purchase'
    GROUP BY
      1
  ),
  UNNEST(item_list) AS IL
WHERE
  target_item IN (SELECT item_name FROM UNNEST(item_list))
  -- Remove the following line if you want the target_item to appear in the results
  AND target_item != IL.item_name
GROUP BY
  item_name
ORDER BY
  quantity DESC;

Importe promedio de dinero gastado por sesión de compra por usuario

La siguiente consulta muestra la cantidad promedio de dinero gastado por sesión por cada usuario. Esto solo considera las sesiones en las que el usuario realizó una compra.

-- Example: Average amount of money spent per purchase session by user.

WITH
  events AS (
    SELECT
      session.value.int_value AS session_id,
      COALESCE(spend.value.int_value, spend.value.float_value, spend.value.double_value, 0.0)
        AS spend_value,
      event.*

    -- Replace table name
    FROM `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*` AS event
    LEFT JOIN UNNEST(event.event_params) AS session
      ON session.key = 'ga_session_id'
    LEFT JOIN UNNEST(event.event_params) AS spend
      ON spend.key = 'value'

    -- Replace date range
    WHERE _TABLE_SUFFIX BETWEEN '20201101' AND '20210131'
  )
SELECT
  user_pseudo_id,
  COUNT(DISTINCT session_id) AS session_count,
  SUM(spend_value) / COUNT(DISTINCT session_id) AS avg_spend_per_session_by_user
FROM events
WHERE event_name = 'purchase' and session_id IS NOT NULL
GROUP BY user_pseudo_id

ID y número de sesión más recientes para los usuarios

La siguiente consulta proporciona la lista de los últimos ga_session_id y ga_session_number de los últimos 4 días para una lista de usuarios. Puedes proporcionar Lista user_pseudo_id o user_id.

user_pseudo_id

-- Get the latest ga_session_id and ga_session_number for specific users during last 4 days.

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

-- Replace list of user_pseudo_id's with ones you want to query.
DECLARE USER_PSEUDO_ID_LIST ARRAY<STRING> DEFAULT
  [
    '1005355938.1632145814', '979622592.1632496588', '1101478530.1632831095'];

CREATE TEMP FUNCTION GetParamValue(params ANY TYPE, target_key STRING)
AS (
  (SELECT `value` FROM UNNEST(params) WHERE key = target_key LIMIT 1)
);

CREATE TEMP FUNCTION GetDateSuffix(date_shift INT64, timezone STRING)
AS (
  (SELECT FORMAT_DATE('%Y%m%d', DATE_ADD(CURRENT_DATE(timezone), INTERVAL date_shift DAY)))
);

SELECT DISTINCT
  user_pseudo_id,
  FIRST_VALUE(GetParamValue(event_params, 'ga_session_id').int_value)
    OVER (UserWindow) AS ga_session_id,
  FIRST_VALUE(GetParamValue(event_params, 'ga_session_number').int_value)
    OVER (UserWindow) AS ga_session_number
FROM
  -- Replace table name.
  `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
WHERE
  user_pseudo_id IN UNNEST(USER_PSEUDO_ID_LIST)
  AND RIGHT(_TABLE_SUFFIX, 8)
    BETWEEN GetDateSuffix(-3, REPORTING_TIMEZONE)
    AND GetDateSuffix(0, REPORTING_TIMEZONE)
WINDOW UserWindow AS (PARTITION BY user_pseudo_id ORDER BY event_timestamp DESC);

user_id

-- Get the latest ga_session_id and ga_session_number for specific users during last 4 days.

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

-- Replace list of user_id's with ones you want to query.
DECLARE USER_ID_LIST ARRAY<STRING> DEFAULT ['<user_id_1>', '<user_id_2>', '<user_id_n>'];

CREATE TEMP FUNCTION GetParamValue(params ANY TYPE, target_key STRING)
AS (
  (SELECT `value` FROM UNNEST(params) WHERE key = target_key LIMIT 1)
);

CREATE TEMP FUNCTION GetDateSuffix(date_shift INT64, timezone STRING)
AS (
  (SELECT FORMAT_DATE('%Y%m%d', DATE_ADD(CURRENT_DATE(timezone), INTERVAL date_shift DAY)))
);

SELECT DISTINCT
  user_pseudo_id,
  FIRST_VALUE(GetParamValue(event_params, 'ga_session_id').int_value)
    OVER (UserWindow) AS ga_session_id,
  FIRST_VALUE(GetParamValue(event_params, 'ga_session_number').int_value)
    OVER (UserWindow) AS ga_session_number
FROM
  -- Replace table name.
  `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
WHERE
  user_id IN UNNEST(USER_ID_LIST)
  AND RIGHT(_TABLE_SUFFIX, 8)
    BETWEEN GetDateSuffix(-3, REPORTING_TIMEZONE)
    AND GetDateSuffix(0, REPORTING_TIMEZONE)
WINDOW UserWindow AS (PARTITION BY user_pseudo_id ORDER BY event_timestamp DESC);