高级查询

使用集合让一切井井有条 根据您的偏好保存内容并对其进行分类。

本页面中的高级查询适用于 Google Analytics(分析)4 的 BigQuery 事件导出数据。如果您在寻找适用于 Universal Analytics 的同类资源,不妨参阅适用于 Universal Analytics 的 BigQuery 实战宝典。请先尝试基本查询,然后再试用高级查询。

已购买特定产品的客户购买的产品

以下查询会显示已购买特定产品的客户还购买了哪些其他产品。本示例并不假设客户是在同一订单中购买的这些产品。

优化版查询示例依靠 BigQuery 脚本功能来定义变量,用以声明要过滤的商品。虽然这并不能提高性能,但与使用 WITH 子句创建单值表相比,这种定义变量的方法可读性更高。简化版查询使用的就是前一种方法,即使用 WITH 子句。

简化版查询会创建一个单独的“产品 A 买家”名单,并与该数据联接。优化版查询则使用 ARRAY_AGG 函数创建一个列表,其中包含某位用户在各个订单中购买的所有商品。然后,使用外部 WHERE 子句,针对 target_item 过滤所有用户的购买列表,最后仅显示相关商品。

简化版

-- 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;

优化版

-- 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;

用户每次购买会话的平均支出金额

以下查询会显示每位用户每次会话的平均支出金额。此查询仅考虑用户完成了购买的会话。

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

SELECT
  user_pseudo_id,
  COUNT(
    DISTINCT(SELECT EP.value.int_value FROM UNNEST(event_params) AS EP WHERE key = 'ga_session_id'))
    AS session_count,
  AVG(
    (
      SELECT COALESCE(EP.value.int_value, EP.value.float_value, EP.value.double_value)
      FROM UNNEST(event_params) AS EP
      WHERE key = 'value'
    )) AS avg_spend_per_session_by_user,
FROM
  -- Replace table name.
  `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
WHERE
  event_name = 'purchase'
  -- Replace date range.
  AND _TABLE_SUFFIX BETWEEN '20201101' AND '20210131'
GROUP BY
  1;

用户的最新会话 ID 和会话编号

以下查询提供用户列表在过去 4 天中最新 ga_session_id 和 ga_session_number 的列表。您可以提供 user_pseudo_id 列表或 user_id 列表。

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);