جرِّب خادم MCP لخدمة "إحصاءات Google". يمكنك تثبيت التطبيق من
GitHub والاطّلاع على
الإعلان لمعرفة المزيد من التفاصيل.
طلبات البحث المتقدمة
تنظيم صفحاتك في مجموعات
يمكنك حفظ المحتوى وتصنيفه حسب إعداداتك المفضّلة.
تنطبق الطلبات المتقدّمة في هذه الصفحة على بيانات تصدير أحداث BigQuery للنطاق
إحصاءات Google اطّلِع على دليل BigQuery حول Universal Analytics إذا كنت
البحث عن المورد نفسه في Universal Analytics جرِّب طلبات البحث الأساسية
أولاً قبل تجربة الخطوات المتقدمة.
المنتجات التي اشتراها العملاء الذين اشتروا منتجًا معيّنًا
يعرض الاستعلام التالي المنتجات الأخرى التي اشتراها العملاء الذين
اشترى منتجًا معينًا. لا يفترض هذا المثال أن المنتجات
تم شراؤها بنفس الطلب.
يعتمد المثال المحسّن على ميزات البرمجة النصية في BigQuery لتحديد متغير
التي تشير إلى العناصر التي يجب الفلترة وفقًا لها. وعلى الرغم من أنّ هذا الإجراء لا يحسّن الأداء،
فهو أسلوب أكثر قابلية للقراءة لتحديد المتغيرات مقارنة بإنشاء
جدول القيمة المفردة باستخدام عبارة WITH
. يستخدم الاستعلام المبسط الأخير
باستخدام عبارة WITH
.
ينشئ الاستعلام المبسط قائمة منفصلة من "مشتري المنتج أ" ويقوم
ربط تلك البيانات. وبدلاً من ذلك، ينشئ الاستعلام المحسَّن قائمة بكل العناصر
أجرى المستخدم عملية شراء في عدة طلبات باستخدام الدالة ARRAY_AGG
. ثم استخدام
خارجية WHERE
، تتم فلترة قوائم الشراء لجميع المستخدمين بحثًا عن
يتم عرض target_item
والعناصر ذات الصلة فقط.
مبسّطة
-- 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;
محسَّن
-- 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;
متوسط مبلغ الإنفاق لكل جلسة شراء حسب المستخدم
يعرض طلب البحث التالي متوسط مبلغ الإنفاق لكل جلسة حسب كل جلسة،
المستخدم. ولا يأخذ ذلك في الاعتبار سوى الجلسات التي أجرى فيها المستخدم عملية شراء.
-- 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
آخر معرّف جلسة ورقم جلسة للمستخدمين
يوفر الاستعلام التالي قائمة بأحدث جلسات ga_session_id
ga_session_number من آخر 4 أيام لقائمة من المستخدمين. يمكنك تقديم إما
قائمة "user_pseudo_id
" أو قائمة "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);
إنّ محتوى هذه الصفحة مرخّص بموجب ترخيص Creative Commons Attribution 4.0 ما لم يُنصّ على خلاف ذلك، ونماذج الرموز مرخّصة بموجب ترخيص Apache 2.0. للاطّلاع على التفاصيل، يُرجى مراجعة سياسات موقع Google Developers. إنّ Java هي علامة تجارية مسجَّلة لشركة Oracle و/أو شركائها التابعين.
تاريخ التعديل الأخير: 2024-09-12 (حسب التوقيت العالمي المتفَّق عليه)
[null,null,["تاريخ التعديل الأخير: 2024-09-12 (حسب التوقيت العالمي المتفَّق عليه)"],[[["\u003cp\u003eThis page provides advanced BigQuery queries for analyzing Google Analytics 4 event export data, going beyond basic queries.\u003c/p\u003e\n"],["\u003cp\u003eIt includes queries to identify products frequently purchased together, calculate average spending per purchase session, and retrieve the latest session information for specific users.\u003c/p\u003e\n"],["\u003cp\u003eThe queries are demonstrated with examples and explanations, including simplified and optimized versions where applicable.\u003c/p\u003e\n"],["\u003cp\u003eBefore using these advanced queries, it's recommended to familiarize yourself with the basic BigQuery queries for Google Analytics 4.\u003c/p\u003e\n"],["\u003cp\u003eUsers of Universal Analytics can find similar resources in the BigQuery cookbook for Universal Analytics linked on the page.\u003c/p\u003e\n"]]],["This document provides advanced BigQuery queries for Google Analytics event data. It details how to identify other products purchased by customers who bought a specific item, offering both simplified and optimized query examples that filter purchase lists. Another query calculates the average amount spent per purchase session per user. Lastly, it outlines how to retrieve the latest session ID and number for users, with examples for both `user_pseudo_id` and `user_id` lists.\n"],null,["# Advanced queries\n\nThe advanced queries in this page apply to the BigQuery event export data for\nGoogle Analytics. See [BigQuery cookbook for Universal Analytics](https://support.google.com/analytics/answer/4419694) if you are\nlooking for the same resource for Universal Analytics. Try the [basic queries](/analytics/bigquery/basic-queries)\nfirst before trying out the advanced ones.\n\n### Products purchased by customers who purchased a certain product\n\nThe following query shows what other products were purchased by customers who\npurchased a specific product. This example does not assume that the products\nwere purchased in the same order.\n\nThe optimized example relies on BigQuery scripting features to define a variable\nthat declares which items to filter on. While this does not improve performance,\nthis is a more readable approach for defining variables compared creating a\nsingle value table using a `WITH` clause. The simplified query uses the latter\napproach using the `WITH` clause.\n\nThe simplified query creats a separate list of \"Product A buyers\" and does a\njoin with that data. The optimized query, instead, creates a list of all items a\nuser has purchased across orders using the `ARRAY_AGG` function. Then using the\nouter `WHERE` clause, purchase lists across all users are filtered for the\n`target_item` and only relevant items are shown. \n\n### Simplified\n\n -- Example: Products purchased by customers who purchased a specific product.\n --\n -- `Params` is used to hold the value of the selected product and is referenced\n -- throughout the query.\n\n WITH\n Params AS (\n -- Replace with selected item_name or item_id.\n SELECT 'Google Navy Speckled Tee' AS selected_product\n ),\n PurchaseEvents AS (\n SELECT\n user_pseudo_id,\n items\n FROM\n -- Replace table name.\n `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`\n WHERE\n -- Replace date range.\n _TABLE_SUFFIX BETWEEN '20201101' AND '20210131'\n AND event_name = 'purchase'\n ),\n ProductABuyers AS (\n SELECT DISTINCT\n user_pseudo_id\n FROM\n Params,\n PurchaseEvents,\n UNNEST(items) AS items\n WHERE\n -- item.item_id can be used instead of items.item_name.\n items.item_name = selected_product\n )\n SELECT\n items.item_name AS item_name,\n SUM(items.quantity) AS item_quantity\n FROM\n Params,\n PurchaseEvents,\n UNNEST(items) AS items\n WHERE\n user_pseudo_id IN (SELECT user_pseudo_id FROM ProductABuyers)\n -- item.item_id can be used instead of items.item_name\n AND items.item_name != selected_product\n GROUP BY 1\n ORDER BY item_quantity DESC;\n\n### Optimized\n\n -- Optimized Example: Products purchased by customers who purchased a specific product.\n\n -- Replace item name\n DECLARE target_item STRING DEFAULT 'Google Navy Speckled Tee';\n\n SELECT\n IL.item_name AS item_name,\n SUM(IL.quantity) AS quantity\n FROM\n (\n SELECT\n user_pseudo_id,\n ARRAY_AGG(STRUCT(item_name, quantity)) AS item_list\n FROM\n -- Replace table\n `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`, UNNEST(items)\n WHERE\n -- Replace date range\n _TABLE_SUFFIX BETWEEN '20201201' AND '20201210'\n AND event_name = 'purchase'\n GROUP BY\n 1\n ),\n UNNEST(item_list) AS IL\n WHERE\n target_item IN (SELECT item_name FROM UNNEST(item_list))\n -- Remove the following line if you want the target_item to appear in the results\n AND target_item != IL.item_name\n GROUP BY\n item_name\n ORDER BY\n quantity DESC;\n\n### Average amount of money spent per purchase session by user\n\nThe following query shows the average amount of money spent per session by each\nuser. This takes into account only the sessions where the user made a purchase. \n\n -- Example: Average amount of money spent per purchase session by user.\n\n WITH\n events AS (\n SELECT\n session.value.int_value AS session_id,\n COALESCE(spend.value.int_value, spend.value.float_value, spend.value.double_value, 0.0)\n AS spend_value,\n event.*\n\n -- Replace table name\n FROM `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*` AS event\n LEFT JOIN UNNEST(event.event_params) AS session\n ON session.key = 'ga_session_id'\n LEFT JOIN UNNEST(event.event_params) AS spend\n ON spend.key = 'value'\n\n -- Replace date range\n WHERE _TABLE_SUFFIX BETWEEN '20201101' AND '20210131'\n )\n SELECT\n user_pseudo_id,\n COUNT(DISTINCT session_id) AS session_count,\n SUM(spend_value) / COUNT(DISTINCT session_id) AS avg_spend_per_session_by_user\n FROM events\n WHERE event_name = 'purchase' and session_id IS NOT NULL\n GROUP BY user_pseudo_id\n\n### Latest Session Id and Session Number for users\n\nThe following query provides the list of the latest ga_session_id and\nga_session_number from last 4 days for a list of users. You can provide either a\n`user_pseudo_id` list or a `user_id` list. \n\n### user_pseudo_id\n\n -- Get the latest ga_session_id and ga_session_number for specific users during last 4 days.\n\n -- Replace timezone. List at https://en.wikipedia.org/wiki/List_of_tz_database_time_zones.\n DECLARE REPORTING_TIMEZONE STRING DEFAULT 'America/Los_Angeles';\n\n -- Replace list of user_pseudo_id's with ones you want to query.\n DECLARE USER_PSEUDO_ID_LIST ARRAY\u003cSTRING\u003e DEFAULT\n [\n '1005355938.1632145814', '979622592.1632496588', '1101478530.1632831095'];\n\n CREATE TEMP FUNCTION GetParamValue(params ANY TYPE, target_key STRING)\n AS (\n (SELECT `value` FROM UNNEST(params) WHERE key = target_key LIMIT 1)\n );\n\n CREATE TEMP FUNCTION GetDateSuffix(date_shift INT64, timezone STRING)\n AS (\n (SELECT FORMAT_DATE('%Y%m%d', DATE_ADD(CURRENT_DATE(timezone), INTERVAL date_shift DAY)))\n );\n\n SELECT DISTINCT\n user_pseudo_id,\n FIRST_VALUE(GetParamValue(event_params, 'ga_session_id').int_value)\n OVER (UserWindow) AS ga_session_id,\n FIRST_VALUE(GetParamValue(event_params, 'ga_session_number').int_value)\n OVER (UserWindow) AS ga_session_number\n FROM\n -- Replace table name.\n `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`\n WHERE\n user_pseudo_id IN UNNEST(USER_PSEUDO_ID_LIST)\n AND RIGHT(_TABLE_SUFFIX, 8)\n BETWEEN GetDateSuffix(-3, REPORTING_TIMEZONE)\n AND GetDateSuffix(0, REPORTING_TIMEZONE)\n WINDOW UserWindow AS (PARTITION BY user_pseudo_id ORDER BY event_timestamp DESC);\n\n### user_id\n\n -- Get the latest ga_session_id and ga_session_number for specific users during last 4 days.\n\n -- Replace timezone. List at https://en.wikipedia.org/wiki/List_of_tz_database_time_zones.\n DECLARE REPORTING_TIMEZONE STRING DEFAULT 'America/Los_Angeles';\n\n -- Replace list of user_id's with ones you want to query.\n DECLARE USER_ID_LIST ARRAY\u003cSTRING\u003e DEFAULT ['\u003cuser_id_1\u003e', '\u003cuser_id_2\u003e', '\u003cuser_id_n\u003e'];\n\n CREATE TEMP FUNCTION GetParamValue(params ANY TYPE, target_key STRING)\n AS (\n (SELECT `value` FROM UNNEST(params) WHERE key = target_key LIMIT 1)\n );\n\n CREATE TEMP FUNCTION GetDateSuffix(date_shift INT64, timezone STRING)\n AS (\n (SELECT FORMAT_DATE('%Y%m%d', DATE_ADD(CURRENT_DATE(timezone), INTERVAL date_shift DAY)))\n );\n\n SELECT DISTINCT\n user_pseudo_id,\n FIRST_VALUE(GetParamValue(event_params, 'ga_session_id').int_value)\n OVER (UserWindow) AS ga_session_id,\n FIRST_VALUE(GetParamValue(event_params, 'ga_session_number').int_value)\n OVER (UserWindow) AS ga_session_number\n FROM\n -- Replace table name.\n `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`\n WHERE\n user_id IN UNNEST(USER_ID_LIST)\n AND RIGHT(_TABLE_SUFFIX, 8)\n BETWEEN GetDateSuffix(-3, REPORTING_TIMEZONE)\n AND GetDateSuffix(0, REPORTING_TIMEZONE)\n WINDOW UserWindow AS (PARTITION BY user_pseudo_id ORDER BY event_timestamp DESC);"]]