本頁的進階查詢適用於 Google Analytics (分析) 4 的 BigQuery 事件匯出資料。如要針對通用 Analytics (分析) 尋找相同的資源,請參閱通用 Analytics (分析) 的 BigQuery 教戰手冊。請先試用基本查詢,再試用進階查詢。
購買特定產品的客戶購買的產品
下列查詢顯示購買特定產品的客戶購買了哪些其他產品。本範例不會假設這些產品是以相同順序購買。
經過最佳化的範例會使用 BigQuery 指令碼功能來定義變數,以宣告要篩選的項目。雖然這樣做不會改善效能,但相較於使用 WITH
子句建立單一值資料表,這個定義變數的讀取方式比較容易理解。簡化的查詢會使用 WITH
子句採取後者的方法。
簡化的查詢會產生單獨的「產品 A 買家」清單,並與該項資料進行彙整。最佳化查詢會使用 ARRAY_AGG
函式建立一份清單,列出使用者在所有訂單中購買的所有商品。然後使用外部 WHERE
子句,系統會根據 target_item
篩選所有使用者的購買清單,只顯示相關的商品。
單省
-- 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;
最佳化
-- 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;
使用者單次購買工作階段的平均消費金額
以下查詢顯示每位使用者的單次工作階段平均花費金額。並將使用者完成購買的工作階段納入考量。
-- 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
使用者的最新工作階段 ID 和工作階段號碼
以下查詢提供使用者清單最近 4 天內最新 ga_session_id 和 ga_session_number 的清單。您可以提供 user_pseudo_id
清單或 user_id
清單。
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);