本頁中的查詢範例適用於 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。
這裡列出的所有查詢使用範例資料集,並應產生有效的結果。如要使用您自己的 Google Analytics (分析) 資源 BigQuery 事件匯出資料,請在每項查詢中尋找註解 -- Replace table
,並替換範例資料集 ID。如要查看資料集 ID,請前往 BigQuery UI 中的 BigQuery 匯出專案。舉例來說,如果您的 BigQuery 匯出資料集 ID 為 my-
first-gcp-project:analytics_28239234
,則替換:
-- Replace table
`bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
取代為:
-- Replace table
`my-first-gcp-project.analytics_28239234.events_*`
查詢特定日期範圍
如要從 BigQuery 事件匯出資料集中查詢特定日期範圍,請在查詢的 WHERE 子句中使用 _TABLE_SUFFIX 虛擬資料欄。詳情請參閱使用 _TABLE_SUFFIX 篩選所選資料表。
舉例來說,以下查詢會依日期和事件名稱計算特定日期和所選事件的不重複事件:
-- Example: Query a specific date range for selected events.
--
-- Counts unique events by date and by event name for a specifc period of days and
-- selected events(page_view, session_start, and purchase).
SELECT
event_date,
event_name,
COUNT(*) AS event_count
FROM
-- Replace table name.
`bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
WHERE
event_name IN ('page_view', 'session_start', 'purchase')
-- Replace date range.
AND _TABLE_SUFFIX BETWEEN '20201201' AND '20201202'
GROUP BY 1, 2;
使用者人數和新使用者
- 如要取得使用者總數,請計算不同
user_id
的數量。不過,如果您的 Google Analytics (分析) 用戶端不會在每次命中時傳回user_id
,或是您不確定,請計算不同user_pseudo_id
的數量。 - 針對新使用者,您可以採用上述的計數方法,但只要輸入下列
event_name
值:
-- Example: Get 'Total User' count and 'New User' count.
WITH
UserInfo AS (
SELECT
user_pseudo_id,
MAX(IF(event_name IN ('first_visit', 'first_open'), 1, 0)) AS is_new_user
-- Replace table name.
FROM `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
-- Replace date range.
WHERE _TABLE_SUFFIX BETWEEN '20201101' AND '20201130'
GROUP BY 1
)
SELECT
COUNT(*) AS user_count,
SUM(is_new_user) AS new_user_count
FROM UserInfo;
每位購買者的平均交易次數
以下查詢顯示了每位購買者的平均交易次數。
-- Example: Average number of transactions per purchaser.
SELECT
COUNT(*) / COUNT(DISTINCT user_pseudo_id) AS avg_transaction_per_purchaser
FROM
-- Replace table name.
`bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
WHERE
event_name IN ('in_app_purchase', 'purchase')
-- Replace date range.
AND _TABLE_SUFFIX BETWEEN '20201201' AND '20201231';
特定事件名稱的值
下列查詢會顯示所有 purchase
事件和相關聯的事件參數值的 event_timestamp
:
-- Example: Query values for a specific event name.
--
-- Queries the individual timestamps and values for all 'purchase' events.
SELECT
event_timestamp,
(
SELECT COALESCE(value.int_value, value.float_value, value.double_value)
FROM UNNEST(event_params)
WHERE key = 'value'
) AS event_value
FROM
-- Replace table name.
`bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
WHERE
event_name = 'purchase'
-- Replace date range.
AND _TABLE_SUFFIX BETWEEN '20201201' AND '20201202';
您可以修改先前的查詢,顯示事件參數值總數而非清單:
-- Example: Query total value for a specific event name.
--
-- Queries the total event value for all 'purchase' events.
SELECT
SUM(
(
SELECT COALESCE(value.int_value, value.float_value, value.double_value)
FROM UNNEST(event_params)
WHERE key = 'value'
))
AS event_value
FROM
-- Replace table name.
`bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
WHERE
event_name = 'purchase'
-- Replace date range.
AND _TABLE_SUFFIX BETWEEN '20201201' AND '20201202';
在購物車中加入的前 10 項商品
以下查詢會依據使用者人數最多,顯示加入購物車的前 10 項商品。
-- Example: Top 10 items added to cart by most users.
SELECT
item_id,
item_name,
COUNT(DISTINCT user_pseudo_id) AS user_count
FROM
-- Replace table name.
`bigquery-public-data.ga4_obfuscated_web_ecommerce.events_*`, UNNEST(items)
WHERE
-- Replace date range.
_TABLE_SUFFIX BETWEEN '20201101' AND '20210131'
AND event_name IN ('add_to_cart')
GROUP BY
1, 2
ORDER BY
user_count DESC
LIMIT 10;
按購買者類型劃分的平均網頁瀏覽量 (購買者與未購買者)
以下查詢會顯示使用者平均網頁瀏覽類型 (購買者與未購買者) 的平均次數:
-- Example: Average number of pageviews by purchaser type.
WITH
UserInfo AS (
SELECT
user_pseudo_id,
COUNTIF(event_name = 'page_view') AS page_view_count,
COUNTIF(event_name IN ('in_app_purchase', 'purchase')) AS purchase_event_count
-- Replace table name.
FROM `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
-- Replace date range.
WHERE _TABLE_SUFFIX BETWEEN '20201201' AND '20201202'
GROUP BY 1
)
SELECT
(purchase_event_count > 0) AS purchaser,
COUNT(*) AS user_count,
SUM(page_view_count) AS total_page_views,
SUM(page_view_count) / COUNT(*) AS avg_page_views,
FROM UserInfo
GROUP BY 1;
網頁瀏覽順序
以下查詢顯示使用者不重複工作階段期間的網頁瀏覽順序:
-- Example: Sequence of pageviews.
SELECT
user_pseudo_id,
event_timestamp,
(SELECT value.int_value FROM UNNEST(event_params) WHERE key = 'ga_session_id') AS ga_session_id,
(SELECT value.string_value FROM UNNEST(event_params) WHERE key = 'page_location')
AS page_location,
(SELECT value.string_value FROM UNNEST(event_params) WHERE key = 'page_title') AS page_title
FROM
-- Replace table name.
`bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
WHERE
event_name = 'page_view'
-- Replace date range.
AND _TABLE_SUFFIX BETWEEN '20201201' AND '20201202'
ORDER BY
user_pseudo_id,
ga_session_id,
event_timestamp ASC;
事件參數清單
下列查詢會列出資料集中出現的所有事件參數:
-- Example: List all available event parameters and count their occurrences.
SELECT
EP.key AS event_param_key,
COUNT(*) AS occurrences
FROM
-- Replace table name.
`bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`, UNNEST(event_params) AS EP
WHERE
-- Replace date range.
_TABLE_SUFFIX BETWEEN '20201201' AND '20201202'
GROUP BY
event_param_key
ORDER BY
event_param_key ASC;
加入 Google Ads
如要擷取 GA4 事件的其他 Google Ads 資料,請設定 Google Ads 適用的 BigQuery 資料移轉服務,然後從 Google Ads 移轉作業中,將 GA4 事件資料的 collected_traffic_source.gclid
加進 ads_ClickStats_
customer_id 的 gclid
欄位。
請注意,GA4 事件資料匯出作業會為每一天分別建立資料表,Google Ads 移轉作業則會為每個客戶填入一個 ads_ClickStats_
customer_id 資料表。