AI-generated Key Takeaways
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This page provides advanced BigQuery queries for analyzing Google Analytics 4 event export data, going beyond basic queries.
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It includes queries to identify products frequently purchased together, calculate average spending per purchase session, and retrieve the latest session information for specific users.
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The queries are demonstrated with examples and explanations, including simplified and optimized versions where applicable.
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Before using these advanced queries, it's recommended to familiarize yourself with the basic BigQuery queries for Google Analytics 4.
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Users of Universal Analytics can find similar resources in the BigQuery cookbook for Universal Analytics linked on the page.
The advanced queries in this page apply to the BigQuery event export data for Google Analytics. See BigQuery cookbook for Universal Analytics if you are looking for the same resource for Universal Analytics. Try the basic queries first before trying out the advanced ones.
Products purchased by customers who purchased a certain product
The following query shows what other products were purchased by customers who purchased a specific product. This example does not assume that the products were purchased in the same order.
The optimized example relies on BigQuery scripting features to define a variable that declares which items to filter on. While this does not improve performance, this is a more readable approach for defining variables compared creating a single value table using a WITH clause. The simplified query uses the latter approach using the WITH clause.
The simplified query creats a separate list of "Product A buyers" and does a join with that data. The optimized query, instead, creates a list of all items a user has purchased across orders using the ARRAY_AGG function. Then using the outer WHERE clause, purchase lists across all users are filtered for the target_item and only relevant items are shown.
Simplified
-- 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; Optimized
-- 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; Average amount of money spent per purchase session by user
The following query shows the average amount of money spent per session by each user. This takes into account only the sessions where the user made a purchase.
-- 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 Latest Session Id and Session Number for users
The following query provides the list of the latest ga_session_id and ga_session_number from last 4 days for a list of users. You can provide either a user_pseudo_id list or a user_id list.
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);