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Advanced queries

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);

Basic event queries

User-data queries

Advanced queries Stay organized with collections Save and categorize content based on your preferences.