You can access detailed information about individual products within BigQuery by mapping GA4 Ecommerce Dimensions to BigQuery fields, including the product ID, name, variant, category, price, and more. 

GA4 ecommerce dimensions to BigQuery mapping allows you to identify top-selling products, analyze product performance across categories, and understand user behaviour related to specific items within BigQuery.

Here’s a breakdown of GA4 Ecommerce dimensions and their corresponding BigQuery fields:

Points to Consider:

item_coupon: While ‘MAX(items.promotion_id)’ might capture the ID of a promotion associated with an item, it might not directly represent the coupon code used by the customer. Consider using ‘CASE WHEN’ statements or exploring other fields within ‘ecommerce.promotions’ schema if you specifically need the coupon code.

local_item_price: There might not be a dedicated field for “local item price” within the standard GA4 ecommerce schema. You might need to perform calculations based on ‘price’ and currency conversion rates if you want to analyze price in a specific local currency.

order_coupon: The ‘purchase.coupon’ field likely captures the order-level coupon code, which might differ from an item-specific coupon (‘item_coupon’).

Transaction ID: While ‘MAX(transaction_id)’ retrieves a transaction ID, it might not necessarily be unique across your entire BigQuery dataset. Consider using appropriate filters or concatenating with other identifiers for uniqueness.

Consider using ‘CASE WHEN’ statements within your BigQuery queries to handle scenarios where a single field might not perfectly capture the desired data element.