In this report page, we are presenting the core mapping Boxalino is able to perform between:

1. The Google Ads Data (on the left)

2. The Google Ads Shopping Data (in the center)

3. The Attributed (Shopping) Metrics (on the right)

Google Ads Data - LEFT

You can see here your overall Google Ads Data calculated from the data provided by the Google Ads Data Transfer.

These numbers are on the same basis as the ones on the prior report pages.

Google Ads Shopping Data - CENTER

You can see here your ShoppingPerformance Data calculated from the data provided shopping performance as documented here: https://developers.google.com/google-ads/api/fields/v12/shopping_performance_view

These data are also provided by the Google Ads Data Transfer.

Attributed (Shopping) Metrics - RIGHT

The right column is based on an attribution of the E-shop Transactions and Revenue from the E-shop data as well as the Margin (if available) from the E-shop or ERP data as per the following Diagram:

The attribution process can be described as follows:

1. The Shopping Performance Data is aggregated with the following dimensions:

   • Date

   • Device

   • Campaign Id

   • Offer Id (i.e.: one id per Product Detail Page, even if each product variant is individually advertised)
     

2. For each defined attribution model (by default 'Google Ads - Last Click is the only one defined) each Google Analytics Transaction (according to GA4 data in BigQuery) is attributed to a traffic source and if applicable a GCLID (Google Click Identifier) and the Offer id related to this GCLID.
   
   GA4 plays therefore a key role to connect the Google Ads data with the E-shop/ERP data in this report.
   

3. The Google Analytics transaction ids are themselves mapped with the e-shop (and optionally the ERP) data in order to retrieve the Attributed: Transactions, Revenue and Margin.

Differences between CENTER and RIGHT

You will probably notice differences between the Google Ads Shopping Conversions & Conversion Value and the Attributed (Shopping) Transactions & Revenue.

Differences are mainly caused by differences in the attribution models and it is quite common to see differences of 10% or more.

As the following pages are taking the right columns as a basis for the Shopping analysis, it is important to understand where the numbers are coming from.