Expected Google Analytics Warehoused Data

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Some information was used with permission from our friends at Stitch.

Google Analytics Warehoused integration in Magento BI utilizes the GA Core Reporting API.

In order to avoid unexpected or nonsensical results, confirm that any dimensions you use are compatible with the metric(s) you use in the Report Builder. You can check here.

A single table - called report - will be created in your Data Warehouse.

The schema of this table will be composed of the Metrics and Dimensions you selected during the setup process and two other columns: start-date and end-date.

If, for example, you selected the following Metrics and Dimensions during setup:

  • Metrics: ga:users
  • Dimensions: ga:month

The table would look like the example below.

Column Name Description
_id This column is the primary key.
_rjm_record_hash Magento BI unique identifier. This column is created by Magento BI.
_updated_at This column contains the last time that the data row was updated. This column is created by Magento BI.
start-date Identification of what day the row is for.
end-date Identification of what day the row is for.
month Selected dimension: Month of the session, a two digit integer from 01 to 12.
users Selected metric: The total number of users for the requested time period.

Reminder: Difference between GA Warehoused and Live Integration

The main differentiator is that one integration is stored (GA Warehoused), and the other is not (GA Live). In the case of GA Warehoused, this allows for manipulation of your GA data and gives you the ability to combine GA and other data sources to create insightful reporting.

Let's look at GA ad campaigns for an example of what can be done from a manipulation standpoint. Suppose you had multiple ad campaigns for Q4 with different names. The campaigns were a result of a specific marketing initiative. With warehoused data, we can create a new column that finds the campaign names in question and returns the Q4 initiative name of "Operation Dumbo".

The combination aspect allows GA data to be joined to other data in order to conduct analyses. For example, take "Total Time On Site By Ad Campaign" data from GA and join it up against "Total Spent Per Campaign" data from Facebook Ads to get a complete picture of how much engagement is costing you.

With the GA Live integration on the other hand, every GA chart is like a little silo that is not stored in your Magento BI data warehouse.