This feature is available only in Business and Enterprise plans.
By default, Google Analytics is the source of transactions and users behavior data.
If you want to attribute value of products that were bought not online (e.g. brick-and-mortar, call center, etc.), change transactions data source on the model page.
To do that firstly click on data source change button:
And then select Google BigQuery view or table:
Information in Google BigQuery table or view has to be in the following format:
|Field name||Data type||Description|
Data in the field must be consistent with data in the same field in users behavior data source (user.Id for OWOX BI Pipeline; customDimensions.index and customDimensions.value for BigQuery Export for Google Analytics).
||Product ID (if the model attributes value of product)|
||Count of products in a transaction|
||Time of order creation|
During model calculation data of the source you add replaces data from Google Analytics with grouping by
product_id. That's why is critical to have consistent transactions, users and products data in both sources.
If User ID information is not available, you can populate Client IDs into
user_id field to match data by Client IDs.
There might be three cases when the transactions are replaced:
- There is a transaction in Google Analytics, but new transactions data source doesn't contain it
Value of such transactions isn't attributed.
- There is no transaction in Google Analytics, but new transactions data source contains it
For such transactions purchase sessions are generated. The sessions have medium set as 'offline'.
If a user had online interactions prior to offline purchase, those online interactions get value.
Value for all funnel interactions that didn't happen online is attributed to the offline purchase session.
If there were no online interactions prior to offline purchase, all value of offline transaction is attributed to its session.
- There is a transaction both in Google Analytics and new transaction data source
All information about such transactions and sessions that led to them matches Google Analytics data.