View your generated predictions contextually by storing them in records. To write back a
prediction, select a preconfigured output connector and then select an object and field to store
the prediction. You can write back predictions only if you’ve trained and deployed the model for a
template configuration.
Required Editions
Available in: Lightning Experience
User Permissions Needed
To set up a CRM Analytics template configuration:
Scoring Framework Admin
Before you configure write back of predictions, configure an output connection in Analytics
Studio to write the prediction.
From Setup, in the Quick Find box, enter Industries Cloud Einstein,
and then select Scoring Framework.
On the card of the template configuration that you want to use, click , and select
Edit.
For Configure Write Back of Predictions, click Set Up.
Select the output connection to write the prediction.
Select the object and field that you want to store the prediction in.
Note For information about Output Connector Limits, see CRM Analytics
Limits. You can purchase more licenses to increase the base limits. For information
about the licenses, see Data
Pipelines Limits.
If the writeback object isn’t the object selected for training and scoring, specify join
keys from the writeback object and the object used for training and scoring.
The join keys define the relationship between the two objects.
To continue defining the template configuration, click Save &
Continue.
You can also save your changes and return to the Scoring Framework Setup page.
Example An insurance company uses Scoring Framework to get predictions about customers who aren’t
likely to renew their insurance policy. In Salesforce, the customer details are stored in Account
records. The insurance company’s Salesforce admin creates a custom field on the Account object to
store the predicted non-renewal likelihood score. The admin then selects Account and its custom
field as the object and field to store the predictions in. So, financial analysts can take the
right steps by viewing this prediction score within the context of the customers’
details.
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