Training Pega Email Bot to identify addresses

Pega Email Bot™ learns with every email that it processes.

This demonstration shows how you can help train Pega Email Bot to improve its accuracy.

This demonstration uses the MySupport channel, which is already configured with a natural language processing (NLP) model and trained to recognize address entities.

U+ Bank wants to improve the text analysis performed by Pega Email Bot. You are asked to help train the email bot to identify addresses. To support this scenario, you must update the training data for the MySupport channel.

Sending a test email to create an Account Address Change case

First, you send an email to test how Pega Email Bot processes the message. The email must trigger an Address Change service case. In this scenario, Sara Connor is moving and sends an email to U+ Bank to request an address change.

Reviewing entities and make required changes

As CSR , you log in to the Interaction Portal and find Sara's address change case assigned to you. You open the case and review its status.

You start the suggested Address change case.

The email entry shows the email details. Fields are color-coded to help you visually identify the mappings. You can add a missing field or correct a mapping.

You select the street address, city, and state and add the values to the address change form. You notice that the postal code was not identified because it has an extra number. You add the postal code and correct the entry.

You complete the case.

The updated email is added to the MySupport email channel as a training record.

Reviewing and approving the training records

As administrator, you log in to App Studio to review activity on the MySupport channel. You check the Training tab to see how Pega Email Bot has handled recent messages.

A new training record for the Address Change case shows the changes made by the CSR. A training record is identified as triaged following a CSR update.

When a record is selected, the email content detail, including the NLP analysis, is shown in the left pane.

You review the training record, make any changes, then mark it as reviewed, which means that the record is approved for training in the text analytics model.

To further train Pega Email Bot, you add three more records that contain a street address. These examples help the bot become more accurate in identifying the entity.

You mark the new records as reviewed.

You build the model, so that the new records are reflected in Pega Email Bot's learning.

This concludes the demonstration. You learned how to improve text analytics by training the email bot.