Text predictions use natural language processing (NLP), predictive and adaptive
analytics, and artificial intelligence to analyze incoming messages in your
conversational channels, such as email or chat. Text analytics can help you route work,
populate properties in business cases, and respond to users with relevant
The system creates a text prediction automatically for each new channel that you create
in App Studio. A link to the text prediction is available on the
Behavior tab in the channel configuration.
Tip: You can also create a prediction for text analytics in Prediction Studio and add the text prediction to a conversational channel
later. For more information, see Creating text predictions.
You can configure and train the models in a text prediction to predict different aspects
of emails, chat messages, or voice commands, and then use that information to automate
certain tasks in your application:
The general subject or intent of a message, such as a request for service or
support. For example, a Pega Intelligent Virtual Assistant (IVA) or an email bot can
determine that the topic of an email is a request to cancel a flight ticket, and
then open a flight cancellation business case.
Keywords and phrases in a message that the system can assign to specific
categories, such as people, locations, dates, organizations, and postal codes.
You can configure an IVA or an email bot to automatically assign the detected
entities to properties in a business case.
The attitude or opinion that a user expresses in a message: positive, neutral,
or negative. An email bot can detect negative sentiments in an email, and then
escalate the issue by automatically forwarding that information to a customer
The language of a message. An email bot can detect the language of an email, and
then automatically respond to the user in that language.
The following diagram shows the high-level workflow for configuring a text
What to do next: Automate tasks by configuring your application behavior
based on the results of the text analysis. For example, you can route emails to specific
work queues based on the detected topics as shown in the following video: