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Training data for the Email channel


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To use Pega Email Bot in your application to seamlessly respond to user problems, train the system to recognize different user input in emails, such as help requests or issues. When you train the data for the email bot, the system learns from training records, improves the artificial intelligence algorithms, and provides better responses to user input.

For example, when you train the data for the email bot and apply changes to the model, a user might send an email requesting information about car insurance. The system then performs the analysis of the email content and detects the correct topic, entities, sentiment, and language. Based on this information and intelligent email routing, the email bot then runs automated tasks, such as starting a business case for a car insurance quote, and sends an automatic reply.
Build an email bot by configuring the Email channel. For more information, see Building an Email channel.

To ensure that the email bot learns from the training records and detects the correct information, such as topics and entities, apply the training changes to the text analytics model for the system. You apply changes to the model after you enable the training data recording, and then create and review the training records.

  1. Enable training of your text analytics model for the email bot.

    For more information, see Enabling the training data recording for an email bot.

  2. Improve the email bot model by providing sample training records.

    For more information, see Creating training data manually for an email bot.

  3. Update the training data by ensuring that the email bot detects the right information by using text analysis.

    For more information, see Correcting training data in an email bot.

  4. Optional:

    To move the training data to another email bot, export and import the training records.

    This action makes the email bot easier to maintain and build in production, QA and development environments. For more information, see Transferring training data to another email bot.

  5. Make the email bot learn from the training data by building a text analytics model.

    This action improves the artificial intelligence of the system. For more information, see Applying changes to a text analytics model for an email bot.

Triage incoming emails for the email bot. For example, update text analysis information, reply to users, transfer triage cases, spin off business cases, or resolve triage cases. For more information, see Triaging incoming emails.

  • Exploring text analyzers

    Text analyzers for Pega Intelligent Virtual Assistant (IVA) and Pega Email Bot process user input and help the system find the best matching response by using natural language processing (NLP) and adaptive analytics. You can configure text analyzers to detect topics, entities, sentiment, and language in an email, chat text message, or a voice command.

  • Understanding text analysis

    Text analysis is an important aspect of conversational channels that enables a Pega Platform application to intelligently and seamlessly interact with a user in a natural conversational manner. Text analyzers examine user input one by one using natural language processing (NLP), adaptive analytics, and artificial intelligence.

  • Using the Email channel

    Improve Pega Email Bot interactions with users and applications by using the Email channel. To ensure that the email bot intelligently responds to user emails, classify and add the training data to the text analytics model. You can also create custom reports for the email bot.

  • Pega Email Bot overview

    Pega Email Bot is a bot system that intelligently interacts with your application to help users with their problems and speed up business processes. Interacting with an email bot through emails, users can more efficiently address their concerns and resolve problems, for example, by requesting more information and opening a business case in the system.

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