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Mission

Email Bots for Pega Customer Service

4 Modules

3 Challenges

2 hrs 40 mins

Visible to all users
Intermediate Pega Customer Service 8.4

Learn how Pega Email Bot™ can act as a virtual assistant to your customer service representatives. Reduce time spent categorizing and routing inbound emails and provide quicker, more efficient responses to customer requests. As the Pega Email Bot learns from processing emails, you can completely automate some email cases.

This Mission has been updated. Click here to continue your progress on the latest version.
  • Module

    Intelligent email

    3 Topics

    20 mins

  • Learn how Pega Email Bot™ leverages Pega’s industry-leading natural language processing (NLP) and process automation to interpret, route, audit, and...

  • Challenge

    Suggesting a reply based on email content

    6 Tasks

    15 mins

  • Your team asks you to update the Pega Customer Service™ email channel. You need a suggested reply for when a customer sends an email and there is some...

  • Module

    Routing email based on content

    3 Topics

    25 mins

  • Pega Email Bot can detect the topic or sentiment of an email and route the email to the appropriate operator or queue. Intelligent routing allows the...

  • Challenge

    Routing email based on content

    4 Tasks

    15 mins

  • U+ is a major company in the financial industry that wants to improve engagement with customers. U+ has recently opened an email channel where...

  • Module

    Creating a case from email

    2 Topics

    25 mins

  • Pega Email Bot can improve email response by starting a service case based on the content of a customer email.

  • Challenge

    Creating a case from an email

    4 Tasks

    20 mins

  • Your customer, U+ Bank, wants to speed up email interactions for address changes by pre-populating cases with the correct values. For address change...

  • Module

    Training the email bot

    2 Topics

    20 mins

  • Pega Email Bot uses natural language processing (NLP) to analyze and learn from patterns of conversation between customer service representatives...

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