Customer Experiences with Contact Center AI

Icono Duración del curso
Duración del curso
35 horas
Lugar de impartición
Madrid / Online
Icono modalidad del curso
Presencial, Aula Virtual
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Fecha de inicio


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Acerca del curso

This instructor-led course introduces participants to contact center artificial intelligence and its use in design, development, and deployment of customer conversational solutions.

Additionally, this course provides best practices on integrating conversational solutions with existing contact center software; on establishing a framework for human agent assistance; and on implementing the solution securely and at scale.

  • Define what Google Contact Center AI is
  • Explain how Dialogflow can be used in contact center applications
  • Describe how natural language understanding (NLU) is used to enable Dialogflow conversations
  • Implement a chat virtual agent
  • Implement a voice virtual agent
  • Describe options for storing parameters and fulfilling user requests
  • Deploy a virtual agent to production
  • Identify best practices for design and deployment of virtual agents
  • Identify key aspects, such as security and compliance in the context of contact centers.

  • Completed Google Cloud Product Fundamentals or have equivalent experience

Modulo 0: Course Overview

Modulo 1: Overview of Contact Center AI

  • Define what Contact Center AI (CCAI) is and what it can do for contact centers
  • Identify each component of the CCAI Architecture: Speech Recognition Dialogflow, Speech Synthesis, Agent Assist, and Insights
  • Describe the role each component plays in a CCAI solution

Modulo 2: Google Implementation Methodology

  • Identify the stages of the Google Implementation Methodology
  • Enumerate the key activities of each implementation stage
  • Acknowledge how to use Google's support assets for Partners

Modulo 3: Conversational Experiences

  • List the basic principles of a conversational experience
  • Explain the role of conversation virtual agents in a conversational experience
  • Articulate how STT (speech to text) can determine the quality of a conversational experience
  • Demonstrate and test how speech adaptation can improve the speech recognition accuracy of the agent
  • Recognize the different NLU (natural language understanding) and NLP(natural language processing) techniques and the role they play on conversational experiences
  • Explain the different elements of a conversation (intents, entities, etc)
  • Use sentiment analysis to help with the achievement of a higher-quality conversational experience
  • Improve conversational experiences by choosing different TTS voices(Wavenet vs Standard)
  • Modify the speed and pitch of a synthesized voice
  • Describe how to leverage SSML to modify the tone and emphasis of a synthesized passage

Modulo 4: Fundamentals of building conversations with Dialogflow

  • Identify user roles and their journeys
  • Write personas for virtual agents and users
  • Model user-agent interactions
  • List the basics elements of the Dialogflow user interface
  • Build a virtual agent to handle identified user journeys
  • Train the NLU model through the Dialogflow console
  • Define and test intents for a basic agent
  • Train the agent to handle expected and unexpected user scenarios
  • Recognize the different types of entities and when to use them
  • Create entities
  • Define and test entities on a basic agent
  • Implement slot filling using the Dialogflow UI
  • Describe when Mega Agent might be used

Modulo 5: Maintaining context in a conversation

  • Create follow up intentsRecognize the scenarios in which context should be used
  • Identify the possible statuses of a context (active versus inactive context)
  • Implement dialogs using input and output contexts

Modulo 6: Moving from Chat agent to Voice agent

  • Describe two ways that the media type changes the conversation
  • Configure the telephony gateway for testing
  • Test a basic voice agent
  • Modify the voice of the agent
  • Show how the different media types can have different responses
  • Consider the modifications needed when moving to production
  • Be aware of the telephony integration for voice in a production environment

Modulo 7: Taking actions with fulfillment

  • Define the role of fulfillment with respect to CCAI
  • Characterize what needs to be collected in order to fulfill a request
  • Identify existing backend systems on the customer infrastructure
  • Use Firestore to store mappings returned from functions
  • Appreciate that the interaction with customers' data storages will vary based on the customer's data warehouses
  • Implement fulfillment using Cloud Functions
  • Implement fulfillment using Python on AppEngine
  • Describe the use of Apigee for application deployment

Modulo 8: Testing and Logging

  • Debug a virtual agent by testing intent accuracy
  • Debug fulfillment by testing the different functions and integrations with backend systems through API calls
  • Implement version control to achieve more scalable collaboration
  • Log conversations using Cloud Logging
  • Recognize ways that audits can be performed

Modulo 9: Environment Management

  • Create Draft and Published versions of your virtual agent
  • Create environments where your virtual agent will be published
  • Load a saved version of your virtual agent to Draft
  • Change which version is loaded to an environment

Modulo 10: Intelligent Assistance for Live Agents

  • Recognize use cases where Agent Assist adds value
  • Identify, collect, and curate documents for knowledge base construction
  • Set up knowledge bases
  • Describe how FAQ Assist works
  • Describe how Document Assist works
  • Describe how the Agent Assist UI works
  • Describe how Dialogflow Assist works
  • Describe how Smart Reply works
  • Describe how real-time entity extraction works

Modulo 11: Integrating a virtual agent with third parties

  • Use the Dialogflow API to programmatically create and modify the virtual agent
  • Describe connectivity protocols: gRPC, REST, SIP endpoints, and phone numbers over PSTN
  • Replace existing head intent detection on IVRs with Dialogflow intents
  • Describe virtual agent integration with Google Assistant
  • Describe virtual agent integration with messaging platforms
  • Describe virtual agent integration with CRM platforms (eg. Salesforce and Zendesk)
  • Describe virtual agent integration with enterprise communication platforms(eg. Genesys, Avaya, Cisco, and Twilio)
  • Explain the ability that telephony providers have of identifying the caller and how that can modify the agent design
  • Incorporate IVR features in the virtual agent.

Modulo 12: Drawing insights from call recordings

  • Analyze audio recordings using the Speech Analytics Framework (SAF)

Modulo 13: Methods of compliance with federal regulations

  • Describe two ways that security can be implemented on a Contact CenterAI integration
  • Identify current compliance measures and scenarios where compliance is needed

Modulo 14: Best practices for virtual agents

  • Convert pattern matching and decision trees to smart conversational design
  • Recognize situations that require escalation to a human agent
  • Support multiple platforms, devices, languages, and dialects
  • Use Diagflow’s built-in analytics to assess the health of the virtual agent
  • Perform agent validation through the Dialogflow UI
  • Monitor conversations and Agent Assist
  • Institute a DevOps and version control framework for agent development and maintenance
  • Consider enabling spell correction to increase the virtual agent's accuracy

Modulo 15: Course Summary

  • Recapitulate was covered during this course

Documentación Oficial de Google Cloud - Customer Experiences with Contact Center AI

  • Formador Certificado por GCP
  • Más de 5 años de experiencia profesional
  • Más de 4 años de experiencia docente
  • Profesional activo en empresas del sector IT

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