Customer Experiences with Contact Center AI

• 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.

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

• 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