Customer Experiences with Contact Center AI

After completing the Customer Experiences with Contact Center AI course, students will be able to:

• 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.

• Architects and systems integrators implementing Contact Center AI
• Conversational Architects
• Contact center virtual agent and application developers
• Business managers

• Completed Google Cloud Product Fundamentals or have equivalent experience.
• Desirable but not required: Knowledge of a programming language such as Python or JavaScript.

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.

Conversational Experiences

• List the basic principles of a conversational experience.
• Explain the role of conversation virtual agents in a conversation experience.
• Articulate how STT (speech to text) can determine the quality of a conversation 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 in conversati
  • experiences.
• Explain the different elements of a conversation (intents, entities, etc.).
• Use sentiment analysis to help with the achievement of a higher-quality conversation experience.
• Improve conversation 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.

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 basic 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.
• Demonstrate how to add access to a knowledge base for your virtual agent to answer customer questions straight from a company FAQ

Maintaining Context in a Conversation

• Create follow-up intents.
• Recognize 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.

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

Taking Actions with Fulfillment

• Define the role of fulfillment with respect to Contact Center AI.
• 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 storage will vary based
  • their data warehouses.
• Implement fulfillment using Cloud Functions.
• Implement fulfillment using Python on AppEngine.
• Describe the use of Apigee for application deployment.

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.

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.

Drawing Insights from Recordings

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

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 (such as Salesforce and Zendesk).
• Describe virtual agent integration with enterprise communication platforms (such as 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.

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.

Methods of Compliance with Federal Regulations

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

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.