Customer Experiences with Contact Center AI - Dialogflow CX

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

• Define what Contact Center AI (CCAI) is and what it can do for contact centers.
• 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 using Dialogflow CX.
• Describe how natural language understanding (NLU) is used to enable Dialogflow conversations.
• Describe options for storing parameters and fulfilling user requests.
• Describe how to deploy virtual agents to production.
• Identify best practices for development of virtual agents in Dialogflow CX.
• Identify key aspects, such as security and compliance, in the context of contact centers.

This is a beginner to intermediate course, intended for learners with the following types of roles:

• Conversational designers: Designs the user experience of a virtual assistant. Translates the brand's business requirements into natural dialog flows.
• Citizen developers: Creates new business applications for consumption by others using high level development and runtime environments.
• Software developers: Codes computer software in a programming language (e.g., C++, Python, Javascript) and often using an SDK/API.

Completed GCP Fundamentals or have equivalent experience
 

The course includes presentations, demonstrations, and hands-on labs. Module 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 AI.
• Describe the role each component plays in a CCAI solution.

Module 2: 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 on conversation 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.

Module 3: Fundamentals of Designing Conversations

• Identify user roles and their journeys.
• Write personas for virtual agents and users.
• Model user-agent interactions.

Module 4: Dialogflow Product Options

• Describe two primary differences between Dialogflow Essentials (ES) and Dialogflow Customer Experience (CX).
• Identify two design principles for your virtual agent which apply regardless of whether you implement in Dialogflow ES or CX.
• Identify two ways your virtual agent implementation changes based on whether you implement in Dialogflow ES or CX.
• List the basic elements of the Dialogflow user interface.

Module 5: Course Review

• Review what was covered in the course as relates to the objectives.

Module 6: Fundamentals of Building Conversations with Dialogflow CX

• List the basic elements of the Dialogflow CX User Interface.
• Create entities.
• Create intents and form fill entities in training phrases.
• Train the NLU model through the Dialogflow console.
• Build a basic virtual agent to handle identified user journeys.

Module 7: Scaling with Standalone Flows

• Recognize the scenarios in which standalone flows can help scale your virtual agent.
• Implement a flow that uses other flows.

Module 8: Using Route Groups for Reusable Routes

• Define the concept of route groups with respect to Dialogflow CX.
• Create a route group.
• Recognize the scenarios in which route groups should be used.
• Identify the possible scope of a route group.
• Implement a flow that uses a route group.

Module 9: Course Review

• Review what was covered in the course as relates to the objectives.

Module 10: Testing and Logging

• Use Dialogflow tools for troubleshooting.
• Use Google Cloud tools for debugging your virtual agent.
• Review logs generated by virtual agent activity.
• Recognize ways an audit can be performed.

Module 11: Taking Actions with Fulfillment

• Characterize the role of fulfillment with respect to Contact Center AI.
• Implement a virtual agent using Dialogflow ES.
• Use Cloud Firestore to store customer data.
• Implement fulfillment using Cloud Functions to read and write Firestore data.
• Describe the use of Apigee for application deployment.

Module 12: Integrating Virtual Agents

• Describe how to use the Dialogflow API to programmatically create and modify the virtual agent.
• Describe connectivity protocols: gRPC, REST, SIP endpoints, and phone numbers over PSTN.
• Describe how to 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.
• Describe how to incorporate IVR features in the virtual agent.

Module 13: Course Review

• Review what was covered in the course as relates to the objectives.

Module 14: 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.

Module 15: Drawing Insights from Recordings with SAF

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

Module 16: Intelligence Assistance for Live Agents

• Recognize use cases where Agent Assist adds value.
• Identify, collect and curate documents for knowledge base construction.
• Describe how to 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.

Module 17: Compliance and Security

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

Module 18: Best Practices

• 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.

Module 19: Implementation Methodology

• Identify the stages of the Google Enterprise Sales Process.
• Describe the Partner role in the Enterprise Sales Process.
• Detail the steps in a Contact Center AI project using Google’s ESP.
• Describe the key activities of the Implementation Phase in ESP.
• Locate and understand how to use Google's support assets for Partners.

Module 20: Course Review

• Review what was covered in the course as relates to the objectives.