In this course, learn how to design customer conversations using Contact Center Artificial Intelligence (CCAI). You’ll use Dialogflow CX to create virtual agents and test them using the simulator. Learn to add functionality to access data from external systems, making virtual agents conversationally dynamic. You’ll be introduced to testing methods, connectivity protocols, APIs, environment management, and compliance measures. Learn best practices for integrating conversational solutions with your existing contact center software and implementing solutions securely and at scale.
Objectives
In this course, participants will learn the following skills:
- Define Google CCAI.
- Explain how Dialogflow can be used in Contact Center applications.
- Implement a virtual agent using Dialogflow CX.
- Read and write data from Firestore using Cloud Functions.
- Use Dialogflow tools and cloud logging for troubleshooting.
- Describe how to manage virtual agent environments.
- Identify general best practices for virtual agents.
- Identify key aspects such as security and compliance in the context of contact centers.
- Analyze audio recordings using the Speech Analytics Framework (SAF).
- Recognize use cases where Agent Assist adds value.
Audience
This is a beginner 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.
- Operations specialists: Monitors system operations and troubleshoots problems. Installs, supports, and maintains network and system tools.
Prerequisites
To get the most out of this course, participants should have:
• Completed Google Cloud Fundamentals: Core Infrastructure or have equivalent experience
Duration
4 days or 32 hours
Investment
Check the next open public class in our enrollment page.
If you are interested in a private training class for your company, contact us.
Course outline
The course includes presentations, demonstrations, and hands-on labs.
- 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.
- 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.
- Identify user roles and their journeys.
- Write personas for virtual agents and users.
- Model user-agent interactions
- 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.
- Review what was covered in the course as relates to the objectives.
- 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.
- Recognize the scenarios in which standalone flows can help scale your virtual agent.
- Implement a flow that uses other flows.
- 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.
- Review what was covered in the course as relates to the objectives.
- 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.
- 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.
- 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.
- Review what was covered in the course as relates to the objectives.
- 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.
- Analyze audio recordings using the Speech Analytics Framework (SAF).
- 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.
- Describe two ways security can be implemented on a CCAI integration.
- Identify current compliance measures and scenarios where compliance is needed.
- 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.
- 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.
- Review what was covered in the course as relates to the objectives.