AI Integrated Chatbot to Human Agent Escalation Workflow Guide

Chatbot-to-Human Agent Escalation Workflow

1. Initial Customer Interaction

1.1 Customer Inquiry

The customer initiates contact through a digital channel, such as a website chat widget or mobile app.

1.2 Chatbot Engagement

An AI-driven chatbot, such as Zendesk Chatbot or LivePerson, engages the customer to gather preliminary information and address common queries.

2. AI-Driven Analysis

2.1 Intent Recognition

The chatbot utilizes natural language processing (NLP) algorithms to identify the customer’s intent and categorize the inquiry.

2.2 Knowledge Base Consultation

The chatbot references a predefined knowledge base, powered by tools like IBM Watson or Google Dialogflow, to provide relevant responses.

3. Escalation Criteria Assessment

3.1 Determine Complexity

Based on the interaction, the chatbot assesses whether the inquiry requires human intervention. Criteria may include:

• Complexity of the issue
• Customer sentiment analysis
• Duration of interaction

3.2 Escalation Trigger

If the inquiry meets escalation criteria, the chatbot triggers the escalation process.

4. Human Agent Notification

4.1 Agent Assignment

The system assigns a qualified human agent, utilizing tools like Salesforce Service Cloud or Freshdesk, based on availability and expertise.

4.2 Notification to Agent

The assigned agent receives a notification, including the chat history and customer details, through an integrated dashboard.

5. Human Agent Interaction

5.1 Agent Engagement

The human agent engages with the customer through the same channel, reviewing the chat history to ensure continuity.

5.2 Resolution Process

The agent addresses the customer’s issue, utilizing CRM tools to access relevant information and resources.

6. Post-Interaction Follow-Up

6.1 Customer Feedback Collection

After resolution, the agent prompts the customer for feedback on their experience using tools like SurveyMonkey or built-in feedback forms.

6.2 Data Analysis and Reporting

Feedback and interaction data are analyzed to identify trends and improve both chatbot performance and customer service strategies.

7. Continuous Improvement

7.1 AI Model Training

Insights gained from interactions are used to refine the chatbot’s AI model, enhancing its ability to handle future inquiries.

7.2 Knowledge Base Updates

The knowledge base is regularly updated with new information and common issues to improve the chatbot’s effectiveness.