AI Powered Travel Dispute Resolution Workflow for Customers

AI-Driven Dispute Resolution for Travel Bookings

1. Initial Dispute Identification

1.1 Customer Complaint Submission

Customers submit complaints through an online portal or mobile app.

1.2 AI Chatbot Interaction

An AI-driven chatbot, such as Zendesk Chat, engages with customers to gather initial details about the dispute.

2. Data Collection and Analysis

2.1 Automated Data Gathering

Utilize AI tools like IBM Watson to collect relevant data from booking systems, customer emails, and chat logs.

2.2 Sentiment Analysis

Implement sentiment analysis tools, such as MonkeyLearn, to assess the emotional tone of customer communications.

3. Dispute Categorization

3.1 AI-Driven Classification

Employ machine learning algorithms to categorize disputes into predefined categories (e.g., refunds, cancellations, service issues).

3.2 Prioritization of Disputes

Utilize AI to prioritize disputes based on severity and customer impact using tools like TensorFlow.

4. Resolution Proposal Generation

4.1 AI-Generated Solutions

Leverage AI systems to generate possible resolutions based on historical data and similar past disputes.

4.2 Recommendation Engine

Implement recommendation engines, such as Google Cloud AI, to suggest optimal resolutions to customer service representatives.

5. Customer Communication

5.1 Automated Response System

Use automated email systems to communicate proposed resolutions to customers.

5.2 AI-Enhanced Follow-Up

Deploy AI tools to schedule follow-ups and check customer satisfaction post-resolution.

6. Dispute Resolution Execution

6.1 Implementation of Resolutions

Utilize AI-driven workflow management tools, such as Asana, to track the execution of resolutions.

6.2 Feedback Loop Creation

Establish a feedback loop using AI analytics to refine resolution processes based on outcomes and customer feedback.

7. Continuous Improvement

7.1 Data Analysis and Reporting

Analyze dispute resolution data using AI analytics platforms, such as Tableau, to identify trends and areas for improvement.

7.2 AI Model Refinement

Continuously update AI models based on new data and evolving customer expectations to enhance future dispute resolution capabilities.