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Introduction
Customer support ticket escalation is a critical aspect of any organization’s customer service operations. When customer issues are not resolved in a timely and satisfactory manner, they may escalate to higher levels of management, leading to increased costs and decreased customer satisfaction. Predicting customer support ticket escalation can help organizations proactively address potential issues before they escalate, thereby improving customer satisfaction and reducing operational costs.
This thesis aims to explore the predictive factors that contribute to customer support ticket escalation and develop a model to forecast when a ticket is likely to escalate. By leveraging data analytics and machine learning techniques, we seek to help organizations optimize their customer support processes and improve overall customer experience.
Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms
Chapter 2: Literature Review
2.1 Overview of Customer Support Ticket Escalation
2.2 Factors Contributing to Ticket Escalation
2.3 Predictive Modeling in Customer Support
2.4 Machine Learning Techniques for Predictive Analytics
2.5 Previous Studies on Ticket Escalation Prediction
2.6 Case Studies on Ticket Escalation Prediction
2.7 Best Practices in Customer Support Ticket Management
2.8 Customer Satisfaction and Ticket Escalation
2.9 Technology Solutions for Ticket Escalation Prevention
2.10 Summary of Literature Review
Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Preprocessing
3.4 Feature Selection
3.5 Model Development
3.6 Model Evaluation
3.7 Ethical Considerations
3.8 Limitations of the Study
Chapter 4: Discussion of Findings
4.1 Predictive Factors for Ticket Escalation
4.2 Performance Evaluation of the Predictive Model
4.3 Comparison with Existing Models
4.4 Practical Implications for Organizations
4.5 Recommendations for Future Research
Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Implications for Practice
5.3 Contributions to Knowledge
5.4 Limitations of the Study
5.5 Recommendations for Future Research
5.6 Conclusion
Thesis Overview on Predicting Customer Support Ticket Escalation
Customer support ticket escalation is a common occurrence in many organizations, where customer issues are not resolved in a timely manner and require intervention from higher levels of management. This can lead to increased operational costs, decreased customer satisfaction, and potential reputational damage for the organization. Predicting when a ticket is likely to escalate can help organizations proactively address potential issues before they escalate, thereby improving customer satisfaction and reducing operational costs.
This thesis aims to explore the predictive factors that contribute to customer support ticket escalation and develop a model to forecast when a ticket is likely to escalate. By leveraging data analytics and machine learning techniques, we seek to help organizations optimize their customer support processes and improve overall customer experience. The research methodology involves data collection, preprocessing, feature selection, model development, and evaluation to create an accurate and reliable predictive model.
The literature review provides an overview of customer support ticket escalation, factors contributing to escalation, predictive modeling in customer support, machine learning techniques, previous studies on ticket escalation prediction, and best practices in customer support ticket management. The discussion of findings will highlight predictive factors for ticket escalation, performance evaluation of the predictive model, practical implications for organizations, and recommendations for future research.
In conclusion, this thesis aims to contribute to the body of knowledge on predicting customer support ticket escalation and provide valuable insights for organizations looking to improve their customer support processes. By developing a robust predictive model, organizations can proactively address customer issues, enhance customer satisfaction, and optimize their operations.
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