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Introduction:

Customer service call centers are an essential part of any business, as they serve as the primary point of contact for customers seeking assistance or information. In today’s highly competitive market, it is crucial for businesses to efficiently manage their call volume to ensure customer satisfaction and minimize costs. Predicting customer service call volume is a challenging task that requires a thorough understanding of the factors that influence call volume, such as seasonality, promotions, and customer behavior.

This thesis aims to explore the various methods and techniques used to predict customer service call volume, with the goal of helping businesses improve their call center operations. By accurately forecasting call volume, businesses can better allocate resources, optimize staffing levels, and improve overall customer service.

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 Service Call Centers
2.2 Factors Influencing Call Volume
2.3 Traditional Methods of Call Volume Prediction
2.4 Machine Learning Approaches to Call Volume Prediction
2.5 Evaluation Metrics for Call Volume Prediction Models
2.6 Case Studies on Call Volume Prediction
2.7 Challenges and Limitations in Predicting Call Volume
2.8 Best Practices for Call Volume Prediction
2.9 Recent Trends in Call Volume Prediction
2.10 Gaps in Existing Literature

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Selection
3.5 Model Selection
3.6 Model Evaluation
3.7 Performance Metrics
3.8 Experimental Setup
3.9 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Analysis of Call Volume Prediction Models
4.2 Comparison of Different Prediction Techniques
4.3 Impact of Call Volume Prediction on Business Operations
4.4 Practical Implications for Businesses
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 the Field
5.4 Limitations of the Study
5.5 Recommendations for Future Research

Thesis Overview on Predicting Customer Service Call Volume:

The prediction of customer service call volume is a critical aspect of call center management, as it directly impacts the efficiency and effectiveness of customer service operations. This thesis aims to investigate the various methods and techniques used to predict call volume, with the goal of providing valuable insights for businesses looking to optimize their call center performance.

The thesis begins with an introduction that provides a background of the study, defines the problem statement, outlines the objectives, limitations, and scope of the study, and explains the significance of the research. The structure of the thesis is also outlined, along with definitions of key terms.

The literature review in chapter two provides an overview of customer service call centers, discusses the factors influencing call volume, reviews traditional and machine learning approaches to call volume prediction, evaluates metrics for model performance, presents case studies, discusses challenges and limitations, highlights best practices, and identifies gaps in existing literature.

Chapter three details the research methodology, including research design, data collection, preprocessing, feature selection, model selection, evaluation metrics, experimental setup, and ethical considerations.

Chapter four presents a discussion of the findings, analyzing call volume prediction models, comparing different techniques, discussing the impact on business operations, outlining practical implications, and providing recommendations for future research.

Lastly, chapter five offers a conclusion and summary of the thesis, highlighting key findings, discussing implications for practice, outlining contributions to the field, addressing limitations of the study, and recommending areas for future research. Through this comprehensive analysis, this thesis aims to contribute to the existing body of knowledge on predicting customer service call volume and provide practical insights for businesses seeking to improve their call center operations.

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