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Introduction

Customer churn is a critical issue in the hotel industry as it directly affects revenue and profitability. Identifying customers who are likely to churn and implementing strategies to retain them is essential for the success of a hotel business. With the advancement of technology, machine learning algorithms have become popular tools for predicting customer churn. This study focuses on customer churn prediction in the hotel industry using booking data and machine learning techniques.

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 Two: Literature Review
2.1 Customer churn in the hotel industry
2.2 Machine learning algorithms for churn prediction
2.3 Previous studies on customer churn prediction
2.4 Factors influencing customer churn
2.5 Importance of customer retention
2.6 Data mining techniques in the hotel industry
2.7 Benefits of predictive analytics
2.8 Challenges in customer churn prediction
2.9 Strategies for customer retention
2.10 Review of relevant theoretical frameworks

Chapter Three: 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 Cross-validation
3.8 Performance metrics
3.9 Ethical considerations

Chapter Four: Discussion of Findings
4.1 Analysis of customer churn predictors
4.2 Evaluation of machine learning models
4.3 Comparison of predictive performance
4.4 Interpretation of results
4.5 Implications for the hotel industry
4.6 Recommendations for improving customer retention
4.7 Future research directions
4.8 Managerial implications

Chapter Five: Conclusion and Summary
5.1 Summary of key findings
5.2 Contribution to the existing literature
5.3 Practical implications
5.4 Limitations of the study
5.5 Recommendations for future research
5.6 Conclusion

Thesis Overview on Customer Churn Prediction in the Hotel Industry using Booking Data and Machine Learning

Customer churn prediction is a critical aspect of hotel management, as retaining customers is essential for sustainable business growth. With the increasing availability of booking data and advancements in machine learning algorithms, it is now possible to predict customer churn accurately. This thesis aims to explore the application of machine learning techniques in predicting customer churn in the hotel industry using booking data.

The introduction provides a brief overview of the research topic, highlighting the importance of customer churn prediction and the relevance of machine learning in this context. The background of the study sets the stage by discussing the current challenges faced by the hotel industry in retaining customers. The problem statement identifies the gap in the existing literature and the need for research in this area.

The objectives of the study are outlined to guide the research process, followed by a discussion on the limitations and scope of the study. The significance of the study is highlighted to emphasize the potential impact of the research findings on hotel management practices. The structure of the thesis is provided to give readers an overview of the content and organization of the study.

The literature review chapter examines previous research on customer churn prediction, machine learning algorithms, and factors influencing customer churn in the hotel industry. It also discusses the importance of customer retention and strategies for improving customer loyalty. The research methodology chapter outlines the research design, data collection, data preprocessing, and model selection process.

The discussion of findings chapter presents the analysis of customer churn predictors, evaluation of machine learning models, and interpretation of results. It also includes recommendations for improving customer retention and future research directions. The conclusion and summary chapter summarizes the key findings, contributions to the literature, practical implications, and recommendations for future research.

In conclusion, this thesis aims to enhance our understanding of customer churn prediction in the hotel industry using booking data and machine learning. By applying advanced analytical tools and techniques, hotel managers can identify at-risk customers and implement targeted retention strategies to enhance customer satisfaction and loyalty.

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