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Introduction:

Customer churn, or the loss of customers, is a critical issue for utility companies as it can have a significant impact on their revenue and profitability. In recent years, there has been a growing interest in using predictive analytics to identify customers who are at risk of churning, in order to take proactive measures to retain them. This research aims to explore the use of predictive modeling techniques to predict customer churn in utility companies.

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 Churn in Utility Companies
2.2 Factors influencing Customer Churn
2.3 Predictive Modeling Techniques
2.4 Previous Studies on Predicting Customer Churn
2.5 Customer Retention Strategies
2.6 Data Mining and Machine Learning
2.7 Customer Segmentation
2.8 Customer Lifetime Value
2.9 Customer Satisfaction and Loyalty
2.10 Summary of Literature Review

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Variable Selection
3.4 Model Development
3.5 Model Evaluation
3.6 Data Preprocessing
3.7 Sampling Techniques
3.8 Software Tools
3.9 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Descriptive Analysis of Data
4.2 Model Performance Evaluation
4.3 Key Predictors of Customer Churn
4.4 Comparison of Predictive Models
4.5 Managerial Implications
4.6 Recommendations for Utility Companies
4.7 Future Research Directions

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusion
5.3 Implications for Practitioners
5.4 Contributions to Theory
5.5 Recommendations for Future Research

Thesis Overview on Predicting Customer Churn in Utility Companies:

Customer churn is a pressing issue for utility companies, as losing customers can have adverse effects on their revenue and profitability. In response to this challenge, this research focuses on predicting customer churn using advanced predictive modeling techniques. The study aims to explore the factors influencing customer churn, develop predictive models to identify at-risk customers, and provide recommendations for utility companies to improve customer retention strategies.

The literature review will discuss the existing research on customer churn in utility companies, factors influencing churn, predictive modeling techniques, customer retention strategies, and other relevant topics. The research methodology will outline the research design, data collection methods, variable selection, model development, and evaluation techniques to be used in the study.

The discussion of findings will present the descriptive analysis of data, model performance evaluation, key predictors of customer churn, and comparisons of predictive models. The chapter will also provide managerial implications and recommendations for utility companies based on the research findings.

In conclusion, this research aims to contribute to the existing literature on customer churn in utility companies and provide practical insights for managers to effectively predict and manage customer churn. By understanding the factors driving customer churn and implementing targeted retention strategies, utility companies can reduce churn rates and improve customer satisfaction and loyalty.

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