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Thesis Overview:

Title: Customer churn prediction in the utilities industry using consumption data and machine learning

Introduction:

The utilities industry faces the challenge of customer churn, where customers switch service providers due to various reasons such as pricing, customer service, or better offerings from competitors. Customer churn prediction is crucial for utilities companies as it allows them to identify at-risk customers and implement targeted retention strategies. This thesis aims to explore the use of consumption data and machine learning techniques for predicting customer churn in the utilities industry.

Chapter 1:

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 Introduction to customer churn prediction
2.2 Previous studies on customer churn prediction in the utilities industry
2.3 Machine learning techniques for customer churn prediction
2.4 Consumption data in customer churn prediction
2.5 Factors influencing customer churn in the utilities industry
2.6 Retention strategies for reducing customer churn
2.7 Evaluation metrics for customer churn prediction models
2.8 Data preprocessing techniques for consumption data
2.9 Feature selection and engineering for customer churn prediction
2.10 Challenges in customer churn prediction in the utilities industry

Chapter 3: Research Methodology

3.1 Introduction
3.2 Data collection
3.3 Data preprocessing
3.4 Feature selection and engineering
3.5 Model selection
3.6 Model training and evaluation
3.7 Hyperparameter tuning
3.8 Cross-validation
3.9 Performance evaluation metrics

Chapter 4: Discussion of Findings

4.1 Descriptive analysis of the dataset
4.2 Performance comparison of different machine learning models
4.3 Feature importance analysis
4.4 Retention strategies based on model insights
4.5 Limitations of the study
4.6 Future research directions

Chapter 5: Conclusion and Summary

5.1 Summary of key findings
5.2 Contributions of the study
5.3 Implications for utilities companies
5.4 Recommendations for future research
5.5 Conclusion

Overall, this thesis aims to provide insights into customer churn prediction in the utilities industry using consumption data and machine learning techniques. By understanding the factors influencing customer churn and implementing effective retention strategies, utilities companies can improve customer satisfaction and reduce revenue loss.

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