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Introduction:

In today’s competitive market, customer churn is a significant challenge for businesses across various industries. Customer churn, also known as customer attrition, is the loss of customers or clients. It is essential for companies to be able to predict and prevent customer churn to maintain sustainable growth and profitability. Artificial Intelligence (AI) powered predictive analytics has emerged as a powerful tool for businesses to analyze patterns, trends, and customer behavior to predict and prevent customer churn.

This thesis focuses on the utilization of AI-powered predictive analytics for customer churn prevention. The study aims to explore how companies can leverage AI technologies to analyze large volumes of customer data and predict potential churners. By anticipating customer behavior and implementing targeted retention strategies, businesses can reduce customer churn rates and improve overall customer satisfaction and profitability.

Table of Contents:

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
2.2 AI and Predictive Analytics
2.3 Customer Behavior Analysis
2.4 Customer Retention Strategies
2.5 Applications of AI in Customer Churn Prevention
2.6 Case Studies in Customer Churn Prevention
2.7 Challenges in Customer Churn Prediction
2.8 Success Factors in Customer Retention
2.9 The Role of Data in Customer Churn Prevention
2.10 Ethical Considerations in AI-powered Analytics

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Sampling Techniques
3.5 Variable Measurement
3.6 Model Development
3.7 Model Validation
3.8 Ethical Considerations
3.9 Limitations of the Methodology

Chapter 4: Discussion of Findings
4.1 Data Analysis Results
4.2 Predictive Model Performance
4.3 Identification of Churn Risk Factors
4.4 Comparison of Retention Strategies
4.5 Implications for Business
4.6 Recommendations for Future Research
4.7 Managerial Implications
4.8 Theoretical Contributions

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Managerial Implications
5.4 Recommendations for Businesses
5.5 Future Research Directions

Overall, this thesis aims to provide insights into the use of AI-powered predictive analytics for customer churn prevention and offer practical recommendations for businesses to enhance customer retention strategies and reduce churn rates. By leveraging advanced technologies and data analytics, companies can gain a competitive edge in retaining valuable customers and sustaining long-term growth.

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