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Table of Contents

Chapter 1: Introduction
1.1 Background of the study
1.2 Problem statement
1.3 Research questions
1.4 Objectives of the study
1.5 Significance of the study
1.6 Scope of study
1.7 Limitations of study

Chapter 2: Literature Review
2.1 Definition of Customer Churn
2.2 Importance of Customer Churn Prediction
2.3 Previous Studies on Customer Churn Prediction
2.4 Predictive Analytics in Customer Churn Prediction

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Model Selection and Evaluation

Chapter 4: Discussion of Findings
4.1 Data Analysis Results
4.2 Model Performance Evaluation
4.3 Comparison with Previous Studies
4.4 Implications of Findings

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusion
5.3 Recommendations for Future Research

Brief Overview on Predictive Analytics for Customer Churn Prediction

Predictive analytics is a powerful tool that businesses use to forecast future outcomes based on historical data and statistical algorithms. This technique is especially valuable in customer churn prediction, which is the process of identifying customers who are likely to stop using a product or service.

Customer churn can have a significant impact on a company’s bottom line, as it represents a loss of revenue and potential negative word-of-mouth marketing. By using predictive analytics, businesses can proactively identify at-risk customers and take action to retain them before they churn.

In this final year project, the focus will be on developing a predictive analytics model for customer churn prediction. The study will involve conducting a thorough literature review on customer churn prediction and predictive analytics techniques. The research methodology will include data collection, data analysis, model selection, and evaluation.

The findings from the study will be discussed in chapter four, highlighting the effectiveness of the predictive analytics model in identifying at-risk customers. The conclusion and summary in chapter five will provide a comprehensive overview of the study’s results and recommendations for future research in this area.

Overall, predictive analytics for customer churn prediction is a valuable tool for businesses looking to improve customer retention and boost profitability. By leveraging data and advanced analytics techniques, companies can better understand customer behavior and take proactive measures to prevent churn.

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