[ad_1]

Introduction

Customer churn prediction is a critical aspect of customer relationship management in e-commerce platforms. As the competition in the online marketplace intensifies, retaining customers and reducing churn rate has become paramount for the success of e-commerce businesses. By identifying customers who are at risk of churning, companies can implement targeted strategies to prevent customer defection and increase customer loyalty.

This thesis aims to explore the use of predictive analytics and machine learning techniques to predict customer churn in e-commerce platforms. The study will focus on developing a predictive model that can accurately forecast customer churn based on various demographic, behavioral, and transactional data. By understanding the factors that contribute to customer churn, e-commerce companies can proactively address customer dissatisfaction and improve retention rates.

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 e-commerce
2.2 Factors influencing customer churn
2.3 Predictive analytics and machine learning in customer churn prediction
2.4 Previous studies on customer churn prediction in e-commerce
2.5 Customer retention strategies in e-commerce
2.6 Data mining techniques for customer churn prediction
2.7 Customer segmentation and targeting
2.8 Customer lifetime value estimation
2.9 Technology trends in customer churn prediction
2.10 Ethical considerations in customer churn prediction

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection
3.3 Data preprocessing
3.4 Feature selection
3.5 Model development
3.6 Model evaluation
3.7 Performance metrics
3.8 Validation techniques

Chapter 4: Discussion of Findings
4.1 Descriptive analysis of the data
4.2 Feature importance in customer churn prediction
4.3 Model performance evaluation
4.4 Comparison of different machine learning models
4.5 Interpretation of results
4.6 Recommendations for e-commerce companies
4.7 Implications for future research

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Conclusion
5.3 Practical implications
5.4 Contributions to the field
5.5 Limitations of the study
5.6 Recommendations for future research

Thesis Overview

Customer churn prediction is a crucial aspect of customer relationship management in e-commerce platforms. With the increasing competition in the online marketplace, it is essential for companies to retain customers and reduce churn rates. This thesis focuses on developing a predictive model using predictive analytics and machine learning techniques to forecast customer churn in e-commerce platforms.

Chapter 1 provides an introduction to the topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter 2 presents a comprehensive literature review on customer churn in e-commerce, factors influencing churn, predictive analytics, machine learning, retention strategies, data mining techniques, customer segmentation, and ethical considerations.

Chapter 3 outlines the research methodology, including research design, data collection, preprocessing, feature selection, model development, evaluation, performance metrics, and validation techniques. Chapter 4 discusses the findings of the study, including descriptive analysis, feature importance, model performance, results interpretation, recommendations, and implications for future research.

Chapter 5 concludes the thesis by summarizing key findings, presenting conclusions, discussing practical implications, highlighting contributions to the field, addressing limitations, and providing recommendations for future research. Overall, this thesis aims to contribute to the understanding of customer churn prediction in e-commerce platforms and provide valuable insights for e-commerce companies to improve customer retention strategies.

[ad_2]

---

Purchase Detail

Download the complete project materials to this project with Abstract, Chapters 1 – 5, References and Appendix (Questionaire, Charts, etc), Click Here to place an order via whatsapp. Got question or enquiry; Click here to chat us up via Whatsapp.
You can also call 08111770269 or +2348059541956 to place an order or use the whatsapp button below to chat us up.
Bank details are stated below.

Bank: UBA
Account No: 1021412898
Account Name: Starnet Innovations Limited

The Blazingprojects Mobile App

Download and install the Blazingprojects Mobile App from Google Play to enjoy over 50,000 project topics and materials from 73 departments, completely offline (no internet needed) with monthly update to topics, click here to install.