[ad_1]

Introduction

In the era of subscription-based services, retaining customers is crucial for the success of any business. Customer churn, which refers to the phenomenon of customers discontinuing their subscriptions, poses a significant challenge for companies offering such services. Predicting customer churn can help businesses take proactive measures to reduce customer attrition and increase customer retention rates. This thesis aims to explore the factors that influence customer churn in subscription services and develop a predictive model to anticipate and prevent customer churn.

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 subscription services
2.2 Customer churn in subscription services
2.3 Factors influencing customer churn
2.4 Predictive analytics in customer churn prediction
2.5 Machine learning algorithms for churn prediction
2.6 Customer retention strategies
2.7 Case studies on customer churn prediction
2.8 Customer lifetime value
2.9 Customer segmentation
2.10 Technology and customer churn prediction tools

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Variables selection
3.4 Data analysis techniques
3.5 Model development
3.6 Model evaluation
3.7 Data preprocessing
3.8 Sampling techniques

Chapter 4: Discussion of Findings
4.1 Data analysis results
4.2 Factors influencing customer churn
4.3 Predictive model performance
4.4 Comparison with existing models
4.5 Implications for subscription businesses
4.6 Recommendations for reducing customer churn
4.7 Practical applications of the predictive model
4.8 Future research directions

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

Thesis Overview

The subscription-based business model has gained popularity in recent years, with companies offering a wide range of services from streaming platforms to software as a service (SaaS). However, retaining customers in this competitive market is a constant challenge, as customers can easily switch to alternative providers or cancel their subscriptions altogether. Customer churn, the rate at which customers discontinue their subscriptions, is a critical metric for subscription businesses, as it directly impacts revenue and profitability.

This thesis focuses on predicting customer churn in subscription services, with the aim of helping businesses identify at-risk customers and implement strategies to retain them. The study will delve into the factors that influence customer churn, such as pricing, customer satisfaction, service quality, and competitive offerings. By analyzing historical subscriber data and applying predictive analytics techniques, a model will be developed to forecast customer churn and assist businesses in proactive churn prevention efforts.

The literature review will explore the existing research on customer churn prediction in subscription services, including the use of machine learning algorithms, customer segmentation techniques, and customer lifetime value analysis. Case studies and best practices in customer retention strategies will also be examined to provide a comprehensive understanding of the topic.

The research methodology section will outline the data collection methods, variables selection, and data analysis techniques employed in the study. The process of developing and evaluating the predictive model will be described in detail, along with the model’s performance metrics and implications for subscription businesses.

The discussion of findings chapter will present the results of the data analysis, identifying the key factors influencing customer churn and the predictive model’s effectiveness in churn prediction. Recommendations for reducing customer churn and practical applications of the model will be discussed, along with implications for future research in the field.

In conclusion, this thesis will provide valuable insights into customer churn prediction in subscription services, offering a framework for businesses to anticipate and mitigate customer churn. By understanding the factors driving customer attrition and leveraging advanced analytics tools, subscription businesses can improve their customer retention strategies and enhance overall business performance.

[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.