[ad_1]

Introduction

The retail industry is highly competitive, and businesses are constantly seeking ways to attract and retain customers in order to maximize profits. One approach that has gained popularity in recent years is predictive modeling for customer lifetime value. By analyzing a customer’s purchase history and using machine learning algorithms, retailers can predict the future value of each customer and tailor marketing strategies accordingly. This thesis aims to explore the effectiveness of predictive modeling for customer lifetime value in the retail industry.

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 Two: Literature Review
– Importance of customer lifetime value in the retail industry
– Previous studies on predictive modeling for customer lifetime value
– Machine learning algorithms commonly used in predictive modeling
– Strategies for increasing customer lifetime value
– Challenges and limitations of predictive modeling for customer lifetime value

Chapter Three: Research Methodology
– Research design and approach
– Data collection methods
– Data analysis techniques
– Sampling techniques
– Ethical considerations
– Validity and reliability of data
– Limitations of the research methodology
– Assumptions and constraints

Chapter Four: Discussion of Findings
– Analysis of customer lifetime value predictions
– Effectiveness of machine learning algorithms in predicting customer behavior
– Comparison of different predictive modeling techniques
– Recommendations for improving predictive modeling strategies
– Implications for the retail industry
– Future research directions

Chapter Five: Conclusion and Summary
– Summary of key findings
– Conclusions drawn from the research
– Contributions to existing knowledge
– Practical implications for retailers
– Recommendations for future research
– Conclusion

Thesis Overview

Predictive modeling for customer lifetime value in the retail industry using purchase history and machine learning has the potential to revolutionize the way retailers understand and engage with their customers. By analyzing historical purchase data and utilizing advanced machine learning algorithms, retailers can predict the future behavior of individual customers and segment them based on their value to the business. This thesis will explore the effectiveness of predictive modeling for customer lifetime value in the retail industry, examining the various challenges and opportunities that come with implementing such a strategy.

In Chapter One, the introduction will provide an overview of the research topic, including the background of the study, problem statement, objectives, limitations and scope of the study, significance of the study, structure of the thesis, and definition of terms.

Chapter Two will consist of a comprehensive review of the literature related to customer lifetime value, predictive modeling, machine learning algorithms, and strategies for increasing customer value in the retail industry.

Chapter Three will outline the research methodology, including details on the research design, data collection methods, analysis techniques, sampling procedures, ethical considerations, validity and reliability of data, limitations, assumptions, and constraints.

Chapter Four will present a detailed discussion of the findings, including an analysis of customer lifetime value predictions, the effectiveness of machine learning algorithms, comparisons of predictive modeling techniques, recommendations for improvement, implications for the retail industry, and future research directions.

Chapter Five will provide a conclusion and summary of the project, summarizing key findings, drawing conclusions, discussing contributions to existing knowledge, practical implications for retailers, recommendations for future research, and wrapping up the thesis.

[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.