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Introduction

Subscription box services have gained popularity in recent years, offering customers a convenient way to receive curated products regularly. However, one major challenge faced by these companies is customer churn, where subscribers cancel their subscriptions. Predicting customer churn is crucial for subscription box businesses to retain customers and maintain profitability. In this thesis, we aim to explore the use of delivery data and machine learning algorithms to predict customer churn for subscription box services.

1.2 Background of the Study

1.3 Problem Statement

1.4 Objective of the Study

1.5 Limitation of the Study

1.6 Scope of the Study

1.7 Significance of the Study

1.8 Structure of the Thesis

1.9 Definition of Terms

Chapter Two: Literature Review

2.1 Subscription Box Services
2.2 Customer Churn
2.3 Predictive Analytics
2.4 Machine Learning Algorithms
2.5 Delivery Data Analysis
2.6 Customer Segmentation
2.7 Customer Lifetime Value
2.8 Customer Retention Strategies
2.9 Previous Studies on Customer Churn Prediction
2.10 Gap Analysis

Chapter Three: Research Methodology

3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Engineering
3.5 Model Selection
3.6 Evaluation Metrics
3.7 Cross-Validation
3.8 Implementation Plan

Chapter Four: Discussion of Findings

4.1 Descriptive Analysis of Delivery Data
4.2 Customer Segmentation Results
4.3 Churn Prediction Model Performance
4.4 Feature Importance Analysis
4.5 Interpretation of Results
4.6 Managerial Implications
4.7 Recommendations for Subscription Box Companies
4.8 Future Research Directions

Chapter Five: Conclusion and Summary

In conclusion, this thesis aims to provide subscription box businesses with a predictive model for customer churn using delivery data and machine learning techniques. By understanding the factors that contribute to churn and implementing targeted retention strategies, companies can improve customer retention and increase profitability. Additionally, this study contributes to the existing literature on customer churn prediction and provides insights for future research in this area.

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