The Online Customer: New Data Mining and Marketing Approaches

by Yinghui Yang

Table of Contents

Foreword

Preface

Acknowledgements

List of Tables

List of Figures


Part I. Introduction


Part II. Segmenting Customer Transactions Using a Pattern-Based Clustering Approach

2.1 Introduction

2.2 Pattern-Based Clustering of Web Transactions

2.2.1 Features of Web Transactions

2.2.2 Objective Function for Pattern-based Clustering

2.3 The Clustering Algorithm

2.4 Segmentation-Based Modeling

2.5 Experiments

2.5.1 Experiment I : Building Segmentation-Based Predictive Models for Online Retailers

2.5.2 Experiment II : Evaluating GHIC on User-Centric Web Browsing Sessions

2.5.2.1 Experiment setup

2.5.2.2 Results

2.6 Literature Review

2.6.1 Market Segmentation

2.6.2 Pattern-Based Clustering

2.6.3 Item / Itemset / Rule-Based Clustering

2.6.4 Segmentation-Based Modeling

2.6.5 Profiling and Signature Discovery

2.7 Conclusion: Contributions, Limitations and Future Work


Part III. Free Shipping Promotions and Internet Shopping Behavior: Theory and Evidence

3.1 Introduction

3.2 The Model

3.2.1 Purchase Quantity and Cost for Different Shipping Schedules

3.2.2 Relationship between Shipping Threshold (T) and Price (p)

3.2.3 Comparisons

3.2.4 Hypotheses

3.3 Empirical Analysis and Hypothesis Testing

3.3.1 Data Description

3.3.2 Hypothesis Testing: Average Quantity and Quantity Variance

3.3.3 Hypothesis Testing: Threshold Level and Purchase Quantity

3.3.4 Hypothesis Testing: Threshold and Price Dispersion

3.4 Literature Review

3.5 Conclusion: Contributions, Limitations and Future Work


Part IV. Conclusion

4.1 Contributions

4.2 Limitations and Future Work


Appendix 1. Proof

Appendix 2. A Generalized Mixture Regression Model (GLIMMIX)

Appendix 3. Definition for Contrast Sets

Appendix 4. Variables in Experiment I

Bibliography

Index


 

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