The Online Customer: New Data Mining and Marketing Approaches

by Yinghui Yang

Table of Contents




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 Experiment setup 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




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