Earticle

초록

영어

With the availability of big customer data and advances in machine learning techniques, the prediction of customer behavior at the session-level has attracted considerable attention from marketing practitioners and scholars. This study aims to predict customer purchase conversion at the session-level by employing customer profile, transaction, and clickstream data. For this purpose, we develop a multimodal deep learning fusion model with dynamic and static features (i.e., DS-fusion). Specifically, we base page views within focal visist and recency, frequency, monetary value, and clumpiness (RFMC) for dynamic and static features, respectively, to comprehensively capture customer characteristics for buying behaviors. Our model with deep learning architectures combines these features for conversion prediction. We validate the proposed model using real-world e-commerce data. The experimental results reveal that our model outperforms unimodal classifiers with each feature and the classical machine learning models with dynamic and static features, including random forest and logistic regression. In this regard, this study sheds light on the promise of the machine learning approach with the complementary method for different modalities in predicting customer behaviors.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Conceptual Background
2.1. Dynamic Platform Engagement: Clickstream Data
2.2. Customers’ Static Features
2.3. Online Purchase Prediction
2.4. Multimodal Fusion Approach
Ⅲ. Research Context and Data
Ⅳ. Methodology
4.1. Feature Extraction
4.2. Proposed Model
4.3. Evaluations
Ⅴ. Results
5.1. Comparison with Baseline Models
5.2. Model Performance
Ⅵ. Discussion
6.1. Discussion of Findings
6.2. Implications for Research and Practice
6.3. Limitations and Future Research
Ⅶ. Conclusion
Acknowledgements

저자

• Minsu Kim [ Data Scientist, LG UPlus Corporation, Korea ]
• Woosik Shin [ PhD Candidate, Graduate School of Information, Yonsei University, Korea ]
• SeongBeom Kim [ PhD Candidate, Graduate School of Information, Yonsei University, Korea ]
• Hee-Woong Kim [ Professor, Graduate School of Information, Yonsei University, Korea ] Corresponding Author

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