Asia Pacific Journal of Information Systems (APJIS)

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Past Issue

Date	September 2023
Vol. No.	Vol. 33 No. 3
DOI	https://doi.org/10.14329/apjis.2023.33.3.737
Page	737~767
Title	Predicting Session Conversion on E-commerce : A Deep Learning-based Multimodal Fusion Approach
Author	Minsu Kim, Woosik Shin, SeongBeom Kim, Hee-Woong Kim
Keyword	Purchase Conversion, Multimodal Fusion, Clickstream Data, Rfmc, Deep Learning
Abstract	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.

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