Unsupervised Modelling of E-Customers’ Profiles: Multiple Correspondence Analysis with Hierarchical Clustering of Principal Components and Machine Learning Classifiers

Vijoleta Vrhovac, Marko Orošnjak (), Kristina Ristić, Nemanja Sremčev, Mitar Jocanović, Jelena Spajić and Nebojša Brkljač
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Vijoleta Vrhovac: Department of Industrial Engineering and Engineering Management, Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia
Marko Orošnjak: Department of Industrial Engineering and Engineering Management, Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia
Kristina Ristić: Department of Industrial Engineering and Engineering Management, Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia
Nemanja Sremčev: Department of Industrial Engineering and Engineering Management, Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia
Mitar Jocanović: Department of Industrial Engineering and Engineering Management, Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia
Jelena Spajić: Department of Industrial Engineering and Engineering Management, Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia
Nebojša Brkljač: Department of Industrial Engineering and Engineering Management, Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia

Mathematics, 2024, vol. 12, issue 23, 1-25

Abstract: The rapid growth of e-commerce has transformed customer behaviors, demanding deeper insights into how demographic factors shape online user preferences. This study performed a threefold analysis to understand the impact of these changes. Firstly, this study investigated how demographic factors (e.g., age, gender, education) influence e-customer preferences in Serbia. From a sample of n = 906 respondents, conditional dependencies between demographics and user preferences were tested. From a hypothetical framework of 24 tested hypotheses, this study successfully rejected 8/24 (with p < 0.05), suggesting a high association between demographics with purchase frequency and reasons for quitting the purchase. However, although the reported test statistics suggested an association, understanding how interactions between categories shape e-customer profiles was still required. Therefore, the second part of this study considers an MCA-HCPC (Multiple Correspondence Analysis with Hierarchical Clustering on Principal Components) to identify user profiles. The analysis revealed three main clusters: (1) young, female, unemployed e-customers driven mainly by customer reviews; (2) retirees and older adults with infrequent purchases, hesitant to buy without experiencing the product in person; and (3) employed, highly educated, male, middle-aged adults who prioritize fast and accurate delivery over price. In the third stage, the clusters are used as labels for Machine Learning (ML) classification tasks. Particularly, Gradient Boosting Machine (GBM), Decision Tree (DT), k-Nearest Neighbors (kNN), Gaussian Naïve Bayes (GNB), Random Forest (RF), and Support Vector Machine (SVM) were used. The results suggested that GBM, RF, and SVM had high classification performance in identifying user profiles. Lastly, after performing Permutation Feature Importance (PFI), the findings suggested that age, work status, education, and income are the main determinants of shaping e-customer profiles and developing marketing strategies.

Keywords: e-commerce; customer profiles; demographics; user preferences; multiple correspondence analysis; hierarchical clustering; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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