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May 31, 2026
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Abstract

The intensification of digital retail competition has compelled shopping mall operators to adopt data-driven customer understanding as a strategic imperative. Although clustering algorithms have been applied to retail segmentation, rigorous comparative analyses that integrate hard-partition, fuzzy-membership, hierarchical, and density-based approaches on the same large-scale physical mall dataset remain absent in the literature. This study applies K-Means, Fuzzy C-Means (FCM), Hierarchical Clustering, and DBSCAN to the Shopping Mall Customer Segmentation dataset (n = 15,079) using Age, Annual Income, and Spending Score as segmentation variables. The Elbow Method identified five as the optimal cluster count, with a clear WSS inflection point. K-Means yielded five behaviorally distinct segments: Golden Spenders (mean age 71, spending score 72.4), High-Earning Pragmatists (mean income USD 159,000, spending score 49.6), Low-Budget Conservatives (spending score 27.9), Thrifty Seniors (spending score 24.1), and Middle-Aged Enthusiasts (lowest income yet highest spending score of 77.8). FCM with FPC = 0.681 identified membership-overlap zones revealing transient consumer profiles, while DBSCAN isolated outlier customers undetectable by centroid methods. Income alone is demonstrated to be an insufficient predictor of spending behavior. These findings support an operational CRM framework for personalized digital marketing, loyalty program differentiation, and targeted flash-sale deployment across five strategically distinct customer tiers.

Keywords

clustering algorithms customer segmentation DBSCAN digital marketing Fuzzy C-Means K-Means shopping mall

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How to Cite
Salsabila, Shasy Due Mahardika, Arnisyah, Rizal Bakri, & Farida Islamiah. (2026). Data-Driven Customer Segmentation for Digital Marketing Strategy in Shopping Malls Using Four Unsupervised Learning Algorithms. Online Journal of Management, Innovation, Economics, and Digital Studies, 1(1), 27–38. Retrieved from https://onmind.bisdig.feb.unm.ac.id/article/view/638
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