Strategic Customer Segmentation Using Unsupervised Learning and PCA: A Data-Driven Approach to Personalized Marketing التقسيم الاستراتيجي للعملاء باستخدام التعلم غير الخاضع للإشراف وتحليل المكونات الرئيسية (PCA): نهج قائم على البيانات للتسويق المُخصص | ||
| منارة الاسكندرية للعلوم التجارية | ||
| Volume 1, Issue 2, July 2025, Pages 211-182 PDF (1.24 M) | ||
| Document Type: المقالة الأصلية | ||
| DOI: 10.21608/mauta.2025.410326.1007 | ||
| Authors | ||
| رامي كمال* 1; نوال محمد2 | ||
| 1اكاديمية اسكندرية للادارة والمحاسبة | ||
| 2اكاديميه الاسكندرية للادارة والمحاسبة | ||
| Abstract | ||
| In the era of data-driven decision-making, businesses must leverage advanced analytical approaches to better understand customer behavior and optimize marketing strategies. This study integrates unsupervised learning with Principal Component Analysis (PCA) to enhance customer segmentation. Using a real-world customer dataset of 2,240 records, the analysis underwent rigorous preprocessing and feature engineering to ensure data quality and representativeness. PCA reduced the feature space from 23 variables to 3 principal components, preserving 82% of the variance while simplifying complexity. Agglomerative Hierarchical Clustering was applied, with the Elbow Method confirming four optimal clusters. The results revealed distinct groups: (i) high-income, high-spending customers (35%), (ii) middle-income families with moderate engagement (28%), (iii) low-income, price-sensitive customers (25%), and (iv) disengaged, low-spending customers (12%). Clusters were further validated using Silhouette Scores, which improved by 18% compared to demographic-only segmentation. Compared with traditional methods such as K-Means and DBSCAN, the PCA-enhanced approach demonstrated superior interpretability and stability. Key findings highlight that high-value customers are less responsive to promotions despite their spending power, while price-sensitive groups show higher responsiveness to targeted deals. These insights suggest that personalized marketing, tailored to the behavioral and demographic profiles uncovered, can significantly improve customer engagement and resource allocation. | ||
| Keywords | ||
| Customer Segmentation; Unsupervised Learning; Agglomerative Clustering; Principal Component Analysis (PCA); Data-Driven Marketing | ||
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