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Journals / Turkish Journal of Mathematics and Computer Science / 2026 / Volume: 18 Issue: 3

The Examining Consumer Behavior Patterns with Distance-Based Clustering Models in Business Analytics

Anıl Burcu Özyurt Serim(HALİÇ ÜNİVERSİTESİ, İŞLETME FAKÜLTESİ)0000-0001-9868-2676
Pages
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Publication Date
2026-10-13
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Empirical results indicate that a five cluster solution provided the most meaningful partition, revealing distinct consumer clusters such as high-income conservative spenders and young groups of customers with elevated spending tendencies.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Business analytics uncovers complex consumer behaviors by extracting structured models from customer data.
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Key Findings & Data:To validate this configuration, oneway ANOVA (η2) demonstrated that cluster membership explains over 67% of the segmentation variance while Principal Component Analysis (PCA) confirmed its structural stability by retaining 78% of the total variance.
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Conclusion & Impact:Empirical results indicate that a five cluster solution provided the most meaningful partition, revealing distinct consumer clusters such as high-income conservative spenders and young groups of customers with elevated spending tendencies.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Unsupervised learning · distance-based clustering · K-means algorithm

Abstract

Business analytics uncovers complex consumer behaviors by extracting structured models from customer data. Applying K-means and hierarchical clustering to a retail dataset containing demographic and spending variables, this study identifies latent customer segments. Although the Elbow method favored fewer clusters, the Gap Statistic and Silhouette index established an optimal five-cluster structure. To validate this configuration, oneway ANOVA (η2) demonstrated that cluster membership explains over 67% of the segmentation variance while Principal Component Analysis (PCA) confirmed its structural stability by retaining 78% of the total variance. Empirical results indicate that a five cluster solution provided the most meaningful partition, revealing distinct consumer clusters such as high-income conservative spenders and young groups of customers with elevated spending tendencies. Methodologically, the convergence of K-means and hierarchical clustering cross-validated the structural robustness of the segments, supported by high bootstrap stability (ARI = 0.817). Ultimately, this research bridges data-driven consumer insights with distance-based segmentation, highlighting the role of cluster analytics in enhancing business performance.

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Cite This Article

Serim, A. B. Ö. (2026). The Examining Consumer Behavior Patterns with Distance-Based Clustering Models in Business Analytics. Turkish Journal of Mathematics and Computer Science, Volume: 18 Issue: 3. https://doi.org/10.47000/tjmcs.1820010

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