KaydedilenlerDerginizi ekleyin

Dergiler / 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
Sayfa
—
Yayın Tarihi
2026-10-13
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Ultimately, this research bridges data-driven consumer insights with distance-based segmentation, highlighting the role of cluster analytics in enhancing business performance.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Business analytics uncovers complex consumer behaviors by extracting structured models from customer data.
📊
Temel Bulgular & Veri: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.
💡
Sonuç & Katkı:Ultimately, this research bridges data-driven consumer insights with distance-based segmentation, highlighting the role of cluster analytics in enhancing business performance.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler: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.

🔖

Bu Makaleye Atıf Yap

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

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (43)

  1. Afzal, A., Khan, L., Hussain, M.Z., Hasan, M.Z., Mustafa, M. et al., Customer segmentation using hierarchical clustering, Proceedings of the IEEE 9th International Conference for Convergence in Technology, (2024), 1–6.
  2. Agresti, A., Categorical Data Analysis, John Wiley and Sons, 2013.
  3. Almeida, J.S., Prieto, C., Marques de S´a, J.P., Cluster analysis and classification, Independent Component Analysis and Blind Signal Separation, Springer, 4666(2007), 19–28.
  4. Aslantaş, G., Gençgül, M., Rumelli, M., Özsaraç, M., Bakırlı, G., Customer segmentation using K-Means clustering algorithm and RFM model, Proceedings of DEUFMD, 25(74)(2023), 491–503.
  5. Ayodele, E., Sodeinde, V., Customer segmentation using the K-Means clustering algorithm, Ilaro Journal of Science and Technology, 3(2024), 76–81.
  6. Birn, R.J., Lee, S., Evolution of Marketing Theory and Practice: Bridging the Gap, Copyright© 2025 Eurasian Research Institute, 2025.
  7. Bishop, C. M., Pattern recognition and Machine Learning, Springer, 2006.
  8. Blashfield, R.K., Mixture model tests of cluster analysis: accuracy of four agglomerative hierarchical methods, Psychological Bulletin, 83(3)(1976), 377–388.
  9. Cohen, J., Statistical Power Analysis for the Behavioral Sciences, Routledge, 2013.
  10. Deng, S., Ye, Y., Guan, S., Li, M., KNN-maximum dispersion balanced K-means algorithm, International Conference on Machine Learning and Embedded Systems, 14295(2026), 1429502.
  11. Dolnicar, S., Grun, B., Leisch, F., Schmidt, K., Required sample sizes for data-driven market segmentation analyses in tourism, Journal of Travel Research, 53(3)(2014), 296–306.
  12. Eszerg´ar-Kiss, D., Caesar, B., Definition of user groups applying Ward’s method, Transportation Research Procedia, 22(2017), 25–34.
  13. Everitt, B.S., Landau, S., Leese, M., Stahl, D., Cluster Analysis, Wiley, 2011.
  14. Fr¨anti, P., Sieranoja, S., K-means properties on six clustering benchmark datasets, Applied Intelligence, 48(12)(2018), 4743–4759.
  15. Grewal, R., Gupta, S., Hamilton, R., Marketing insights from multimedia data: Text, image, audio, and video, Journal of Marketing Research, 58(6)(2021), 1025–1033.
  16. Hands, S., Everitt, B., A Monte Carlo study of the recovery of cluster structure in binary data by hierarchical clustering techniques, Multivariate Behavioral Research, 22(2)(1987), 235–243.
  17. Hair Jr, J.F., Black, W.C., Babin, B.J., Multivariate Data Analysis, Cengage, 2019.
  18. Harrison, T., Ansell, J., Customer retention in the insurance industry: Using survival analysis to predict cross-selling opportunities, Journal of Financial Services Marketing, 6(3)(2002), 229–239.
  19. Hennig, C., What are the true clusters?, Pattern Recognition Letters, 64(2015), 53–62.
  20. Hubert, L., Arabie, P., Comparing partitions, Journal of Classification, 2(1)(1985), 193–218.
  21. Jain, A.K., Data clustering: 50 years beyond K-means, Pattern Recognition Letters, 31(8)(2010), 651–666.
  22. Jolliffe, I.T., Principal Component Analysis, Springer, 2002.
  23. Kaufman, L., Rousseeuw, P.J., Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, 2008.
  24. Khalili-Damghani, K., Sadi-Nezhad, S., A decision support system for fuzzy multi-objective multi-period sustainable project selection, Computers and Industrial Engineering, 64(4),(2013), 1045–1060.
  25. Kim, E., Kim, W., Lee, Y., Combination of multiple classifiers for the customer’s purchase behavior prediction, Decision Support Systems, 34(2)(2003), 167–175.
  26. Kotler, P., Keller, K.L., Chernev, A., Marketing Management, Pearson, 2021.
  27. Maryani, I., Riana, D., Astuti, R.D., Ishaq, A., Sutrisno et al., Customer Segmentation based on RFM model and Clustering Techniques with KMeans Algorithm, Third International Conference on Informatics and Computing, (2018), 1–6.
  28. Meng, C., Lv, Y., You, L., Yue, Y., Intrusion detection method based on improved K-means algorithm, Journal of Physics: Conference Series, 1302(2019), 032011.
  29. Milligan, G.W., Cooper, M.C., A study of the comparability of external criteria for hierarchical cluster analysis, Multivariate Behavioral Research, 21(4)(1986), 441–458.
  30. Murtagh, F., Legendre, P., Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion?, Journal of Classification, 31(3)(2014), 274–295.
  31. Özdemir, Y.E., Bayraklı, S., A case study on building a cross-selling model through machine learning in the insurance industry, Avrupa Bilim ve Teknoloji Dergisi, 35(2022), 364–372.
  32. Patel, C., A Survey of Data-Driven Customer Segmentation Methods for Targeted Marketing Campaigns, ESP Journal of Engineering and Technology Advancements, 3(3)(2023), 154–162.
  33. Punj, G., Stewart, D. W., Cluster analysis in marketing research: Review and suggestions for application, Journal of Marketing Research, 20(2)(1983), 134–148.
  34. Roberts, M.L., Liu, R.R., Hazard, K., Strategy, technology and organisational alignment: Key components of CRM success, Database Marketing and Customer Strategy Management, 12(4)(2005), 315–326.
  35. Rousseeuw, P.J., Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics, 20(1987), 53–65.
  36. Schubert, E., Sander, J., Ester, M., Kriegel, H.P., Xu, X., DBSCAN revisited, revisited: why and how you should (still) use DBSCAN, ACM Transactions on Database Systems, 42(3)(2017), 1–21.
  37. Sharma, S., Desai, N., Data-driven customer segmentation using clustering methods for business success, 4th IEEE Global Conference for Advancement in Technology, (2023), 1–7.
  38. Tabianan, K., Velu, S., Ravi, V., K-means clustering approach for intelligent customer segmentation using customer purchase behavior data, Sustainability, 14(12)(2022), 7243.
  39. Tibshirani, R., Walther, G., Hastie, T., Estimating the number of clusters in a data set via the gap statistic, Journal of the Royal Statistical Society: Series B, 63(2)(2001), 411–423.
  40. Ward Jr, J.H., Hierarchical grouping to optimize an objective function, Journal of the American Statistical Association, 58(301)(1963), 236–244.
  41. Wu, C.H., Kao, S.C., Su, Y.Y., Wu, C.C., Targeting customers via discovery knowledge for the insurance industry, Expert Systems with Applications, 29(2)(2005), 291–299.
  42. Yim, O., Ramdeen, K.T., Hierarchical cluster analysis: comparison of three linkage measures and application to psychological data, The Quantitative Methods for Psychology, 11(1)(2015), 8–21.
  43. Birn, R.J., Lee, S., Evolution of Marketing Theory and Practice: Bridging the Gap, Copyright© 2025 Eurasian Research Institute, 2025.