Application of Clustering and Classification Algorithms in Analyzing Customer Behavior in Data-Driven Marketing: A Case Study of Amazon Customers
Abas Asadi
1
(
Assistant Professor, Department of Varamin-Pishva Branch, Islamic Azad University, Varamin, Iran
)
Firouzeh Razavi
2
(
Assistant Professor, Department of Information Technology, Raja University, Qazvin, Iran
)
Reyhane Farshbaf Sabahi
3
(
Science and Research Branch, Islamic Azad University, Tehran, Iran
)
Keywords: Data-Driven Marketing, Machine Learning, Customer Clustering, K-Means Clustering, Customer Classification,
Abstract :
In data-driven marketing, customer behavior analysis plays a crucial role in developing targeted marketing strategies aimed at increasing return on investment, enhancing profitability, and gaining a larger market share. In this study, four clustering methods- including K-means, density-based clustering, principal component analysis, and hierarchical clustering- as well as four classification methods- including Support Vector Machine, XGBoost, Random Forest, and Gradient Boosting- are examined for customer behavior analysis. The data for this study was extracted from the "Amazon Customer Behavior Survey" dataset, which includes 23 features from 602 customers. Initially, the data was preprocessed, and then, using clustering methods, customers were divided into different groups. The performance of these methods was evaluated based on criteria such as the silhouette index, and ultimately, appropriate marketing strategies for each cluster were proposed. Additionally, to examine the possibility of predicting customer membership in the extracted clusters, the aforementioned classification models were implemented and compared. The results indicate that the K-means method performed the best in clustering, while the XGBoost model performed the best in classification. The innovation of this research lies in combining clustering and classification methods to provide targeted marketing strategies and comprehensively comparing these methods on real customer data. This study demonstrates that combining clustering and classification methods can help businesses better understand customer behavior and make more optimal marketing decisions.
Abu'i Mehrizi, Abbas. (2019). " Analyzing customer purchasing behavior in a retail store using data mining. International Conference on Industrial Engineering " International Conference on Industrial Engineering.
Bahrini-Zad, M., Asar, M., & Esmailpour, M. (2022). "Segmenting online retail customers based on demographic characteristics and customer experience ", New Marketing Research, Vol. 44, Issue B, pp. 69-88.
Dezbandi, Afsaneh. (2020). "The Importance of Customer Clustering on Brand Value Creation Based on Aaker’s Brand Equity Model: A Case Study of Shahroud Dairy Products - Miami Cheese." 18th International Conference on Management.
Dadres, M., Rahimi Nik, A., & Nematizadeh, S. (2023). " A study of the financial marketing model of the National Bank of Iran with emphasis on customer grouping, financial engineering, and securities management. " Financial Engineering and Securities Management, Winter 2023, Issue 57, A, pp. 65-79.
Sharifi Isfahani, Hamid. (2023). " Providing an approach based on customer purchase history and product recommendations to customers: A case study of Digikala customers" Journal of Industrial Management Perspectives.
Safabakhsh, M., & Asayesh, F. (2022). "Segmentation of bank customers based on customer lifetime value and their profitability ability (case study: customers of a private bank)" Islamic Financial Studies and Banking, 8th Year, Issue 19, pp. 53-80.
Mohaghegh, A., Habibnejad Behtash, N., Sheikhzadeh Sani, H., & Karimi, A. (2022). " Explaining the financial marketing model with emphasis on customer segmentation of Tejarat Bank Iran."Proceedings of the 6th International Conference on Interdisciplinary Studies in Management and Engineering, International, pp. 791-811.
Taghavi-Fard, Mohammad Taqi. (2022). " Customer clustering in the field of electronic banking using electronic transactions and demographic information (case study: Refah Bank). Journal of Management, Advertising, and Sales.
Alamsyah, P. E. P., Prasetyo, S., Sunyoto, S., Bintari, S. H., Saputro, D. D., Rohman, S., & Pratama, R. N. (2022). " Customer Segmentation Using the Integration of the Recency Frequency Monetary Model and the K-Means Cluster Algorithm", Scientific Journal of Informatics, Vol.
Alnuaimi, A. F. A. H., & Albaldawi, T. H. K. (2024). "An overview of machine learning classification techniques," BIO Web of Conferences, 97, 00133.
Ayodele, E., & Sodeinde, V. (2024). "Customer segmentation using the K-means clustering algorithm," Ilaro Journal of Science and Technology (IJST), 4, 1-6.
Budiyono, M., Tho'in, M., Muliasari, D., & Putri, S. A. R. (2021). " An Analysis of Customer Satisfaction Levels in Islamic Banks Based on Marketing Mix as a Measurement Tool", Annals of R.S.C.B., Vol. 25, Issue 1, pp. 2004-2012. ISSN: 1583-6258.
Guerola-Navarro, V., Gil-Gomez, H., Oltra-Badenes, R., & Sendra-García, J. (2021). "Customer relationship management and its impact on innovation: A literature review," Journal of Business Research, 129, 83-87.
Guerola-Navarro, V., Gil-Gomez, H., Oltra-Badenes, R., & Soto-Acosta, P. (2024). "Customer relationship management and its impact on entrepreneurial marketing: A literature review," International Entrepreneurship and Management Journal, 20, 507-547.
Hariyanto, H. T., & Trisunarno, L. (2020). " Putra, D. P., Suprihartini, L., & Kurniawan, R. (2021). " Celebrity Endorser, Online Customer Review, Online Customer Rating on Purchasing Decision with Trust as an Intervening Variable on Tokopedia Marketplace", Jurnal Bahtera Inovasi, Vol. 5, No. 1, pp.ISSN 2747-0067.", JURNAL TEKNIK ITS, Vol. 9, No. 2, pp.
Kamisa, N., Devita, A., & Novita, D. (2022). " The influence of online customer reviews and online customer ratings on customer trust ", Journal of Economic and Business Research, Vol. 2, No. 1, pp. 21-29.
Li, Y., Chu, X., Tian, D., Feng, J., & Mu, W. (2021). "Customer segmentation using K-means clustering and the adaptive particle swarm optimization algorithm," Applied Soft Computing, 113(B), 107924.
Li, Y., Meng, C., Tian, J., Fang, Z., & Cao, H. (2024). "Data-Driven Customer Online Shopping Behavior Analysis and Personalized Marketing Strategy", Journal of Organizational and End User Computing, Vol. 36, Issue 1, pp.
Lone, H., & Warale, P. (2022). " Cluster Analysis: Application of K-Means and Agglomerative Clustering for Customer Segmentation", Journal of Positive School Psychology, Vol. 6, No. 5, pp. 7798–7804.
Rachman, F. P., Santoso, H., & Djajadi, A. (2024). "Machine learning mini-batch k-means and business intelligence utilization for credit card customer segmentation," Proceedings of the International Conference on Data Science and Business Intelligence, 1-6.
Wu, S., Yau, W.-C., Ong, T.-S., & Chong, S.-C. (2021). "Integrated churn prediction and customer segmentation framework for telco business," IEEE Access, 9, 113456-113467.
Xiao, Z., Zhao, J., Li, Y., Shindou, R., & Song, Z.-D. (2024). "Spin space groups: Full classification and applications," Journal of Quantum Materials, 1(1), 1-6.
Xiahou, X., & Harada, Y. (2022). "B2C E-Commerce Customer Churn Prediction Based on K-Means and SVM", J. Theor. Appl. Electron. Commer. Res., 17, pp. 458–475.
Tabianan, K., Velu, S., & Ravi, V. (2024). "K-means clustering approach for intelligent customer segmentation using customer purchase behavior data," Sustainability, 16(1), 1-10.
Putra, D. P., Suprihartini, L., & Kurniawan, R. (2021). " Celebrity Endorser, Online Customer Review, Online Customer Rating on Purchasing Decision with Trust as an Intervening Variable on Tokopedia Marketplace", Jurnal Bahtera Inovasi, Vol. 5, No. 1, pp.ISSN 2747-0067.