The Evaluation Clustering Algorithm of Iran's Online Shopping Consumer Market

Khorasani, Khatereh; Zaranezhad, Mansour; Amirnezhad, Ghanbar; Kangarani Farahani, Ali

doi:10.48308/jep.5.2.115

The Evaluation Clustering Algorithm of Iran's Online Shopping Consumer Market

Document Type : Original Article

Authors

¹ Ph.D Candidate, Department of Management, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran

² Professor, Department of Management, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

³ Associate Professor, Department of Management, South Tehran Branch, Islamic Azad University, Tehran, Iran.

⁴ Assistant Professor, Department of Management, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran

10.48308/jep.5.2.115

Abstract

With the fast growth of e-commerce and the emerging new retail trend—online and offline integration—it is important to recognize the target market and satisfy customers with different needs by analyzing their online search behaviors. Accordingly, in this study, several internet companies in Iran were investigated. The companies were divided into 5 categories based on their product type: food, cosmetics and luxury goods, industrial goods and their accessories, sanitary goods, detergents, and clothing. Then the trading data of the companies in a certain period are analyzed. The data of this research includes customer transaction records from 2018 to 2019, after removing incomplete and missing data, this number has reached 349 records or the company. According to the inquiry from the Ministry of Mining Industry and Trade, there are 51,307 internet shopping and service sites and 36,200,000 internet buyers in the country.Clustering provides a good understanding of customer needs and helps identify potential customers. Dividing customers into sectors also increases the company's income. It is believed that retaining customers is more important than finding new customers. For example, companies can employ marketing strategies specific to a particular segment to retain customers. This study first performed RFM analysis on transaction data and then applied clustering using k-means. Then the results obtained from the methods were compared with each other.

Keywords

References

Agag, G., Shehawy, Y., Almoraish, A., Eid, R., Lababdi, H., Labben, T., & Abdo, S. (2024). Understanding the relationship between marketing analytics, customer agility, and customer satisfaction: A longitudinal perspective. Journal of Retailing and Consumer Services, (77), 21-34.

Amin, A., Adnan, A., & Anwar, S. (2023). An adaptive learning approach for customer churn prediction in the telecommunication industry using evolutionary computation and Naïve Bayes. Journal of Applied Soft Computing, (137), 691-714.

Bueno, I., Velasco, J., Carrasco, R., & Herrera-Viedma, E. (2022). A geospatial model of RFM analysis: An application to tourism in the Iberian Peninsula. Journal of Procedia Computer Science, (214), 1047-1062.

Chen, Y., Liu, L., Zheng, D., & Li, B. (2023). Estimating travellers’ value when purchasing auxiliary services in the airline industry based on the RFM model. Journal of Retailing and Consumer Services, (74), 957-974.\

Cheung, C., Lee, W., Wang, M., & To, S. (2023). A multi-perspective knowledge-based system for customer service management. Journal of Expert Systems with Applications, 24(4), 457-470.

Cong, L., Ding, H., Xie, N., & Wie, X. (2024). Space delay-tolerant network routing algorithm based on node clustering and social attributes. Journal of Ad Hoc Networks, (155), 649-667.

Ding, K., Gong, X., Huang, T., & Choo, W. (2024). Recommend or not: A comparative analysis of customer reviews to uncover factors influencing explicit online recommendation behavior in peer-to-peer accommodation. Journal of European Research on Management and Business Economics, 30(1), 105-12.

Gustriansyah, R., Ermatita, E., & Palupi Rini, D. (2022). An approach for sales forecasting. Journal of Expert Systems with Applications, (207), 161-179.

Hashemi, E., Gholian-Jouybari, F., & Hajiaghaei-Keshteli, M. (2023). A fuzzy C-means algorithm for optimizing data clustering. Journal of Expert Systems with Applications, (227), 39-55. [In Persian]

Heriqbaldi, U., Jayadi, A., Erlando, A., Samudro, B., Widodo, W., & Esquivias, M. (2023). Survey data on organizational resources and capabilities, export marketing strategy, export competitiveness, and firm performance in exporting firms in Indonesia. Journal of Data in Brief, (48), 443-460.

Hosen, A., Hoshen Moz, S., Kabir, S., Galib, S., & Adnan, N. (2023). Enhancing Thyroid Patient Dietary Management with an Optimized Recommender System based on PSO and K-means. Journal of Procedia Computer Science, (230), 688-697.

Ilbeigipour, S., Albadvi, A., & Noughabi, E. (2022). Cluster-based analysis of COVID-19 cases using self-organizing map neural network and K-means methods to improve medical decision-making. Journal of Informatics in Medicine Unlocked, (32), 1-19.

Ivens, B., Kasper-Brauer, K., Leischnig, A., & Thornton, S. (2024). Implementing customer relationship management successfully: A configurational perspective. Journal of Technological Forecasting and Social Change, (199), 41-58.

Jendoubi, S., Baelde, A., & Tran, T. (2023). SSFuzzyART: A Semi-Supervised Fuzzy ART through seeding initialization and a clustered data generation algorithm to deeply study clustering solutions. Journal of Array, (19), 611-627.

Liu, S., & Yu, Z. (2023). Modeling and efficiency analysis of blockchain agriculture products E-commerce cold chain traceability system based on Petri net. Journal of Heliyon, 9(11), 217-231.Laudien, S., Reuter, U., Garcia, F., & Botella-Carrubi, D. (2024). Digital advancement and its effect on business model design: Qualitative-empirical insights. Journal of Technological Forecasting and Social Change, (200), 278-295.

Ma, F., Wang, C., Huang, J., Zhong, Q., & Zhang, T. (2024). Key Grids based Batch-Incremental CLIQUE Clustering Algorithm Considering Cluster Structure Changes. Journal of Information Sciences, (128), 1-17.

Nazari Ghazvini, S., Vakil Alroaia, Y., & Baharon, R. (2023). The Study of Electroencephalography in Neuromarketing Research, Consumer Behavior and Performance Method: A Systematic Analysis. Journal of System Management, 9(4), 185-204.

Papadimitriou, A., & Tsoukala, V. (2024). Evaluating and enhancing the performance of the K-Means clustering algorithm for annual coastal bed evolution applications. Journal of Oceanologia, (164), 1-16.

Ros, F., Raid, R., & Guillaume, S. (2023). PDBI: A partitioning Davies-Bouldin index for clustering evaluation. Journal of Neurocomputing, (528), 1-22.

Rungruang, C., Riyapan, P., Intarasit, A., Chuarkham, K., & Muangprathub, J. (2024). RFM model customer segmentation based on hierarchical approach using FCA. Journal of Expert Systems with Applications, (237), 94-111.

Sharma, S., & Sadagopan, P. (2022). Influence of conditional holoentropy-based feature selection on automatic recommendation system in E-commerce sector. Journal of King Saud University - Computer and Information Sciences, 34(8), 5564-5577.

Shin, I., Silalahi, A., & Eunike, I. (2024). Engaging audiences in real-time: The nexus of socio-technical systems and trust transfer in live streaming e-commerce. Journal of Computers in Human Behavior Reports, (13), 71-86.

Shirmohammadi, Y., Abiyaran, P., & Peters, M. (2024). Virtual reality (VR) alongside Social Media Marketing Activities (SMMAs) as a solution for Management Information Systems (MIS). Journal of System Management, 10(1), 133-154.

Smaili, M., & Hachimi, H. (2023). New RFM-D classification model for improving customer analysis and response prediction. Ain Shams Engineering Journal, 14(12), 1-23.

Wu, X., & Liang, J. (2023). Study on trust evaluation and service selection for Service-Oriented E-Commerce systems in IoT environments. Egyptian Informatics Journal, 24(2), 257-263.

Valentini, T., Roederer, C., & Casteran, H. (2024). From Redesign to revenue: Measuring the effects of servicescape remodeling on customer lifetime value. Journal of Retailing and Consumer Services, (77), 66-82.

Zhang, H., Li, J., Zhang, J., & Dong, Y. (2024). Speeding up k-means clustering in high dimensions by pruning unnecessary distance computations. Journal of Knowledge-Based Systems, (284), 180-197.