Journal of Intelligent Marketing Management

Journal of Intelligent Marketing Management

Clustering of Iran's Online Shopping Consumer Market by Using Artificial Neural Network

Document Type : Excerpt from doctoral thesis

Authors
1 PhD student, Department of Management, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.
2 Professor, Department of Economics and Social Sciences, Shahid Chamran University, Ahvaz, Iran.
3 Associate Professor, Department of Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
4 Assistant Professor, Department of Management, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
Abstract
The purpose of this study is to cluster the Iranian online shopping consumer market using artificial neural network, so that based on it, customers' needs can be better identified, the characteristics of each cluster can be determined more accurately, and the best clustering method can be chosen. And finally, the development of appropriate strategies for management, communication and better service to customers was achieved. Based on the purpose, the present research is descriptive and of the estimation and evaluation type, and in terms of practical purpose and in terms of time period, the situation of customers has been studied during the years 2021 to 2022. The statistical population included 52,403 online stores, and the present study selected 349 stores based on simple sampling. The method of analysis and classification of data is done by RFM, K-Means and self-organizing fuzzy-neural network. The technique used was the sum of squared error criteria and the Davies-Bouldin index. The findings showed: food items (they had the least delay in purchasing due to frequent needs); cosmetics (the majority of purchases were made by women); Luxury appliances (have the highest monetary value of purchase); industrial supplies and their accessories (the most purchases were made by men) and finally, sanitary supplies, detergents and clothes (have the most frequency of purchases). The results of the research have shown that the use of self-organizing neural networks along with the RFM method is the most suitable method for clustering and separating and valuing customers. Also, Kmeans+ANFIS also achieved good values, but the WRFM+ANFIS method has been more successful in this index.
Keywords

Subjects


احمدی‌زاد، آرمان؛ نسائی، خبات و پورحیدری، علیرضا. (1401). «شناسایی عوامل مؤثر بر خرید آنلاین در شرکت‌های مبتنی بر فناوری (مورد مطالعه : شرکت دیجی‌کالا)» توسعه تکنولوژی صنعتی، 20(49)، 49-62.
امینی خوئی، مهرداد؛ روستا، علیرضا؛ آسایش، کوروش و احمدی، مجید. (1403). «مطالعه تأثیر مدیریت ارتباط جمعی و بازاریابی رابطه ای جهت ایجاداعتماد درصنعت خودروسازی، مورد مطالعه: مشتریان شرکت ایلیا خودرو در سطح شهر تهران». علوم مدیریت ایران، 19(73)، 119-137.
پوربهرامی، شهین. (1398). خوشه‌بندی در یادگیری ماشین. تهران: نیاز دانش.
حسانی خبر، حمزه؛ پاسلاری، پیام؛ باقری، مهدی و مراد پور، سعید. (1403). «بررسی تاثیر مولفه های تبلیغات اقناعی در نیت خرید مصرف کننده در شبکه اجتماعی اینستاگرام». مدیریت بازاریابی هوشمند، 5(2)، 200-221.
رحیمی، فاطمه؛ سبط، محمد وحید و غنبر تهرانی، نسیم. (1400). «تحلیل الگوی رفتاری مشتریان شعب به روش خوشه بندی و دسته بندی با استفاده از روش RFM (مطالعه موردی-رستوران زنجیره ای)». مطالعات مدیریت کسب و کار هوشمند، 9(36)، 189-209.
روستازاده شیخ یوسفی، مریم؛ داودی، سید محمدرضا؛ آقاسی، سعید و شیروانی، علیرضا. (1403). «شناسایی مولفه‌های تولید محتوای دیجیتال در بازاریابی در صنعت مد و پوشاک». علوم و فناوری نساجی و پوشاک، 13(3)، 39-59.
سالارپور، ماشااله و اکاتی، مجتبی. (1402). «خوشه‌بندی بازارهای هدف ایران در صادرات برخی گیاهان دارویی». راهبردهای توسعه روستایی، 10(1)، 1-24.
سرشار، آرین و نوربخش، اعظم السادات. (1403). «خوشه‌بندی مشتریان بر اساس مدل RFM و با استفاده از الگوریتم فراکتال». آرمان پردازش، 5(2)،60-66.
عربشاهی، معصومه و عباس‌زاده قره‌تکان، حسین. (1402). «تأثیر مدیریت ارتباط با مشتری الکترونیکی بر عملکرد بازاریابی با تحلیل نقش میانجی نوآوری محصول و تاکید بر دانش مشتری». ارزش آفرینی در مدیریت کسب و کار، 3(2)، 42-61.
فرزانگان، الهام. (1403). «خوشه‌بندی فازی سری‌های زمانی مالی بر اساس سرریزهای نوسانات جهت‌دار: شواهدی از سهام شرکت‌های پذیرفته‌شده در بورس تهران». اقتصاد مقداری، 21(3)، 1-23.
قربانیان، علی و رضوی، حمیده. (1403). «یک رویکرد جدید به‌منظور خوشه‌بندی سری‌های زمانی بااستفاده از ترکیب زیرسری‌های زمانی». مهندسی صنایع و مدیریت، 40(1)، 27-41.
موحدی، مجتبی؛ همایونفر، مهدی؛ فدایی، مهدی و صوفی، منصور. (1402). «ارایه یک مدل ترکیبی به‌منظور تحلیل تطبیقی الگوریتم‌های خوشه‌بندی داده‌های مالی». تصمیم‌گیری و تحقیق در عملیات، 8(2)، 507-526.
نورائی آباده، مریم. (1403). «چارچوبی برای تحقق تعالی بازاریابی دیجیتال با استفاده از قدرت فناوری پردازش زبان طبیعی: از اصول تا ارزیابی کارایی». مدیریت بازاریابی هوشمند، 5(2)، 11-38.
هاشمی‌فرد، سیدحسین؛ موسوی نقابی، سیدمجتبی و اولادی، مریم. (1403). «تأثیر مدیریت ارتباط با مشتریان بر عملکرد شرکت با توجه به نقش تعدیل‌کنندگی قابلیت نوآوری: شواهدی از شرکت تولید و بسته‌بندی قارچ». اقتصاد و توسعه کشاورزی، 38(1)، 119-139.
Ahmadizad, A., Nesaei, K., & Pourheydari, A. (2022). Identifying Factors Affecting Online Shopping in Technology-Based Companies (Case Study: Digikala Company). Quarterly journal of Industrial Technology Development, 20(49), 49-62. [In Persian]
Amini Khoei, M., Rousta, A., Asayesh, K., & Ahmadi, M. (2024). Studying the Effect of Mass Communication Management and Relational Marketing Orientation to Build Trust in the Automotive Industry: A Study on Customers of Ilia Khodro Company in Tehran. Iranian journal of management sciences, 19(73), 119-137. [In Persian]
Anitha, P., & Patil, M. (2024). RFM model for customer purchase behavior using K-Means algorithm. Journal of King Saud University - Computer and Information Sciences, 19(73), 1785-1792.
Arabshahi, M., & Abbaszadehgaretekan, H. (2023). The Impact of Electronic Customer Relationship Management on Marketing Performance with the Analysis of the Mediating Role of Product Innovation and Emphasis on Customer Knowledge. Journal of value creating in Business Management, 3(2), 42-61. [In Persian]
Bischoff, P., Hogreve, J., Elgeti, L., & Kleinaltenkamp, M. (2023). How salespeople adapt communication of customer value propositions in business markets. Industrial Marketing Management, (114), 226-242. 
Cammarano, A., Varriale, V., Michelino, F., & Caputo, M. (2024). Discovering technological opportunities of cutting-edge technologies: A methodology based on literature analysis and artificial neural network. Journal of Technological Forecasting and Social Change, (209), 347-351.
Chen, Y., Tan, P., Li, M., Yin, H., & Tang, R. (2024). K-means clustering method based on nearest-neighbor density matrix for customer electricity behavior analysis. International Journal of Electrical Power & Energy Systems, (161), 31-47.
Eriksson, N., & Stenius, M. (2024). Older consumers’ views on online grocery shopping. Journal of Procedia Computer Science, (239), 90-95.
Eslahi, F., Mirahmadi, S. M. R., & Aghajani, M. (2024). Identifying the Dimensions and Consequences of Digital Customer Experience: A Phenomenological Study. Journal of Business Management, 16(1), 194-215. [In Persian]
Farzanegan, E. (2024). Directional Volatility Spillovers-based Fuzzy Clustering for Financial Time Series: Evidence from Stocks of Companies Listed on the Tehran Securities Exchange. Quarterly Journal of Quantitative Economics, 21(3), 1-23.  [In Persian]
Golderzahi, V., & Kenneth Pao, H. (2024). Revenue forecasting in smart retail based on customer clustering analysis. Journal of Internet of Things, (27), 119-139.  [In Persian]
G‌h‌o‌r‌b‌a‌n‌i‌a‌n, A., & R‌a‌z‌a‌v‌i, H. (2024). A New Approach to Time Series Clustering by Combination of Sub-S‌eries. Sharif Journal of Industrial Engineering & Management, 40(1), 27-41. [In Persian]
Hamidi, H., & Haghi, B. (2024). An Approach Based on Data Mining and Genetic Algorithm to Optimizing Time Series Clustering for Efficient Segmentation of Customer Behavior. Journal of Computers in Human Behavior Reports, 24(2), 266- 284.  
Hashemifard, S., Moussavi Neghabi, S. M., & Oladi, M. (2024). Effect of Customer Relationship Management on Company Performance According to the Moderating Role of Innovation Capability: Evidence from Button Mushroom Production and Packaging Company. Journal of Agricultural Economics and Development, 38(1), 119-139.  [In Persian]
Hesani Khabr, H., paslari, P., Bagheri, M., & Moradpour, S. (2024). Examining the Impact of Persuasive Advertising Elements on Consumer Purchase Intention in the Social Network Instagram. Journal of Intelligent Marketing Management, 5(2), 200-221. [In Persian]
Jansen, L., Bennin, K., Van Kleef, E., & Van Loo, M. (2024). Online grocery shopping recommender systems: Common approaches and practices. Journal of Computers in Human Behavior, (159), 317-365.
Kazemi, M., Keramati, M. A., & Minooie, M. (2021). Modified LRFM in order to Bank Customer Clustering based on Genetic Algorithm. Business Intelligence Management Studies, 10(38), 317-365. [In Persian]
Liu, X., Yin, C., & Li, M. (2024). The power of voice! The impact of robot receptionists’ voice pitch and communication style on customer value cocreation intention. International Journal of Hospitality Management, (122), 19-38.
Movahedi, M., Homayounfar, M., Fadaei, M., & Soufi, M. (2023). Developing a hybrid model for comparative analysis of financial data clustering algorithms. Journal of Decisions and Operations Research, 8(2), 507-526. [In Persian]
Nakano, S. (2023). Customer demand concentration in online grocery retailing: Differences between online and physical store shopping baskets. Journal of Electronic Commerce Research and Applications, (62), 553-571.
Nooraei abadeh, M. (2024). A Framework for Realizing Digital Marketing Excellence Using the Natural Language Processing Technology: From Principles to Performance Evaluation. Journal of Intelligent Marketing Management, 5(2), 11-38. [In Persian]
Pourbahrami, S. (2019). Clustering in machine learning. Tehran: Niaz Danesh. [In Persian]
Raggiotto, F., Compagno, C., & Scarpi, D. (2023). Care management to improve retail customers' and employees’ satisfaction. International Care management to improve retail customers' and employees’ satisfaction, (72), 573-590.
Rahimi, F., Sebt, M. V., & Ghanbar Tehrani, N. (2021). Branch Client Behavior Analysis Using RFM Method. Business Intelligence Management Studies, 9(36), 189-209. [In Persian]
Roustazadeh Sheikh Yousefi, M., Davoodi, S. M. R., Aghasi, S., & Shirvani, A. (2024). Identifying the components of digital content production in marketing in the fashion and clothing industry. Journal of Textile Science and Technology, 13(3), 39-59. [In Persian]
Sarshar, A., & Nourbakhsh, A. A. S. (2024). Customer Clustering Based on RFM Model and Using Fractal Algorithm. Arman Process Journal (APJ), 5(2), 60-66. [In Persian]
Sparacino, A., Maria Merlino, V., Brun, F., Borra, D., Blanc, S., & Massaglia, S. (2024). Corporate social responsibility communication from multinational chocolate companies. Journal of Sustainable Futures, (7), 153-171.
Wang, K., Gao, Y., & Nurul Habib, K. (2024). Modelling household online shopping and home delivery demand using latent class & ordinal generalized extreme value (GEV) models.  Journal of Choice Modelling, (53), 77-98.
Wen Xiao, J., Xie, Y., Fang, H., & Wu Wang, Y. (2023). A new deep clustering method with application to customer selection for demand response program. International Journal of Electrical Power & Energy Systems, (150), 361-376.
Yang, Z., Li, X., Sun, Y., Li, Y., Chen, D. (2024). Can online-shopping achieve the goal of reducing CO2 emissions? Evidence from China. Journal of Transportation Research Part D: Transport and Environment, (134), 116-132.
Zor, S. (2023). A neural network-based measurement of corporate environmental attention and its impact on green open innovation: Evidence from heavily polluting listed companies in China. Journal of Cleaner Production, (432), 706-722.
Zheng, K., Hou, X., Jasimuddin, S., Zhang, J., & Battaia, O. (2023). Logistics distribution optimization: Fuzzy clustering analysis of e-commerce customers’ demands. Journal of Computers in Industry, (151), 228-246.

  • Receive Date 09 November 2024
  • Revise Date 15 December 2024
  • Accept Date 22 December 2024