The Evaluation Clustering Algorithm of Iran's Online Shopping Consumer Market | ||
| International Journal of New Political Economy | ||
| مقاله 5، دوره 5، شماره 2، 2024، صفحه 115-151 اصل مقاله (1.21 M) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.48308/jep.5.2.115 | ||
| نویسندگان | ||
| Khatereh Khorasani1؛ Mansour Zaranezhad* 2؛ Ghanbar Amirnezhad3؛ Ali Kangarani Farahani4 | ||
| 1Ph.D Candidate, Department of Management, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran | ||
| 2Professor, Department of Management, Shahid Chamran University of Ahvaz, Ahvaz, Iran. | ||
| 3Associate Professor, Department of Management, South Tehran Branch, Islamic Azad University, Tehran, Iran. | ||
| 4Assistant Professor, Department of Management, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Clustering؛ Datamining؛ RFM؛ E-commerce؛ K-Means | ||
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آمار تعداد مشاهده مقاله: 215 تعداد دریافت فایل اصل مقاله: 101 |
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