Customer segmentation using the extended RFMP model based on Customer lifetime value and data mining | ||
| International Journal of New Political Economy | ||
| مقاله 2، دوره 6، شماره 1، 2025، صفحه 23-46 اصل مقاله (372.59 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.48308/jep.2025.236491.1183 | ||
| نویسندگان | ||
| Abdolah Saedi* 1؛ Meysam Abbasi2؛ Alieh Mehdi Nejad3 | ||
| 1Assistant Professor of Management Department. Faculty of Management and Economics. University of Lorestan. Khorramabad . Iran | ||
| 2Master of IT Management , Faculty of Management & Economic, University of Sistan and Baluchestan, Zahedan, Iran | ||
| 3PhD candidate at the Department Of Management, Faculty of Management and Economic,Sistan and Baluchestan University, Zahedan, Iran | ||
| چکیده | ||
| One important way for identifying customers is clustering them to congruent segments. Skillfully clustering can be caused by identifying profitable customers by companies, understanding their requirement and allot their own resources in an appropriate way. We can also examine the change in the clustering of customers after the implementation of any economic policy and formulate and implement suitable public policies accordingly. The main goal of a recent study is clustering and identifying customers using of extended RFMP model. The study was done on 6665 customers of the Zahedan Refah chain store. According to this, after identification, the extended RFMP amounts (sum of recency, frequency, monetary and periodicity) weight of each variable was identified according to the analytic hierarchy process (AHP). At the next stage, optimal clusters number was specified by using Silhouette and Davies Bouldin indexes and customers were clustered with the K-means algorithm and finally, customers were preferred according to customer lifetime value. According to results, customers were divided into 3 parts and their traits were analyzed. Background can be provided for codifying relationship strategies with customers by using of recent study results. | ||
| کلیدواژهها | ||
| Customer relationship management (CRM)؛ Clustering؛ R*FMP model؛ Analytic hierarchy process (AHP)؛ K-means algorithm | ||
| مراجع | ||
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