New GRFM Approach and Fuzzy LP-Metric Weighting Score

Elham Eshrati () and Afshin Safaee ()
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Elham Eshrati: Islamic Azad University, South Tehran Branch, Iran
Afshin Safaee: Islamic Azad University, South Tehran Branch, Iran

European Journal of Studies in Management and Business, 2020, vol. 16, 43-59

Abstract: Nowadays a thorough understanding of the business processes and the organization`s customers is the essential point to survive in the market competition. In this study, a new idea was applied to import customer purchased basket data into data mining computations. First, the products were divided into families, and we assigned a numerical code for each product in the family. The sum of these assigned numbers indicates the status of the basket. After this step, the transactions were clustered based on their basket values. Customers are then clustered in each cluster using the RFM method. Using a new fuzzy LP-metric approach and pairwise comparisons, RFM indices were weighted and we obtain customer value per cluster. Then we will proceed by averaging the customer value according to the presence of each customer in different clusters. Then we clustered customers based on customer lifetime value.

Keywords: RFM; Data Mining; LP Metric; Clustering; K†Means; Paired Comparisons; GRFM (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:bco:mbrqaa::v:16:y:2020:p:43-59

DOI: 10.32038/mbrq.2020.16.04

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