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J-WS: A Hybrid Unsupervised Mining Approach for Customer Segmentation in B2C e-CommerceProceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)10.2991/978-94-6463-094-7_13
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How does recency influence the timing of purchasing smartphones? The moderating role of contextCogent Social Sciences10.1080/23311886.2023.2253611

The RFM Model Analysis for VIP Customer: A Case Study of Golf Clothing Brand

Tung-Hsiang Chou, Shu-Chen Chang

Source Title: International Journal of Knowledge Management (IJKM)18(1)

ISSN: 1548-0666|EISSN: 1548-0658|EISBN13: 9781799893608|DOI: 10.4018/IJKM.290025

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MLA

Chou, Tung-Hsiang, and Shu-Chen Chang. "The RFM Model Analysis for VIP Customer: A Case Study of Golf Clothing Brand." IJKM vol.18, no.1 2022: pp.1-18. http://doi.org/10.4018/IJKM.290025

APA

Chou, T. & Chang, S. (2022). The RFM Model Analysis for VIP Customer: A Case Study of Golf Clothing Brand. International Journal of Knowledge Management (IJKM), 18(1), 1-18. http://doi.org/10.4018/IJKM.290025

Chicago

Chou, Tung-Hsiang, and Shu-Chen Chang. "The RFM Model Analysis for VIP Customer: A Case Study of Golf Clothing Brand," International Journal of Knowledge Management (IJKM) 18, no.1: 1-18. http://doi.org/10.4018/IJKM.290025

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Abstract

Numerous firms accumulate large quantities of data or transactions after importing information systems and services, which leads to troubles with data procedure. Firms also have demands to find customers’ information from large datasets and to understand how to develop marketing strategies accurately to adjust their operational methods. Therefore, this study proposed customer ranking combined Big Data process based on the RFM model (Recency, Frequency, Monetary) to develop a recommendation algorithm using an association rule, which finds greater recommendation to promote operational effects of firms. We adjust the weight of potential information to perform the customer ranking, which is conducted by using agglomerate hierarchical clustering. Finally, we present the recommendation by the association rule for each customer level.The datasets in this study use actual sales data; therefore, they are authentic and have been practically applied. The metrics of evaluation showed that the recommended system his study proposes is highly accurate.