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政大典藏 > College of Commerce > Department of Business Administation > Periodical Articles > Item 140.119/70627

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/70627

Title:	Discovering Recency, Frequency and Monetary (RFM) Sequential Patterns from Customers`` Purchasing Data
Authors:	唐揆 Tang, Kwei
Contributors:	企管系
Keywords:	Sequential pattern;Constraint-based mining;RFM;Segmentation
Date:	2009.10
Issue Date:	2014-10-16 17:52:16 (UTC+8)
Abstract:	In response to the thriving development in electronic commerce (EC), many on-line retailers have developed Web-based information systems to handle enormous amounts of transactions on the Internet. These systems can automatically capture data on the browsing histories and purchasing records of individual customers. This capability has motivated the development of data-mining applications. Sequential pattern mining (SPM) is a useful data-mining method to discover customers’ purchasing patterns over time. We incorporate the recency, frequency, and monetary (RFM) concept presented in the marketing literature to define the RFM sequential pattern and develop a novel algorithm for generating all RFM sequential patterns from customers’ purchasing data. Using the algorithm, we propose a pattern segmentation framework to generate valuable information on customer purchasing behavior for managerial decision-making. Extensive experiments are carried out, using synthetic datasets and a transactional dataset collected by a retail chain in Taiwan, to evaluate the proposed algorithm and empirically demonstrate the benefits of using RFM sequential patterns in analyzing customers’ purchasing data.
Relation:	Electronic Commerce Research and Applications, 8(5), 241-251
Data Type:	article
Appears in Collections:	[Department of Business Administation] Periodical Articles

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