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題名 Discovering Recency, Frequency and Monetary (RFM) Sequential Patterns from Customers`` Purchasing Data
作者 唐揆
Tang, Kwei
貢獻者 企管系
關鍵詞 Sequential pattern; Constraint-based mining; RFM; Segmentation
日期 2009.10
上傳時間 16-Oct-2014 17:52:16 (UTC+8)
摘要 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.
關聯 Electronic Commerce Research and Applications, 8(5), 241-251
資料類型 article
dc.contributor 企管系en_US
dc.creator (作者) 唐揆zh_TW
dc.creator (作者) Tang, Kweien_US
dc.date (日期) 2009.10en_US
dc.date.accessioned 16-Oct-2014 17:52:16 (UTC+8)-
dc.date.available 16-Oct-2014 17:52:16 (UTC+8)-
dc.date.issued (上傳時間) 16-Oct-2014 17:52:16 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/70627-
dc.description.abstract (摘要) 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.en_US
dc.format.extent 825929 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) Electronic Commerce Research and Applications, 8(5), 241-251en_US
dc.subject (關鍵詞) Sequential pattern; Constraint-based mining; RFM; Segmentationen_US
dc.title (題名) Discovering Recency, Frequency and Monetary (RFM) Sequential Patterns from Customers`` Purchasing Dataen_US
dc.type (資料類型) articleen