Discovering recency, frequency, and monetary (RFM) sequential patterns from customers' purchasing data

NCUIR > School of Management at National Central University > Graduate Institute of Information Management > journal & Dissertation > Item 987654321/29656

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29656

Title:	Discovering recency, frequency, and monetary (RFM) sequential patterns from customers' purchasing data
Authors:	Chen,YL;Kuo,MH;Wu,SY;Tang,K
Contributors:	資訊管理研究所
Keywords:	MULTIPLE-STORE ENVIRONMENT;MARKET BASKET ANALYSIS;DATABASES;SEQUENCES;KNOWLEDGE
Date:	2009
Issue Date:	2010-06-29 20:37:29 (UTC+8)
Publisher:	中央大學
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 de. ne 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. (c) 2009 Elsevier B.V. All rights reserved.
Relation:	ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
Appears in Collections:	[Graduate Institute of Information Management] journal & Dissertation

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