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

  • Authors:

  • Yen-Liang Chen;Mi-Hao Kuo;Shin-Yi Wu;Kwei Tang

  • Affiliations:

  • Department of Information Management, National Central University, No. 300, Jhongda Road, Chung-Li 320, Taiwan, ROC;Department of Information Management, National Central University, No. 300, Jhongda Road, Chung-Li 320, Taiwan, ROC;The Industrial Technology Research Institute, Hsinchu 320, Taiwan, ROC;Krannert School of Management, Purdue University, West Lafayette, IN, USA

  • Venue:
  • Electronic Commerce Research and Applications
  • Year:
  • 2009

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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.