Knowledge discovery of weighted RFM sequential patterns from customer sequence databases

Ya Han Hu, Tony Cheng Kui Huang, Yu Hua Kao

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


In today's business environment, there is tremendous interest in the mining of interesting patterns for superior decision making. Although many successful customer relationship management (CRM) applications have been developed based on sequential pattern mining techniques, they basically assume that the importance of each customer is the same. Previous studies in CRM show that not all customers make the same contribution to a business, and it is indispensible to evaluate customer value before developing effective marketing strategies. Therefore, this study includes the concepts of recency, frequency, and monetary (RFM) analysis in the sequential pattern mining process. For a given subsequence, each customer sequence contributes its own recency, frequency, and monetary scores to represent customer importance. An efficient algorithm is developed to discover sequential patterns with high recency, frequency, and monetary scores. Empirical results show that the proposed method is efficient and can effectively discover more valuable patterns than conventional frequent pattern mining.

Original languageEnglish
Pages (from-to)779-788
Number of pages10
JournalJournal of Systems and Software
Issue number3
StatePublished - Mar 2013


  • Constraint-based mining
  • Data mining
  • RFM analysis
  • Sequential patterns


Dive into the research topics of 'Knowledge discovery of weighted RFM sequential patterns from customer sequence databases'. Together they form a unique fingerprint.

Cite this