Mining sequential patterns with consideration to recency, frequency, and monetary

Ya Han Hu, Yu Hua Kao

Research output: Contribution to conferencePaperpeer-review

5 Scopus citations

Abstract

For superior decision making, the mining of interesting patterns and rules becomes one of the most indispensible tasks in today's business environment. Although there have been many successful customer relationship management (CRM) applications based on sequential pattern mining techniques, they basically assume that the importance of each customer are the same. Many studies in CRM show that not all customers have the same contribution to business, and, to maximize business profit, it is necessary to evaluate customer value before the design of effective marketing strategies. In this study, we include the concepts of RFM analysis into 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 more advantageous than conventional sequential pattern mining.

Original languageEnglish
StatePublished - 2011
Event15th Pacific Asia Conference on Information Systems: Quality Research in Pacific, PACIS 2011 - Brisbane, QLD, Australia
Duration: 7 Jul 201111 Jul 2011

Conference

Conference15th Pacific Asia Conference on Information Systems: Quality Research in Pacific, PACIS 2011
Country/TerritoryAustralia
CityBrisbane, QLD
Period7/07/1111/07/11

Keywords

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

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