Mining changes in customer behavior in retail marketing

Mu-Chen Chen*, Ai Lun Chiu, Hsu Hwa Chang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

145 Scopus citations

Abstract

During the past decade, there have been a variety of significant developments in data mining techniques. Some of these developments are implemented in customized service to develop customer relationship. Customized service is actually crucial in retail markets. Marketing managers can develop long-term and pleasant relationships with customers if they can detect and predict changes in customer behavior. In the dynamic retail market, understanding changes in customer behavior can help managers to establish effective promotion campaigns. This study integrates customer behavioral variables, demographic variables, and transaction database to establish a method of mining changes in customer behavior. For mining change patterns, two extended measures of similarity and unexpectedness are designed to analyze the degree of resemblance between patterns at different time periods. The proposed approach for mining changes in customer behavior can assist managers in developing better marketing strategies.

Original languageEnglish
Pages (from-to)773-781
Number of pages9
JournalExpert Systems with Applications
Volume28
Issue number4
DOIs
StatePublished - 1 Jan 2005

Keywords

  • Association rules
  • Change patterns
  • Customer behavior
  • Data mining
  • Retailing

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