Customer-item category based knowledge discovery support system and its application to department store service

Tsukasa Ishigaki, Takeshi Takenaka, Yoichi Motomura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

In the framework of personalization or micromarketing of services, an effective strategy is to examine customers or items of a specific category. This paper describes an actual service support system using discovery of category-based customer behavior knowledge. The method is realized by modeling a customers' purchase behavior with some purchase situations or conditions using massive point of sales data with a customer ID (ID-POS data) in a department store chain. We automatically generate categories of customers and items based on a purchase patterns identified in ID-POS data using probabilistic latent semantics indexing. We produce a Bayesian network model including the customer and item categories, situations and conditions of purchases, and the properties and demographic information of customers. Based on that network structure, we can systematically identify useful knowledge for use in furthering business intelligence or sustainable services. This method is applicable for marketing support, service modeling, and decision making in various business fields, including retail services.

Original languageEnglish
Title of host publicationProceedings - 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010
Pages371-377
Number of pages7
DOIs
Publication statusPublished - 2010 Dec 1
Externally publishedYes
Event2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010 - Hangzhou, China
Duration: 2010 Dec 62010 Dec 10

Publication series

NameProceedings - 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010

Other

Other2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010
Country/TerritoryChina
CityHangzhou
Period10/12/610/12/10

Keywords

  • Bayesian network
  • Business support system
  • Customer modeling
  • Large scale ID-POS data
  • PLSI

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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