Similarity of transactions for customer segmentation

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

Abstract

Customer segmentation is usually the first step towards customer analysis and helps to make strategic plans for a company. Similarity between customers plays a key role in customer segmentation, and is usually evaluated by distance measures. While various distance measures have been proposed in data mining literature, the desirable distance measures for various data sources and given application domains are rarely known. One of the reasons lies in that semantic meaning of similarity and distance measures is usually ignored. This paper discusses several issues related to evaluating customer similarity based on their transaction data. Various set distance measures for customer segmentation are analyzed in several imaginary scenarios, and it is shown that each measure has different characteristics which make the measure useful for some application domains but not for others. We argue that no measure always performs better than other measures, and suitable measures should be adopted for specific purposes depending on applications.

Original languageEnglish
Title of host publicationMultidisciplinary Research and Practice for Information Systems - IFIP WG 8.4, 8.9/TC 5 Int. Cross-Domain Conference and Workshop on Availability, Reliability, and Security, CD-ARES 2012, Proceedings
Pages347-359
Number of pages13
DOIs
Publication statusPublished - Sep 6 2012
EventInternational Cross-Domain Conference and Workshop on Availability, Reliability, and Security, CD-ARES 2012 - Prague, Czech Republic
Duration: Aug 20 2012Aug 24 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7465 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Cross-Domain Conference and Workshop on Availability, Reliability, and Security, CD-ARES 2012
CountryCzech Republic
CityPrague
Period8/20/128/24/12

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Lu, K., & Furukawa, T. (2012). Similarity of transactions for customer segmentation. In Multidisciplinary Research and Practice for Information Systems - IFIP WG 8.4, 8.9/TC 5 Int. Cross-Domain Conference and Workshop on Availability, Reliability, and Security, CD-ARES 2012, Proceedings (pp. 347-359). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7465 LNCS). https://doi.org/10.1007/978-3-642-32498-7_26