Is he becoming an excellent customer for us? a customer level prediction method for a customer relationship management system

Chiaki Doi, Masaji Katagiri, Takashi Araki, Daizo Ikeda, Hiroshi Shigeno

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

1 Citation (Scopus)

Abstract

This paper proposes a method that predicts customer value by focusing on purchasing behavior. The method generates a relevance model for purchase days and amount in each period between customer value and purchasing histories beforehand based on a consumer panel survey. The authors adopt the random forest method to generate the prediction model. The proposed method facilitates the provisioning of smart customer management to each customer according to level such as suggesting products or services.

Original languageEnglish
Title of host publicationProceedings - 32nd IEEE International Conference on Advanced Information Networking and Applications, AINA 2018
EditorsLeonard Barolli, Tomoya Enokido, Marek R. Ogiela, Lidia Ogiela, Nadeem Javaid, Makoto Takizawa
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages320-326
Number of pages7
Volume2018-May
ISBN (Print)9781538621943
DOIs
Publication statusPublished - 2018 Aug 9
Event32nd IEEE International Conference on Advanced Information Networking and Applications, AINA 2018 - Krakow, Poland
Duration: 2018 May 162018 May 18

Other

Other32nd IEEE International Conference on Advanced Information Networking and Applications, AINA 2018
Country/TerritoryPoland
CityKrakow
Period18/5/1618/5/18

Keywords

  • Customer Level Prediction
  • Purchasing Data
  • Random Forest

ASJC Scopus subject areas

  • Engineering(all)

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