Customer modeling method constructed from behavioral data and lifestyle survey

Hitoshi Koshiba, Tsukasa Ishigaki, Takeshi Takenaka, Eeichi Sakurai, Yoichi Motomura

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


It becomes increasingly important for service industries to understand customer behavior using large-scale data such as POS data. However, limitations exist in a customer model constructed on the basis of such behavioral data alone. This paper presents how we can construct a customer model on the basis of both large-scale purchase data and lifestyle survey data. It proposes a method that reveals the connection between lifestyle and behavior by deducing lifestyle from behavioral data using Random Forests, a machine learning algorithm. Then, It applies the proposed method to an actual mass merchandizers using questionnaires on lifestyle collected and the customer behavioral data (ID-POS Data). It thereby demonstrates the effectiveness of the proposed method and its possible use in supporting managerial decision-making on critical issues such as product selection.

Original languageEnglish
Pages (from-to)1787-1795+18
JournalIEEJ Transactions on Electronics, Information and Systems
Issue number9
Publication statusPublished - 2013


  • Computational Customer Model
  • ID-POS
  • Large-scale Data
  • Lifestyle
  • Random Forests


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