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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Customer Profiling Method with Big Data based on BDT and Clustering for Sales Prediction

Author 1: Kohei Arai
Author 2: Zhan Ming Ming
Author 3: Ikuya Fujikawa
Author 4: Yusuke Nakagawa
Author 5: Tatsuya Momozaki
Author 6: Sayuri Ogawa

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130704

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 7, 2022.

  • Abstract and Keywords
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Abstract: We propose a method for customer profiling based on Binary Decision Tree: BDT and k-means clustering with customer related big data for sales prediction; valuable customer findings as well as customer relation improvements. Through the customer related big data, not only sales prediction but also categorization of customers as well as Corporate Social Responsibility (CSR) can be done. This paper describes a method for these purposes. Examples of the analyzed data relating to the sales prediction, valuable customer findings and customer relation improvements are shown here. It is found that the proposed method allows sales prediction, valuable customer findings with some acceptable errors.

Keywords: Customer profiling; binary decision tree: BDT; corporate social responsibility (CSR); k-means clustering; sales prediction; valuable customer findings

Kohei Arai, Zhan Ming Ming, Ikuya Fujikawa, Yusuke Nakagawa, Tatsuya Momozaki and Sayuri Ogawa, “Customer Profiling Method with Big Data based on BDT and Clustering for Sales Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 13(7), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130704

@article{Arai2022,
title = {Customer Profiling Method with Big Data based on BDT and Clustering for Sales Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130704},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130704},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {7},
author = {Kohei Arai and Zhan Ming Ming and Ikuya Fujikawa and Yusuke Nakagawa and Tatsuya Momozaki and Sayuri Ogawa}
}


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