Facebook pixel tracking

The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

Contact Info
Email info@thesai.org
Website thesai.org
Follow Us
Contact Info
Email info@thesai.org
Follow Us
Research Article | Open Access |

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
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 13, No. 7 · Published 2022

DOI: https://doi.org/10.14569/IJACSA.2022.0130704

Download PDF Cite Call for Papers

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

How to Cite this Article

Arai, K., Ming, Z. M., Fujikawa, I., Nakagawa, Y., Momozaki, T., & Ogawa, S. (2022). Customer Profiling Method with Big Data based on BDT and Clustering for Sales Prediction. International Journal of Advanced Computer Science and Applications, 13(7). https://doi.org/10.14569/IJACSA.2022.0130704

Arai, Kohei, et al.. "Customer Profiling Method with Big Data based on BDT and Clustering for Sales Prediction." International Journal of Advanced Computer Science and Applications, vol. 13, no. 7, 2022, https://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},
  volume    = {13},
  number    = {7},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai and Zhan Ming Ming and Ikuya Fujikawa and Yusuke Nakagawa and Tatsuya Momozaki and Sayuri Ogawa},
  doi       = {10.14569/IJACSA.2022.0130704},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130704}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

Back to Issue
IJACSA Journal Cover

IJACSA