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.

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}
}