Abstract: An effective marketing strategy is a method to identify the customers well. One of the methods is by performing a customer segmentation. This study provided an illustration of customer segmentation based on the RFM (Recency, Frequency, Monetary) analysis using a machine learning clustering that can be combined with customer segmentation based on demography, geography, and customer habit through data warehouse-based business intelligence. The purpose of classifying the customers based on the RFM and machine learning clustering analyses was to make a customer level. Meanwhile, customer segmentation based on demography, geography, and behavior was to classify the customers with the same characteristics. The combination of both provided a better analysis result in understanding customers. This study also showed a result that minibatch k-means was the machine learning model with the rapid performance in clustering 3-dimension data, namely recency, frequency, and monetary.

Firman Pradana Rachman, Handri Santoso and Arko Djajadi, “Machine Learning Mini Batch K-means and Business Intelligence Utilization for Credit Card Customer Segmentation” International Journal of Advanced Computer Science and Applications(IJACSA), 12(10), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121024

@article{Rachman2021,
title = {Machine Learning Mini Batch K-means and Business Intelligence Utilization for Credit Card Customer Segmentation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121024},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121024},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {10},
author = {Firman Pradana Rachman and Handri Santoso and Arko Djajadi}
}