Machine Learning Mini Batch K-means and Business Intelligence Utilization for Credit Card Customer Segmentation

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.

@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},
  volume    = {12},
  number    = {10},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Firman Pradana Rachman and Handri Santoso and Arko Djajadi},
  doi       = {10.14569/IJACSA.2021.0121024},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121024}
}