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.
Highlights
Knowing customer needs is a way to win the competition in the market and increase company profits
The data displayed is a summary of the clustering results of machine learning and demography, geography, and customer habits
The data warehouse concept utilization is used for constructing an integrated data management system that can handle a large amount of data
Summary
Knowing customer needs is a way to win the competition in the market and increase company profits. Several other studies combine RFM (Recency, Frequency, Monetary) Analysis with K-means to determine customer ratings [3]. Some of these studies only make segmentation based on numerical values or predictive numbers generated by machine learning such as annual income and spending scores [1] or RFM Score [3], but do not grouping them with categorical and descriptive data. A research idea emerged that utilizes machine learning and business intelligence to create customer segmentation in a data warehouse platform. This study discussed the utilization of machine learning and business intelligence built in the data warehouse platform using SQL Server. This study tested some machine learning models of clustering to find a model with a rapid performance
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More From: International Journal of Advanced Computer Science and Applications
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