The Implementation of RFM Analysis to Customer Profiling Using K-Means Clustering

Abstract

The vast development of information technology causes an explosion in the amount of data, yet the data must be processed to obtain useful insights. The use of data is needed to study the needs, behavior, and customer's value which are meant to build better relationships or what is often referred to Customer Relationship Management (CRM). As the company grows, data is getting abundant and more difficult to interact directly with customers and problems such as marketing campaigns that are less effective can result in losses if not immediately addressed. Therefore, customer segmentation was carried out using recency, frequency, and monetary (RFM) as variables and K-Means clustering by determining the number of clusters using the elbow method and silhouette score. Based on the analysis results, there are three types of clusters, categorized as best customers, may not lost customers, and average customers.

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