Customer Segmentation Based on Loyalty Level Using K-Means and LRFM Feature Selection in Retail Online Store

Abstract

Customer experience is a key component in increasing sales numbers. Customers are important assets that must be kept up for a corporation or firm. Prioritizing customer service is one way to protect client loyalty. To ensure that service priority is right on target, this research was conducted on groups of consumers who are anticipated to have high business prospects. The 2011 retail online shop sales dataset with 379,980 records and eight char-acteristics was used. The length, recency, frequency, and monetary (LRFM) feature selection approach was used in the study process to select features for further segmentation using the K-Means data mining method to define consumer types. Following the completion of the research, clients were divided into four categories: Premium Loyalty, Inertia Loyalty, Latent Loyalty, and No Loyalty. The correct clustering results are displayed in the vali-dation test using the Silhouette Score Index technique, which yielded a score value of 0.943898. Based on the outcomes of this segmentation, business actors may prioritize providing clients with the proper service.