Analisis Segmentasi Pelanggan Toko Online Marketplace Berdasarkan Recency, Frequency, Monetary, Time, Satisfaction (Rfmts) Menggunakan Algoritma K-Medoids Clustering

Abstract

The marketplace is an e-commerce website that applies traditional market concepts and implements them online. Due to the rapid development of the times, a business will seek various ways to maintain its business so that it continues to grow and generate a high income. One way is to build and maintain long-term relationships with customers. Therefore, it is necessary to implement a marketing strategy based on customer relationship management, all of which aims to increase turnover and profit while retaining customers. From these problems, this study applies customer segmentation to detect diversity among customers, so those segments represent potential customers to improve marketing strategies. This segmentation needs to consider the value of the customer's recency, frequency, monetary, time, and satisfaction (RFMTS) variables. Recency is the customer's length of time since the last payment. Frequency is how often customers make transactions. Monetary is the number of transactions made by the customer. Time is the time interval between two consecutive purchases by a customer. Satisfaction is the level of customer satisfaction based on the total rating and number of reviews. This study uses the K-Medoids Clustering algorithm to perform segmentation according to customer characteristics. This study uses primary data from a database of one of the online marketplace stores in Indonesia.