Implementation of Clustering Algorithm Method for Customer Segmentation

Abstract

The intense competition in the sale of goods and services in the digital era of e-commerce requires to manage customers optimally. Some online shops try to improve their marketing strategies by classifying their customers. This study aims to determine potential customers, namely loyal customers. Potential customers can be determined by customer segmentation. Sampling from several online shops in Indonesia. The model used for segmentation is RFM (Recency, Frequency, and Monetary) and data mining techniques, namely clustering method with the K-Means algorithm. The results of this segmentation research divide the customer into 2 clusters. The best number of clusters is determined based on the Davies Bouldin index. The first cluster is cluster 0 consisting of 261 customers with RFM Score between 111–543. The first cluster includes the Everyday Shopper group. The second cluster, cluster 1 consists of 102 customers with RFM Score 443–555. The second cluster includes the Golden Customer group. With the existence of research on customer segmentation, it is expected to help in grouping customers so that companies can determine the right strategy for each group of customers.