Clustering Pelanggan E-Marketplace Shopee Berdasarkan Kategori Produk dan Pendapatan Menggunakan K-Means

Abstract

Customer clustering is a technique for marketing to be more effective in determining the most potential target market. Clustering customers with various characteristics will influence the company's marketing management. This research uses a dataset that is private data. The data source in this research is distributing questionnaires to Shopee e-marketplace customer respondents. The data used was 296 Shopee e-marketplace customer data in the Surakarta area. This dataset has similarities, so it is necessary to carry out a clustering method using the K-Means method. The best K-Means clustering results are with parameter k=3, which produces scores of 0.725 and 0.615. The test results show 3 (three) customer clusters on the Shopee e-marketplace with a relationship between product and turnover. This research shows that the cluster of customers who buy beauty products sold on the Shopee e-marketplace with customer income is between 2 and 6 million. Customers calculate the income they have when purchasing products on the Shopee e-marketplace.