K-Means and K-Medoids Clustering Methods for Customer Segmentation in Online Retail Datasets

Customer segmentation is essential to be targeted in a business that can bring so much profit to a company. In principle, customer segmentation is a way to get to know the target consumer more deeply so that the business can operate at a pace with positive prospects and generate profits. Segmentation of customers has an essential role for businesses because one of the benefits is that it makes it easier for companies to organize the products and services offered to suit consumer needs. That way, the relationship between the company and the customer is well established, resulting in customers shopping more often. This study aims to identify customer segmentation on online retail data based on Recency, Frequency, and Monetary. To achieve this goal, the methods used to perform the customer segmentation process are K-Means and K-Medoids. The result obtained is that this method is the number of appropriate K values in this case study, with a value of 4 for the K-Means method and 6 for the K-Medoids method.