Optimizing Customer Segmentation in Online Retail Transactions through the Implementation of the K-Means Clustering Algorithm

Abstract

Purpose: The main objective of this research is optimal use of customer segmentation using the Recency, Frequency and Monetary (RFM) approach so that companies can better understand and comprehend the needs of each customer. By carrying out this segmentation, companies can communicate better and provide services tailored to each customer.

Methods: The K-means algorithm is used as the main method for customer segmentation in this research. This research uses a dataset of online retail customers. Apart from that, this research also uses the elbow method to help determine the best number of clusters to be created by the model.

Result: Based on the elbow method, the most optimal is to use 3 clusters for this case. Thus, in K-means modeling, forming 3 clusters is the best choice. Clusters produce groups of customers who have specific characteristics in each cluster. The analysis shows that quantity and unit price have a significant influence on online retail customer behavior.

Novelty: This research strengthens the trend of using the K-means algorithm for customer segmentation in online retail datasets, which has proven popular in journals from 2018 to 2022. This research creates 3 new variables that will be used by the model to understand the characteristics of customer transaction behavior. This study also emphasizes the importance of exploratory data analysis in understanding data before clustering and the use of the elbow method to determine the most appropriate number of clusters, providing a significant contribution in analyzing customer segmentation.