A Framework for Customer Segmentation to Improve Marketing Strategies Using Machine Learning

Abstract

It is hard for the marketing team to set a strategy without dividing the customers into groups. Clustering is a well-known machine-learning technique that can be used to implement customer segmentation. It is an unsupervised learning method that creates clusters by dividing a dataset into many valuable subclasses. In online retail datasets, algorithms such as K-means, Mini Batch K-means, Spectral Clustering, and Fuzzy K-means are employed to categorize customers according to their Recency, Frequency, and Monetary (RFM) features. After analyzing the Silhouette Score, the K-means achieved a higher score, 0.432619, which implies that this algorithm achieved comparable cluster cohesion and separation levels. This paper aims to develop a framework for customer segmentation using machine learning to improve marketing strategies.