Customer Segmentation: Transformation from Data to Marketing Strategy

Abstract

Customer segmentation plays a crucial role in modern business strategies, enabling organizations to effectively target and personalize their marketing efforts and enhance customer relationships. Clustering algorithms have emerged as a powerful tool for segmenting customers based on their similarities and differences. We complement the data with an RFM model to support the clustering results. RFM, which stands for Recency, Frequency, and Monetary, is a model for segmenting customers based on their historical transaction data. This study aims to explore the concept of customer segmentation and the application of the RFM model combined with clustering algorithms in the real customer dataset of a company. It presents an overview of datasets, and introduces the RFM model and its components, emphasizing the significance of recency (how recently a customer made a purchase), frequency (how often a customer makes a purchase), and monetary value (the amount spent by a customer). It highlights the practicality of the RFM model in quantifying customer behavior and categorizing customers into distinct segments. It also explains popular clustering algorithms, analyzes experimental results, and concludes with future remarks on the potential of customer segmentation. We combine unsupervised (K-Means and DBSCAN clustering) and supervised machine learning methods to build customer clusters, label each cluster based on its characteristics, and propose a strategy for each cluster.