Customer Segmentation Using Machine Learning Model: An Application of RFM Analysis

Abstract

Machine learning encompasses a diverse array of both supervised and unsupervised techniques that facilitate prediction, classification, and anomaly detection. Among the many fields of application for such techniques, customer churn prediction is a prominent one. In order to forecast customer switching, data scientists employ a variety of demographic, social, transactional, and behavioral variables and attributes. Unfortunately, many businesses in the United Kingdom still lack the comprehensive and adaptable consumer data required to perform accurate analyses. As a result, they often rely heavily on data produced by Enterprise resource planning (ERP) systems, which is primarily transactional in nature. Consequently, businesses are often limited to modeling and forecasting on transactional data alone and are unlikely to invest significantly in marketing research or other customer-related sources. Businesses are often limited to performing modeling and forecasting on transactional data that are most often not based on advanced techniques like RFM and ML. So, the major objective of the current work is to provide a mix of machine learning and Recency, frequency and monetary (RFM) analysis techniques for churn prediction using mostly transactional data. The dataset was taken from the dataset search website containing online retail datasets. Every customer's Recency, frequency and monetary (RFM) scores are computed based on the available data. A churn metric that indicates whether or not the customer has made a transaction in a limited time. Through this paper, different techniques are compared. We used K-means and DBSCAN clustering. By the end of this paper, it may be inferred that the act of dividing customers into six distinct clusters is a more practical and straightforward approach.

Customer Segmentation Through RFM Analysis and K-Means Clustering: Leveraging Data-Driven Insights for Effective Marketing Strategy

“…RFM can also be used to categorize clients based on their past purchases, relying on three key customer attributes: the date of purchase, the frequency of purchases, and the monetary value of purchases (Dzulhaq et al, 2019;Ali et al, 2023). Recency, which measures the time between consecutive purchases, is an important metric influencing customer engagement with a brand (Lewaaelhamd, 2024); (Wang, 2022). Customers who engage frequently tend to be more loyal, showing higher levels of involvement Joung & Kim, (2023); Ma et al, (2023).…”