Customer Segmentation Based on RFM Analysis and Unsupervised Machine Learning Technique

Customers should be one of the main focal points of any profitable business. Loyal customers who develop a relationship with the organization raise multitudes of business prospects. An organization looking to reap benefits from such opportunities must find a way to first of all, identify such customers and secondly, market their products to them in an individualizedway to develop a lucrative business. This would require the organization to spot such customers and then differentiate their personal needs, preferences and behaviours. The aim of this paper is to tackle this problem using RFM analysis and Unsupervised Machine Learning technique called K-Means Clustering. RFM (Recency, Frequency, Monetary) analysis helps determine the behaviour of the customer with the organisation. The RFM values for each customer are calculated first following with the RFM Scores. Then, K-Means Clustering is implemented on the basis of the RFM Scores and in the end, we get clusters of customers. At this point, we will be able to analyze each cluster and accurately identify the characteristics of the customers. This will make it easy for the organization to customize their marketing strategies according to the customer behaviour, which will result in raised profits.