A Study on Micro-Segmentation of Retail Customers Using K-Means Clustering

This study aims to identify clients that share similar traits and develop a new micro-segmentation strategy. Drawn from two marketing theories i.e., customer relationship and personalisation and using the Recency Frequency Monetary (RFM) technique, clusters from the K-means technique are created to predict the behaviour of the best and least contributing retail customers. Transactional data was extracted from a Business to Customer (B2C) hyperretail store in India comprising 10, 20, 284 transactions done by 2140 regular customers taking into account their recency, frequency and total spending. Based on RFM metric values across three heterogeneous segments, customers were characterised as toppers, moderated and churners. Analysis reveals that the most valuable customers have RFM scores as HHH (high recency, high frequency and high monetary value). These are the most loyal customers and retailers cannot afford to lose them. In micro-segmentation, stores should also prioritise retaining customers who have a recent shopping experience (medium recency) but do so infrequently, while spending larger sums. This can be achieved through tailored marketing strategies. Implications stand for both offline and online retail businesses to understand customer behaviour and tailor-made marketing strategies.