Segmentation of Mall Customers Using RFM Analysis and K-Means Algorithm

Abstract

Customer Segmentation is the technique of separating customers into different clusters based on their specific characteristics. Segmenting customers is very essential in every business sector because each individual is different from one another and has distinct interests. But with the help of machine learning techniques, the data can be sorted to find the target group by applying algorithms to the dataset. Based on Recency, frequency and monetary (RFM) value customers purchasing behavior is segmented and the scope of this project is to divide customers based on different groups like loyal, new and churned customers and this is done by RFM table which is used to analyze customer value and K means algorithm is used to cluster the data and to determine the optimal clusters, elbow method is used. The obtained data is then used for further analysis by the organizations to improve the quality of the product, services offered to the customers and develop their relation which can help to improve sales and plan marketing strategy. Every person is different from one another and we don’t know what he/she buys or what their likes are but, with the help of machine learning technique one can sort out the data and can find the target group by applying several algorithms to the dataset.