Segmentation of E-Commerce Data Using K-Means Clustering Algorithm

Abstract

In this era of technology and development, every day large amount of data is being produced, collected and transferred. This data can be analysed by various machine learning algorithms and used as per requirement and interest of people. Companies can increase their productivity according to the interest of customers hence customer segmentation is being needed. It allows businesses to know and understand the needs and motivations of customer, so they provide a great experience and also launches the required and needful products. Businesses may better satisfy the demands of their consumers by customising their products, services, and marketing initiatives by knowing the distinct wants and preferences of various client groups. As a result, customers are more satisfied and loyal, which eventually boosts sales and income. This paper presents the analysis of E-commerce data and segment them on the basis of RFM variables. The customers are clustered on the basis of demographics and behavioural aspects, such as age, gender, marital status, etc. Elbow method was used for finding optimal number of clusters and total 3 clusters are formed on the basis of recency, frequency, and monetary. Now Business people can apply various marketing strategies for their interests.