Customer Segmentation Model using K-means Clustering on E-commerce

Abstract

Customer segmentation is often referred to as dividing a market into groups of customers who may share similar characteristics. This is an effective way to identify unmet customer needs. In today's world, companies are based on innovation and their ability to capture the attention of their customers with a variety of products. But such a wide range of products can confuse customers as what to choose and what not to choose. Even businesses unsure about which consumers to sell their products to. For service companies, excellent customer service is essential to remain competitive in the business. Consumers require change with time and different customer groups have different needs. Today's businesses must constantly find new ways to retain customers while keeping up with the competition. Since various customer types have different options as well as different needs, it is necessary for business groups to have knowledge of different clusters of consumers. This study performs customer segmentation to distinguish customers from each other. Customers can be classified into different groups based on different characteristics. This will allow e-commerce companies to meet their customer demands with more ease and use different marketing strategies for different customer groups. This research concentrates on the issue of customer segmentation in e-commerce using a hybrid approach of the Elbow method and K-means clustering approach on the dataset taken from Kaggle.