K-MEANS CLUSTERING APPROACH BASED INTELLIGENT CUSTOMER SEGMENTATION TO INCREASE SALES USING CUSTOMER PURCHASE BEHAVIOR DATA

Abstract

ABSTRACT: E-commerce system has become more popular and implemented in almost all business areas. E-commerce system is a platform for marketing and promoting the products to customer through online. Customer segmentation is known as a process of dividing the customers into groups which shares similar characteristics. The purpose of customer segmentation is to determine how to deal with customers in each category in order to increase the profit of each customer to the business. Using the large amount of data available on customers and potential customers, a customer segmentation analysis allows marketers to identify discrete groups of customers with a high degree of accuracy based on demographic, behavioral and other indicators. This paper presents, K-Means Clustering approach based Intelligent Customer Segmentation to increase sales using Customer Purchase Behavior Data. To process the collected data and segment the customers, learning algorithm is used which is known as K-Means clustering. We evaluate the prediction model through a set of evaluation metrics, Mean Squared Error (MSE) and coefficient of determination (R2). K-Means clustering gives better performance for a large number of observations.