Segmenting e-commerce customer through data mining techniques

Abstract

Business-to-business e-commerce is in much attention nowadays, mainly due to its growing use. In today’s world, it has become imperative for companies to segment their customers and thereby take required measures to survive against other companies. Since there exist a lot, each company must fulfil the demands of their users or they might lose them to other alternatives that exist. This report aims to analyse the customer data from two years: 2018 and 2019 of the company: Autofurnish.com and thereby recommend methods to increase customer influx and give suggestions on what mistakes should not be repeated by the company in future for better performance and sales. To analyse the database, RapidMiner tool has been used. RapidMiner is an open source predictive analytic software that gives support regarding data mining. It lets the user to build models based on their needs and gives solutions quickly. For this analysis, K-means algorithm clustering will be used. Clustering is dividing groups based on similarities and K means is one of the very commonly used methods to do so. In the software, data has been imported having information about customers which is then analysed to prove different results draw a contrast between two years as told before keeping customer segmentation in mind based on various attributes given in the dataset. Relationships between the features are identified as assess to company’s performance.