Title: Improving business process by predicting customer needs based on seasonal analysis: the role of big data in e-commerce
Authors: K. Moorthi; K. Srihari; S. Karthik
Addresses: Department of Computer Science and Engineering, Jansons Institute of Technology, Tamil Nadu, India ' Department of Computer Science and Engineering, SNS College of Engineering, India ' Department of Computer Science and Engineering, SNS College of Technology, Tamil Nadu, India
Abstract: Many e-commerce sites give item recommendations to buyers while they navigate the site. This study aims to identify the ways to predict the customer demands based on different seasonal in India and improving the business process of a new e-commerce seller by giving recommendations. We focus on textile and we categorised the seasonal in to three winter season, summer season and rainy season. In this study we analyse historical sale record of a new e-commerce seller Esteavo International based on these three seasonal. Using these analyses, we aim to determine the purchase patterns of the customers and the factors affecting the changes in sale on different seasons. Also, we developed new big data architecture it guides the e-commerce seller for taking effective decisions to improve their business process.
Keywords: big data analytics; seasonal analysis; textile; e-commerce; recommendations.
International Journal of Business Excellence, 2020 Vol.20 No.4, pp.561 - 574
Received: 26 Jan 2018
Accepted: 07 Dec 2018
Published online: 31 Mar 2020 *