EconPapers    
Economics at your fingertips  
 

Improving business process by predicting customer needs based on seasonal analysis: the role of big data in e-commerce

K. Moorthi, K. Srihari and S. Karthik

International Journal of Business Excellence, 2020, vol. 20, issue 4, 561-574

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. (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.inderscience.com/link.php?id=106438 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbexc:v:20:y:2020:i:4:p:561-574

Access Statistics for this article

More articles in International Journal of Business Excellence from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().

 
Page updated 2025-03-19
Handle: RePEc:ids:ijbexc:v:20:y:2020:i:4:p:561-574