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

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  • K. Moorthi
  • K. Srihari
  • S. Karthik

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.

Suggested Citation

  • K. Moorthi & K. Srihari & S. Karthik, 2020. "Improving business process by predicting customer needs based on seasonal analysis: the role of big data in e-commerce," International Journal of Business Excellence, Inderscience Enterprises Ltd, vol. 20(4), pages 561-574.
    • Handle: RePEc:ids:ijbexc:v:20:y:2020:i:4:p:561-574
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    Cited by:

    1. Aitor Goti & Leire Querejeta-Lomas & Aitor Almeida & José Gaviria de la Puerta & Diego López-de-Ipiña, 2023. "Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review," Mathematics, MDPI, vol. 11(13), pages 1-32, June.

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