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Customer Behaviour Analysis on Ecommerce Websites

Authors: Sushama Khanvilkar; Maithili Salankar; Ayesha Shetty; Vincia Marian;

Customer Behaviour Analysis on Ecommerce Websites

Abstract

{"references": ["Puspitasari, N. B., WP, S. N., Amyhorsea, D. N., & Susanty, A. (2018). Consumer's Buying Decision-Making Process in E-Commerce. In E3S Web of Conferences (Vol. 31, p. 11003). EDP Sciences.", "Jain D. Analysis of consumer behavior towards online shopping.International Conference on Contemporary Innovations in Library Information Science, Social Science & Technology for Virtual World [ICCLIST'2017], 25 March 2018.", "Sun, T., Wang, M., & Liang, Z. (2017, December). Predictive modeling of potential customers based on the customers clickstream data: A field study. In 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 2221-2225).", "Tjhin V. Decision making in online purchase of movie ticket. International Seminar on Application for Technology of Information and Communication. 2016.", "Cervenka, P., Cognitive system in market analysis. October 2016", "Tanksale, D., Neelam, N., & Venkatachalam, R. (2014). Consumer decision making styles of young adult consumers in India. Procedia-Social and Behavioral Sciences, 133, 211-218.", "Karimi, S., Papamichail, K. N., & Holland, C. P. (2013). Purchase Decision Processes in the Internet Age. In Decision Support Systems III-Impact of Decision Support Systems for Global Environments (pp. 57-66). Springer, Cham.", "Singh, A. K., & Sailo, M. (2013). Consumer behavior in online shopping: A study of Aizawl. International Journal of Business & Management Research, 1(3), 45-49.", "Zhang, A., Zheng, M., Jiang, N., & Zhang, J. (2013, November). Culture and consumers' decision-making styles: An experimental study in individual-level. In 2013 6th International Conference on Information Management, Innovation Management and Industrial Engineering (Vol. 2, pp. 444-449). IEEE.", "Lee, J., Podlaseck, M., Schonberg, E., & Hoch, R. (2001). Visualization and analysis of clickstream data of online stores for understanding web merchandising. Data mining and knowledge discovery, 5(1-2), 59-84."]}

The present study aims at analyzing and predicting the human behaviour of online shopping customers. This paper presents a literary review of recent research on customer purchase forecasting in the context of e-commerce. This paper aims to benefit websites that have started on a small scale. The key contribution is a conceptual research framework that systematically maps this existing literature into two main tasks, such as prediction of purchasing sessions, purchasing decisions.

Keywords

Customer behaviour, clustering, feature selection.

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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