Predicting Purchasing Probability of E-Commerce Customers
DOI:
https://doi.org/10.47941/jbsm.1590Abstract
Purpose: Understanding consumer behavior and anticipating their purchase likelihood is crucial for businesses to flourish in today’s competitive e-commerce market.
Methodology: This paper describes a data- driven technique for predicting the possibility of e-commerce clients completing a purchase. The research begins with a thorough assessment of the current literature, emphasizing the importance of consumer behavior prediction in the e-commerce arena and explaining the difficulties associated with effectively anticipating purchase probability.
Findings: The outcomes of this study provide a substantial contribution to the e-commerce business by giving concrete ideas for increasing customer interaction, optimizing marketing efforts, and tailoring personalized experiences.
Unique contributor to theory, policy and practice: Based on this data research, people prefer mobile applications over websites for their online purchase needs due to search ability, accessibility, and other aspects.
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References
J. Weisberg, D. Te’eni, and L. Arman, “Past pur- chase and intention to purchase in e-commerce: The mediation of social presence and trust,” In- ternet research, 2011.
T. W. Dillon and H. L. Reif, “Factors influencing consumers’ e-commerce commodity purchases.” Information Technology, Learning & Performance Journal, vol. 22, no. 2, 2004.
S. Akter and S. F. Wamba, “Big data analytics in e-commerce: a systematic review and agenda for future research,” Electronic Markets, vol. 26, no. 2, pp. 173–194, 2016.
H. Yu and X. Zhang, “Research on the applica- tion of iot in e-commerce,” in 2017 IEEE Interna- tional Conference on Computational Science and Engineering (CSE) and IEEE International Con- ference on Embedded and Ubiquitous Computing (EUC), vol. 2. IEEE, 2017, pp. 434–436.
M. Z. Shahrel, S. Mutalib, and S. Abdul- Rahman, “Pricecop-price monitor and prediction using linear regression and lsvm-abc methods for e-commerce platform.” International Journal of Information Engineering & Electronic Business, vol. 13, no. 1, 2021.
O. Dospinescu, N. Dospinescu, and I. Bostan, “Determinants of e-commerce satisfaction A comparative study between romania and moldova,” Kybernetes, 2021.
B. Givan, R. Wirawan, D. Andriawan, N. Aisyah,
A. Asep, and A. S. Putra, “Effect of ease and trustworthiness to use e-commerce for purchas- ing goods online,” International Journal of Edu- cational Research & Social Sciences, vol. 2, no. 2,
pp. 277–282, 2021.
T. T. Kidane and R. Sharma, “Factors affect- ing consumers’ purchasing decision through ecom- merce,” in Proceedings of the 2016 International Conference on Industrial Engineering and Op-
erations Management Kuala Lumpur, Malaysia, vol. 8, no. 10, 2016, pp. 159–165.
S. Dreiseitl and L. Ohno-Machado, “Logistic re- gression and artificial neural network classifica- tion models: a methodology review,” Journal of biomedical informatics, vol. 35, no. 5-6, pp. 352–
, 2002.
D. I. Lee and H. G. Kim, “The dynamic research of mobile and pc online media visit activities ef- fects on the e-commerce site visit,” Journal of the Korean Operations Research and Management Science Society, vol. 39, no. 4, pp. 85–95, 2014
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Copyright (c) 2023 Ritambhara Jha
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