EXPLORING CONSUMER BEHAVIORS IN E-COMMERCE USING MACHINE LEARNING
Keywords:
Large Language Models, Clickstream Data Analysis, E-Commerce, Personalized Recommendations, Real-Time Interface Adaptations, Consumer BehaviorsAbstract
Machine learning and clickstream data analysis together have become a game-changing method for improving e-commerce systems. In the context of e-commerce, this study offers a systematic assessment of the application of machine learning to forecast customer purchase choices, uncover engagement factors, and enhance user interfaces. The information utilized in this study, which use machine learning to analyze customer behavior in e-commerce, was acquired from many databases between 2004 and Sep 2023. With the use of machine learning, which offers revolutionary insights into personalizing shopping experiences and streamlining user interfaces, customers enjoy simple and effortless shopping trips through to predictive analytics, tailored suggestions, and real-time interface adjustments. To fully utilize machine learning in e-commerce analytics, however, a variety of issues like interpretability, algorithmic openness, and data protection need to be resolved.
References
L. T. Khrais, “Role of Artificial Intelligence in Shaping Consumer Demand in E-Commerce,” Future Internet, vol. 12, no. 12, p. 226, Dec. 2020, doi: https://doi.org/10.3390/fi12120226.
R. Ketipov, V. Angelova, Lyubka Doukovska, and R. Schnalle, “Predicting User Behavior in e-Commerce Using Machine Learning,” Cybernetics and Information Technologies, vol. 23, no. 3, pp. 89–101, Sep. 2023, doi: https://doi.org/10.2478/cait-2023-0026.
D. Cirqueira, M. Hofer, D. Nedbal, M. Helfert, and M. Bezbradica, “Customer Purchase Behavior Prediction in E-commerce: Current Tasks, Applications and Methodologies,” In International Workshop New Frontiers in Mining Complex Patterns., 2019, Available: http://www.di.uniba.it/~loglisci/NFMCP2019/NFMCP/nfMCP2019_paper_7.pdf
M. T. Ballestar, P. Grau-Carles, and J. Sainz, “Predicting customer quality in e-commerce social networks: a machine learning approach,” Review of Managerial Science, vol. 13, no. 3, pp. 589–603, Dec. 2018, doi: https://doi.org/10.1007/s11846-018-0316-x.
S. Sharma and A. A. Waoo, “Customer Behavior Analysis in E-Commerce using Machine Learning Approach: A Survey. ,” IJSRCSEIT, vol. 9, pp. 163–170., 2023.
S.-C. Necula, “Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior,” Behavioral Sciences, vol. 13, no. 6, p. 439, Jun. 2023, doi: https://doi.org/10.3390/bs13060439.
Nuthakki, S., Neela, S., Gichoya, J. W., & Purkayastha, S. (2019). Natural language processing of MIMIC-III clinical notes for identifying diagnosis and procedures with neural networks. arXiv preprint arXiv:1912.12397.
L. Li, “Analysis of e-commerce customers’ shopping behavior based on data mining and machine learning,” Soft Computing, Jul. 2023, doi: https://doi.org/10.1007/s00500-023-08903-5.
Nuthakki, S., Bucher, S., & Purkayastha, S. (2019). The development and usability testing of a decision support mobile app for the Essential Care for Every Baby (ECEB) program. In HCI International 2019–Late Breaking Posters: 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26–31, 2019, Proceedings 21 (pp. 259-263). Springer International Publishing.
G. Silahtaroglu and H. Donertasli, “Analysis and prediction of Ε-customers’ behavior by mining clickstream data,” 2015 IEEE International Conference on Big Data (Big Data), Oct. 2015, doi: https://doi.org/10.1109/bigdata.2015.7363908.
Z. J. Wen, W. Lin, and H. Liu, “Machine-Learning-Based Approach for Anonymous Online Customer Purchase Intentions Using Clickstream Data,” Systems, vol. 11, no. 5, pp. 255–255, May 2023, doi: https://doi.org/10.3390/systems11050255.
D. Tomescu, “Conversion rate optimization in e-commerce: using machine learning to identify website satisfaction in clickstream patterns,” 2020. Available: http://arno.uvt.nl/show.cgi?fid=156639
Y. Luo, Z. Yang, Y. Liang, X. Zhang, and H. Xiao, “Exploring energy-saving refrigerators through online e-commerce reviews: an augmented mining model based on machine learning methods,” Kybernetes, vol. ahead-of-print, no. ahead-of-print, Jul. 2021, doi: https://doi.org/10.1108/k-11-2020-0788.
D Singh, S Bhogawar, S Nuthakki, N Ranganathan, "Enhancing Patient-Centered Care in Oncology through Telehealth: Advanced Data Analytics and Personalized Strategies in Breast Cancer Treatment", International Journal of Science and Research (IJSR), Volume 10 Issue 9, September 2021, pp. 1707-1715, https://www.ijsr.net/getabstract.php?paperid=SR24108012724
P. Wang and Z. Xu, “A Novel Consumer Purchase Behavior Recognition Method Using Ensemble Learning Algorithm,” Mathematical Problems in Engineering, vol. 2020, pp. 1–10, Dec. 2020, doi: https://doi.org/10.1155/2020/6673535.
P. A. Pavlou, “Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model,” International Journal of Electronic Commerce, vol. 7, no. 3, pp. 101–134, Apr. 2003.
M. Lu, Z. Ye, and Y. Yan, “Research on e-commerce customer repeat purchase behavior and purchase stickiness,” Nankai Business Review International, vol. 9, no. 3, pp. 331–347, Aug. 2018, doi: https://doi.org/10.1108/nbri-05-2017-0026.
Nuthakki, S., Kumar, S., Kulkarni, C. S., & Nuthakki, Y. (2022). “Role of AI Enabled Smart Meters to Enhance Customer Satisfaction”. International Journal of Computer Science and Mobile Computing, Vol.11 Issue.12, December- 2022, pg. 99-107, doi: https://doi.org/10.47760/ijcsmc.2022.v11i12.010
K. Kafkas, Z. N. Perdahçı, and M. N. Aydın, “Discovering Customer Purchase Patterns in Product Communities: An Empirical Study on Co-Purchase Behavior in an Online Marketplace,” Journal of Theoretical and Applied Electronic Commerce Research, vol. 16, no. 7, pp. 2965–2980, Oct. 2021, doi: https://doi.org/10.3390/jtaer16070162.
A. A. Tudoran, “A machine learning approach to identifying decision-making styles for managing customer relationships,” Electronic Markets, Jan. 2022, doi: https://doi.org/10.1007/s12525-021-00515-x.
Singh, D., Nuthakki, S., Naik, A., Mullankandy, S., Singh, P. K., & Nuthakki, Y. (2022). “Revolutionizing Remote Health: The Integral Role of Digital Health and Data Science in Modern Healthcare Delivery”, Cognizance Journal of Multidisciplinary Studies, Vol.2, Issue.3, March 2022, pg. 20-30, doi: https://10.47760/cognizance.2022.v02i03.002
Advaitha Naidu Chintakunta et al, International Journal of Computer Science & Mobile Computing, Vol.12 Issue.8, Aug- 2023, pg. 150-164.
C.-W. Lu, G.-H. Lin, T.-J. Wu, I-Hsiang. Hu, and Y.-C. Chang, “Influencing Factors of Cross-Border E-Commerce Consumer Purchase Intention Based on Wireless Network and Machine Learning,” Security and Communication Networks, vol. 2021, pp. 1–9, Jun. 2021, doi: https://doi.org/10.1155/2021/9984213.
Nuthakki, S., Kolluru, V. K., Nuthakki, Y., & Koganti, S. “Integrating Predictive Analytics and Computational Statistics for Cardiovascular Health Decision-Making”, International Journal Of Innovative Research And Creative Technology, vol. 9, no. 3, pp. 1-12, May 2023, doi: https://doi.org/10.5281/zenodo.11366389
N. Chaudhuri, G. Gupta, V. Vamsi, and I. Bose, “On the platform but will they buy? Predicting customers’ purchase behavior using deep learning,” Decision Support Systems, vol. 149, p. 113622, Jun. 2021, doi: https://doi.org/10.1016/j.dss.2021.113622.
D. Moher et al., “Preferred Reporting Items for Systematic Review and meta-analysis Protocols (PRISMA-P) 2015 Statement,” Systematic Reviews, vol. 4, no. 1, pp. 1–9, Jan. 2015.
VK Kolluru, AN Chintakunta, Y Nuthakki, S Koganti. “Advancements in Wildfire Prediction and Detection: A Systematic Review”- International Journal for Multidisciplinary Research. Vol.4, no. 6, pg. 1-30, Nov – Dec 2022.
H. A. Long, D. P. French, and J. M. Brooks, “Optimising the Value of the Critical Appraisal Skills Programme (CASP) Tool for Quality Appraisal in Qualitative Evidence Synthesis,” Research Methods in Medicine & Health Sciences, vol. 1, no. 1, pp. 31–42, 2020, Available: https://journals.sagepub.com/doi/full/10.1177/2632084320947559
E. Ulitzsch, V. Ulitzsch, Q. He, and O. Lüdtke, “A machine learning-based procedure for leveraging clickstream data to investigate early predictability of failure on interactive tasks,” Behavior Research Methods, Jun. 2022, doi: https://doi.org/10.3758/s13428-022-01844-1.
Kathiriya, S., Nuthakki, S., Mulukuntla, S., & Charllo, B. V. “AI and The Future of Medicine: Pioneering Drug Discovery with Language Models”, International Journal of Science and Research, vol. 12, no. 3, pp. 1824-1829, Mar. 2023, doi: https://dx.doi.org/10.21275/SR24304173757
S. Gupta, Pooja. S. Kushwaha, U. Badhera, P. Chatterjee, and E. D. R. S. Gonzalez, “Identification of benefits, challenges, and pathways in E-commerce industries: An integrated two-phase decision-making model,” Sustainable Operations and Computers, vol. 4, 2023, doi: https://doi.org/10.1016/j.susoc.2023.08.005.
B. Requena, G. Cassani, J. Tagliabue, C. Greco, and L. Lacasa, “Shopper intent prediction from clickstream e-commerce data with minimal browsing information,” Scientific Reports, vol. 10, no. 1, Oct. 2020, doi: https://doi.org/10.1038/s41598-020-73622-y.
Z. Wu, B. H. Tan, R. Duan, Y. Liu, and R. S. Mong Goh, “Neural Modeling of Buying Behaviour for E-Commerce from Clicking Patterns,” Proceedings of the 2015 International ACM Recommender Systems Challenge, Sep. 2015, doi: https://doi.org/10.1145/2813448.2813521.
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Copyright (c) 2023 Sonika Koganti, Advaitha Naidu Chintakunta, Vinoth Kumar Kolluru, Yudhisthir Nuthakki, Sudeep Mungara (Author)
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