A Study of E-Commerce Product Consumption Analysis and Visualization

In the current paper researcher says that this review presents a comprehensive overview of many methodologies used in recent years to visualize, predict customer behavior and segment using data analysis and machine learning. It introduce new trends such as pictorial storytelling with data, AI driven personalization, real-time behavioral analysis and deep learning prediction models as fundamental influencers for the next generation of e-commerce platforms. The paper also suggest future research directions like hybrid model development (combine two or more methods), protecting user privacy, and adaptive visualization systems that can help businesses make better decisions and customer engagement in online retail ecosystems.

E-Commerce Product Consumption Analysis, Machine Learning–Driven Visualization, Consumer Behavior Analytics, Interactive Visual Analytics, Real-Time Predictive Analysis, AI-Powered Decision Support

1. W. Yu and J. Xu, "Application Research of Data Visualization in the E-Commerce Product Selection Platform," in Proc. 4th Int. Conf. Economic Management and Model Engineering (ICEMME), Nanjing, China, Nov. 2022. doi: 10.4108/eai.18-11-2022.2327111 [Google Scholar] [Crossref]

2. Shivani Sharma and Sayanta Kumar Das, “Dynamic Data Visualization and Clickstream Data Analytic for E-Commerce,” International Journal of Applied Research, Special Issue: National Conference on Multidisciplinary Research for Sustainable Development, vol. SP4, pp. 48–50, 2023. Available: https://www.allresearchjournal.com/ [Google Scholar] [Crossref]

3. M. A. Raji, H. B. Olodo, T. T. Oke, W. A. Addy, O. C. Ofodile, and A. T. Oyewole, "E-commerce and Consumer Behavior: A Review of AI-powered Personalization and Market Trends," GSC Advanced Research and Reviews, vol. 18, no. 3, pp. 66–77, Mar. 2024. doi: 10.30574/gscarr.2024.18.3.0090 [Google Scholar] [Crossref]

4. B. Yang, "Intelligent Analysis Method of E-commerce Data Based on Various Machine Learning Algorithms," Open Journal of Business and Management, vol. 11, no. 2, pp. 865–875, 2023. doi: 10.4236/ojbm.2023.112049 [Google Scholar] [Crossref]

5. C. Hu, C. Wu, and Z. Huang, "Research on Precision Marketing of E-commerce Enterprise Based on Cluster Analysis in the Big Data Environment," Procedia Computer Science, vol. 247, pp. 403–411, 2024. doi: 10.1016/j.procs.2024.10.048 [Google Scholar] [Crossref]

6. A. Wasilewski, K. Juszczyszyn, and V. Suryani, "Multi-factor Evaluation of Clustering Methods for E-commerce Application," Procedia Computer Science, vol. 199, pp. 1116–1123, 2022. doi: 10.1016/j.procs.2022.01.134 [Google Scholar] [Crossref]

7. T. T. Nguyen, H. N. Mai, H. T. Nguyen, T. V. A. Pham, A. V. Nguyen, and P. T. Tran, "Consumers’ Vulnerability and E-commerce Purchase Behavior: A Serial Mediation Model," Journal of Retailing and Consumer Services, vol. 74, p. 103362, 2023. doi: 10.1016/j.jretconser.2023.103362 [Google Scholar] [Crossref]

8. F. M. Talaat, A. Aljadani, B. Alharthi, M. A. Farsi, M. Badawy, and M. Elhosseini, "A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing," Applied Sciences, vol. 14, no. 7, Art. no. 3032, 2024. doi: 10.3390/app14073032 [Google Scholar] [Crossref]

9. R. Siagian, P. Sirait, and A. Halima, "E-Commerce Customer Segmentation Using K-Means Algorithm and Length, Recency, Frequency, Monetary Model," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 2, pp. 501–507, 2023. doi: 10.14569/IJACSA.2023.0140265 [Google Scholar] [Crossref]

10. B. Yang, "Intelligent Analysis Method of E-commerce Data Based on Various Machine Learning Algorithms," Procedia Computer Science, vol. 259, pp. 591–597, 2023. doi: 10.1016/j.procs.2023.04.008 [Google Scholar] [Crossref]

11. Y. Xiong, N. Wei, K. Qiao, Z. Li, and Z. Wei, "Exploring Consumption Intent in Live E-Commerce Barrage: A Text Feature-Based Approach Using BERT-BiLSTM Model," IEEE Access, vol. 11, pp. 34731–34742, 2023. doi: 10.1109/ACCESS.2023.3259347 [Google Scholar] [Crossref]

12. H. Suryadi and R. S. Putra, "Predicting Consumption Intention of Consumer Relationship Management Users Using Deep Learning Techniques: A Review," Indonesian Journal of Science and Technology, vol. 8, no. 2, pp. 307–328, Feb. 2023. doi: 10.17509/ijost.v8i2.55814 [Google Scholar] [Crossref]

13. Md A. A. Rahib, N. Saha, R. Mia, and A. Sattar, “Customer data prediction and analysis in e-commerce using machine learning,” Bulletin of Electrical Engineering and Informatics (BEEI), vol. 13, no. 2, pp. 817–825, Apr. 2024, doi: 10.11591/eei.v13i2.6420. [Google Scholar] [Crossref]

14. A. Patel, A Data Analysis for E-Commerce Application to Improve Customer Services Using Data Mining Techniques, M.Tech. thesis, Dept. of Computer Science and Information Technology, Rai Univ., 2021. [Online]. Available: http://hdl.handle.net/10603/353205 [Google Scholar] [Crossref]

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