Understanding the Consumer: AI-Driven Predictive Analytics and the Transformation of Purchasing Behavior
Keywords:
Artificial Intelligence (AI), Predictive Analytics, Consumer Behavior, Buying Behavior, Marketing.Abstract
The digital era has revolutionized marketing from a product-centric to a customer-centric approach. Understanding consumer behavior is no longer an indulgence; it is a requirement. Artificial intelligence (AI), with its remarkable predictive analytics features, is a game-changing asset to companies. This article analyzes the implications of utilizing Artificial Intelligence Systems in predicting consumer behavior and its impact on business strategies. The method used in this study is an investigation of actual cases of firms that have effectively used this technology. Case studies were conducted on several companies that have successfully implemented this technology to understand the context of their implementation and the benefits they derived. This paper also addresses the benefits and drawbacks of AI for consumer behavior prediction and ways to overcome these obstacles. Results revealed that implementing AI Technology in forecasting customer behavior can significantly improve product individualization, enhance marketing tactics, and provide profound insights into consumer preferences. While AI's potential in marketing is evident, it's crucial to emphasize the need for more study to fully comprehend its long-term implications on consumer psychology and establish safeguards against biased algorithms.
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Wilson, J., Sachdev, S., & Alter, A. (2016). How companies are using machine learning to get faster and more efficient. Harvard Business Review Digital Articles.
Al-Ghuribi, S., Mohd Noah, S. A., & Mohammed, M. (2023). An experimental study on the performance of collaborative filtering based on user reviews for large-scale datasets. PeerJ Computer Science, 9, e1525. https://doi.org/10.7717/peerj-cs.1525
Chaffey, D. (2023). Amazon.com marketing strategy 2023: E-commerce retail giant business case study. Smart Insights. Retrieved from https://www.smartinsights.com/digital-marketing-strategy/online-business-revenue-models/amazon-case-study/
Darshan, K., Samuel, J., Swamy, M., Koparde, P., & Shivashankara, N. (2024). NLP-powered sentiment analysis on Twitter. Saudi Journal of Engineering and Technology, 9(1), 1-11. https://doi.org/10.36348/sjet.2024.v09i01.001/
Gavira, M. (2018). Amazon's recommendation engine secret sauce. LinkedIn. Retrieved from https://www.linkedin.com/pulse/amazons-recommendation-engine-secret-sauce-mario-gavira
Guo M, Xiao SS. An empirical analysis of the factors driving customers' purchase intention of green smart home products. Front Psychol. 2023 Oct 26;14:1272889. doi: 10.3389/fpsyg.2023.1272889. PMID: 37965665; PMCID: PMC10640982.
Haleem, A., Javaid, M., Qadri, M., Singh, R., & Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3, Article 100005. https://doi.org/10.1016/j.ijin.2022.08.005
Hardesty, L. (2019, November 22). The history of Amazon's recommendation algorithm: Collaborative filtering and beyond. Amazon Science. Retrieved from https://www.amazon.science/the-history-of-amazons-recommendation-algorithm
Haroon, S., Prins, C., & Schrijvers, E. (2023). Artificial intelligence: Definition and background. In Artificial Intelligence (pp. 1-22). Springer. https://doi.org/10.1007/978-3-031-21448-6_2
Li, Y. (2023). Big data analysis in consumer behavior: Evidence from social media and mobile payment. Advances in Economics, Management and Political Sciences, 64(2), 269-275. https://doi.org/10.54254/2754-1169/64/20231548
Masouras, A., Papademetriou, C., & Kissa, M. (2020). Consumers’ behavioral intentions towards low-cost airlines: The case of consumers in Greece.
Menezes, R., Jha, R., Yeh, G., & Lamkhede, S. (2023). Lessons learnt from consolidating ML models in a large scale recommendation system. Netflix Research. Retrieved from https://research.netflix.com/publication/lessons-learnt-from-consolidating-ml-models-in-a-large-scale-recommendation
Mixson, E. (2021). Data science at Netflix: How advanced data & analytics helps Netflix generate billions. AI Data Analytics Network. Retrieved from https://www.aidataanalytics.network/data-science-ai/articles/data-science-at-netflix-how-advanced-data-analytics-helped-netflix-generate-billions
Necula, S. C. (2023). Exploring the impact of time spent reading product information on e-commerce websites: A machine learning approach to analyze consumer behavior. Behavioral Sciences (Basel), 13(6), 439. https://doi.org/10.3390/bs13060439.
Netsiri, P. (2023). Application of Natural Language Processing to Extract Consumer Behaviors from Product Reviews. Journal of Economics and Business Issues, 3(2), 09–25. Retrieved from https://www.jebi-academic.org/index.php/jebi/article/view/74
Nkomo, L. (2023). From traditional to digital: Exploring the shift in marketing strategies. IMMIN Institute. Retrieved June 21, 2024, from https://imminstitute.co.za/from-traditional-to-digital-exploring-the-shift-in-marketing-strategies/
Nkomo, L. (2023). From traditional to digital: Exploring the shift in marketing strategies. IMMIN Institute. Retrieved June 21, 2024, from https://imminstitute.co.za/from-traditional-to-digital-exploring-the-shift-in-marketing-strategies/
Olaniyi, O., Abalaka, A., & Olabanji, S. (2023). Utilizing big data analytics and business intelligence for improved decision-making at leading Fortune company. Journal of Scientific Research and Reports, 29(9), 64-72. https://doi.org/10.9734/JSRR/2023/v29i91785
Osur, L. (2016). Netflix and the development of the Internet television network (Doctoral dissertation, Syracuse University). Dissertations - ALL, 448. https://surface.syr.edu/etd/448
Rathore, B. (2017). Exploring the intersection of fashion marketing in the metaverse: Leveraging artificial intelligence for consumer engagement and brand innovation. International Journal of New Media Studies, 4(2), 2394-4331. https://doi.org/10.58972/eiprmj.v4i2y17.108
Rathore, B. (2017). Exploring the intersection of fashion marketing in the metaverse: Leveraging artificial intelligence for consumer engagement and brand innovation. International Journal of New Media Studies, 4(2), 2394-4331. https://doi.org/10.58972/eiprmj.v4i2y17.108
Seseri, R. (2023, July 6). AI Atlas #19: Reinforcement learning (RL). LinkedIn. Retrieved from https://www.linkedin.com/pulse/ai-atlas-19-reinforcement-learning-rl-rudina-seseri
Shenhao, Li. (2024). Walmart's data analytics for optimized inventory and customer insights.
Shrirame, V., Sabade, J., Soneta, H., & Vijayalakshmi, M. (2020). Consumer behavior analytics using machine learning algorithms. In 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) (pp. 1-6). IEEE. https://doi.org/10.1109/CONECCT50063.2020.9198562
Simplilearn. (2023). Netflix recommendations: How Netflix uses AI, data science, and ML. Simplilearn. Retrieved from https://www.simplilearn.com/how-netflix-uses-ai-data-science-and-ml-article
Weinstein, A., Anti, K., & Ochoa, E. (2021). World’s biggest retailer launches Walmart Plus and customers have their say. Journal of Business Strategy. Advance online publication. https://doi.org/10.1108/JBS-07-2021-0133.
Wilson, J., Sachdev, S., & Alter, A. (2016). How companies are using machine learning to get faster and more efficient. Harvard Business Review Digital Articles.
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Published
2024-08-13
How to Cite
Jama, I. (2024). Understanding the Consumer: AI-Driven Predictive Analytics and the Transformation of Purchasing Behavior. International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM), 2(1). Retrieved from https://journal.unj.ac.id/unj/index.php/isc-beam/article/view/46706
