Marketing with Artificial Intelligence and Predicting Consumer Choice
Abstract
Any company's ability to predict consumer behavior is critical to its success. To attain this goal in artificial intelligence marketing, a variety of predictive analytic tools are available, each with its own set of pros and limitations. This study project aims to bring these very varied methodologies together and demonstrate their strengths, shortcomings, and ideal uses. It serves as a link between the person who must use or acquire these problem-solving techniques and the community of professionals who perform the analysis. It's also a useful and easy-to-understand reference to the numerous astounding improvements that have recently been made in this intriguing sector.
References
Huang M-H, Rust R, Maksimovic V. The feeling economy: Managing in the next generation of artificial intelligence (AI). Calif Manage Rev. 2019;61(4):43–65.
Sion G. How artificial intelligence is transforming the economy. Will cognitively enhanced machines decrease and eliminate tasks from human workers through automation? J Self-Governance Manag Econ. 2018;6(4):31–6.
Raiter O. Applying Supervised Machine Learning Algorithms for Fraud Detection in Anti-Money Laundering. J Mod Issues Bus Res [Internet]. 2021 Dec 2;1(1):14–26. Available from: https://international-journals.website/index.php/JMIB/article/view/4
Lopez-Rojas EA, Axelsson S. Money laundering detection using synthetic data. In: Annual workshop of the Swedish Artificial Intelligence Society (SAIS). Linköping University Electronic Press, Linköpings universitet; 2012.
Intelligence A. Automation, and the Economy. Exec Off Pres. 2016;18–9.
Martínez-López FJ, Casillas J. Artificial intelligence-based systems applied in industrial marketing: An historical overview, current and future insights. Ind Mark Manag. 2013;42(4):489–95.
Raiter O. Macro-Economic and Bank-Specific Determinants of Credit Risk in Commercial Banks. Empir Quests Manag Essences [Internet]. 2021 Dec 2;1(1):36–50. Available from: https://researchberg.com/index.php/eqme/article/view/28
Vlačić B, Corbo L, e Silva SC, Dabić M. The evolving role of artificial intelligence in marketing: A review and research agenda. J Bus Res. 2021;128:187–203.
Munoz JM, Naqvi A. Business Strategy in the Artificial Intelligence Economy. Business Expert Press; 2018.
Wilson M, Paschen J, Pitt L. The circular economy meets artificial intelligence (AI): Understanding the opportunities of AI for reverse logistics. Manag Environ Qual An Int J. 2021;
Anderson DP, Fedak G. The computational and storage potential of volunteer computing. In: Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGRID’06). IEEE; 2006. p. 73–80.
Lenat DB, Hayes-Roth F, Klahr P. Cognitive economy in artificial intelligence systems. In: Proceedings of the 6th international joint conference on Artificial intelligence-Volume 1. 1979. p. 531–6.
Raiter O. Segmentation of Bank Consumers for Artificial Intelligence Marketing. Int J Contemp Financ Issues [Internet]. 2021;1(1):39–53. Available from: https://scholar-publica.space/index.php/fin/article/view/9
Hartmann E. B-to-B Electronic Marketplaces: Successful Introduction in the Chemical Industry [Internet]. Deutscher Universitätsverlag; 2013. (Business-to-Business-Marketing). Available from: https://books.google.nl/books?id=bApJCAAAQBAJ
Hatch MJ. The pragmatics of branding: an application of Dewey’s theory of aesthetic expression. Eur J Mark. 2012;
Roberts J, Cayla J. Global branding. SAGE Handb Int Mark. 2009;346–60.
Stanley TO, Ford JK, Richards SK. Segmentation of bank customers by age. Int J Bank Mark. 1985;
Goller S, Hogg A, Kalafatis SP. A new research agenda for business segmentation. Eur J Mark. 2002;
Tong A. The possibility of a decentralized economy in China and the USA. 2021;
Rass S, König S, Schauer S. Semi‐automated Parameterization of a Probabilistic Model Using Logistic Regression—A Tutorial. Game Theory Mach Learn Cyber Secur. 2021;438–84.
Wang S-N, Yang J-G. A money laundering risk evaluation method based on decision tree. In: 2007 international conference on machine learning and cybernetics. IEEE; 2007. p. 283–6.
Tang J, Yin J. Developing an intelligent data discriminating system of anti-money laundering based on SVM. In: 2005 International conference on machine learning and cybernetics. IEEE; 2005. p. 3453–7.
Labib NM, Rizka MA, Shokry AEM. Survey of machine learning approaches of anti-money laundering techniques to counter terrorism finance. In: Internet of Things—Applications and Future. Springer, Singapore; 2020. p. 73–87.
Shaheen MY. Applications of Artificial Intelligence (AI) in healthcare: A review. 2021;
Tong A. Comparison of the fin-tech evergreen fund in China and USA. Available SSRN 3904647. 2021;
Akbar K. How economic sustainability is affected by innovation performance and sustainable manufacturing. World J Adv Res Rev. 2021;11(1):247–55.
Ind N, Riondino MC. Branding on the Web: A real revolution? J Brand Manag. 2001;9(1):8–19.
Garcia V. Do Online Marketplaces Play a Significant Role in Shaping Entrepreneurial Intention? An Empirical Investigation. Empir Quests Manag Essences [Internet]. 2021 Nov 29;1(1):24–35. Available from: https://researchberg.com/index.php/eqme/article/view/27
Garcia V. Examining the Impact of Selling on Amazon on the Revenue growth of MSEs in the US. Int J Contemp Financ Issues [Internet]. 2021 Nov 29;1(1):28–38. Available from: https://scholar-publica.space/index.php/fin/article/view/8
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