AN EMPIRICIAL ANALYSIS IN MEASURING THE IMPACT OF ARTIFICIAL INTELLIGENCE FOR BETTER MARKETING COMMUNICATION TO THE END USERS EFFECTIVELY IN THE DIGITAL ERA
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A remarkable development that deserves emphasis is the growing exposure, in the arena of digital marketing, of the basic benefits offered by scientific and technology instruments such as artificial intelligence and big data analysis. These technologies are examples of fundamental advantages. The widespread use of artificial intelligence (AI) in many different spheres of human activity has led many people to believe that AI has the capacity to shake up and completely transform these particular fields. When compared to humans, artificial intelligence has been shown to be capable of processing a wider variety of data types, including organised and unstructured data, at a higher rate and with a higher degree of precision than natural intelligence. Companies who are having trouble in this area are very interested in the issue of customer data organisation as a topic of discussion. Marketers are gaining a more in-depth understanding of their target demographic by putting machine learning's ability to draw connections between disparate data sets to work for them. The aforementioned technologies have the power to do data analysis for the aim of creating predictions, building graphical portrayals of patterns found in social media, and assessing speech tone in order to extract emotional disposition.
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Anand, S., Krushali, S. & Jojo, N. 2018. Cognitive Marketing and Purchase Decision regarding Pop Up and Banner Advertisements. The Journal of Social Sciences Research, 4(12): 718-735. Attaran, M., & Deb, P. 2018. Machine Learning: The New “Big Thing” for Competitive Advantage. International Journal of Knowledge Engineering and Data Mining. 5(1): 1-9 Batra, R., & Keller, K. L. 2016. Integrating marketing communications: New findings, new lessons, and new ideas. Journal of Marketing, 80(6):122-145. Bughin, J., Seong, J., Manyika, J., Chui, M. and Joshi, R. 2018. Notes from the AI frontier: Modelling the impact of AI on the world economy. McKinsey. Bus, H., Ti, L. & Rt, S. 2014. Driving Digital Transformation: New Skills for Leaders, New Role for the CIO. Harvard Business Review.12(4):13 – 17 Gupta, S. Leszkiewicz, A., Kumar, V. 2020. Digital analytics: Modelling for insights and new methods. Journal of Interactive Marketing, 51: 26-43 Hoffman, R. 2016. Using Artificial Intelligence to Set Information Free. MIT Sloan Management Review.14 (22): 42 – 46 Husnain, M., & Toor, A. (2017). The impact of social network marketing on consumer purchase intention in Pakistan: Consumer engagement as a mediator. Asian Journal of Business and Accounting, 10(1): 167-199. Jarrahi, M.H. 2018. Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4): 577–586. Kankam P. K. 2020. Mobile information behaviour of sandwich students towards mobile learning integration at the University of Ghana, Cogent Education, 7(1): 1-14 Kardon, B. 2019. Five AI Solutions Transforming B2B Marketing. MIT Sloan Management Review. 22(7):15 – 24 Kelleher, J., MacNamee, B., & D'Arcy, A. 2015. Fundamentals of machine learning for predictive data analytics: Algorithms, worked examples, and case studies. Cambridge, Mass.: The MIT Press. Kwarteng, M. A., & Pilík, M. 2016. Exploring Consumers’ Propensity for Online Shopping in a Developing Country: A Demographic Perspective. International Journal of Entrepreneurial Knowledge, 4(1): 90-103 Longoni, C., Bonezzi, A., & Morewedge, C. K. 2019. Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4): 629-650. Luo, X., Tong, S., Fang, Z., Qu, Z. 2019. Machines versus humans: The impact of AI chatbot disclosure on customer purchases. Marketing Science, 38 (6): 913-1084 Marinchak, C.M., Forrest, E., & Hoanca, B. 2018. Artificial intelligence: Redefining marketing management and the customer experience. International Journal of Entrepreneurship and Innovation, 8(2):14–24. Martin, K., & Murphy, D. 2017. The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2): 135–155 Mgiba, F. M. (2019). Social Network Theory, Sigma Six, and Customer Experience Management: Common Elements that Aid Positive Sales-marketing Interface: A Review of the Literature. International Review of Management and Marketing, 9(5): 165-172.
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