Influence of AI Tools on Consumer Behavior Management in Digital Marketing
DOI:
https://doi.org/10.15407/scine21.01.067Keywords:
artificial intelligence, artificial intelligence tools, behavior management, digital marketing, Meta Ads, advertising campaign performance, marketing research, digital spaceAbstract
Introduction. With the rapid advancement of digital technologies, online advertising has become essential for effective brand communication with target audiences in digital spaces.
Problem Statement. It has been hypothesized that artificial intelligence (AI) tools can enhance the effectiveness of advertising campaigns, leading to increased sales, higher profits for advertisers, and improved returns on marketing investments.
Purpose. This study aims to examine the impact of AI tools on managing consumer behavior in digital marketing.
Material and Methods. An experiment has been conducted on the Meta Ads platform to compare the outcomes of advertising campaigns configured with AI tools versus manual settings. Two test groups of advertising campaigns have been selected based on the competence and awareness of the management targets. Using Kohonen maps, the refl exive characteristics of the management targets in these groups have been assessed. By employing both manual settings and AI tools, an experiment has been carried out by a digital marketing specialist to modify potential customers’ reflexive characteristics and investigate changes in their behavior regarding targeted actions within the advertising campaigns. Results have been compared across both configurations.
Results. The study has shown that AI tools enable effective influence on user behavior in digital spaces. AI-generated recommendations have led to increased reach, clicks, and conversions from ad links, with the greatest efficiency observed when targeting potential customers. Potential areas for further research have also been identified.
Conclusions. Leveraging AI tools in Meta Ads campaigns enables advertisers to increase product sales and improve returns on marketing investments.
Downloads
References
Bean, R. (2018). How Big Data and AI Are Driving Business Innovation in 2018. URL: https://sloanreview.mit.edu/ article/how-big-data-and-ai-are-driving-business-innovation-in-2018/ (Last accessed: 16.08.2024).
Miller, S. (2018). AI: Augmentation, more so than automation. Asian Management Insights, 5(1), 1—20. URL: https:// ink.library.smu.edu.sg/ami/83 (Last accessed: 16.08.2024).
McKinsey & Company. (2023). The state of AI in 2023: Generative AI’s breakout year. URL: https://www.mckinsey. com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year#research (Last accessed: 16.08.2024).
Forbes. (2023). AI in Business. URL: https://www.forbes.com/advisor/business/software/ai-in-business/ (Last accessed: 01.12.2023).
Daugherty, P. R., Wilson, H. J. (2018). Human+ Machine: Reimagining Work in the Age of AI: Harvard Business Press.
Agrawal, A., Gans, J., Goldfarb, A. (2018). Prediction Machines: The simple economics of artificial intelligence: Harvard Business Press.
Panetta, K. (2018). Gartner Top 10 Strategic Technology Trends for 2018. URL: https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technologytrends-for-2018/ (Last accessed: 01.12.2023).
Li, H. (2019). Special Section Introduction: Artificial Intelligence and Advertising. Journal of Advertising, 48(4), 333— 337. https://doi.org/10.1080/00913367.2019.1654947
Shumanov, M., Cooper, H., Ewing, M. (2021). Using AI predicted personality to enhance advertising effectiveness. European Journal of Marketing. https://doi.org/10.1108/EJM-12-2019-0941
Shumilo, Y. M. (2022). Possibilities and advantages of using artificial intelligence tools in digital marketing activities. In: Commercialization of Innovations: Protection of Intellectual Capital, Marketing, and Innovations (monograph) (Еds. Sager L.Yu., Sihydi L.O.). Sumy [in Ukrainian].
Shumilo, Y. M. (2022). Artificial intelligence tools for managing the behavior of economic agents in marketing activities. Bulletin of V.N. Karazin Kharkiv National University. Series: International Relations. Economics. Country Studies. Tourism, 15, 60—68. https://doi.org/10.15407/scin13.05.019 . [in Ukrainian]
Bačík, R., Fedorko, R., Kakalejčík, L., Pudło, P. (2015). The importance of Facebook Ads in terms of online promotion. Journal of applied economic sciences, 10(5), 35. URL: https://www.ceeol.com/search/article-detail?id=537100 (Last accessed: 16.08.2024).
Srinivasan, D. (2020). Why Google dominates advertising markets. Stanford Technology Law Review, 24, 55. URL: https:// heinonline.org/HOL/LandingPage?handle=hein.journals/stantlr24&div=4&id=&page= (Last accessed: 16.08.2024).
Proskurnina, N. V., Dobroskok, Yu. B. (2019). Artificial intelligence in marketing activities of trade enterprises. Economic development and heritage of Semen Kuznets: materials of the International science and practice conference, (30—31, May, 2019, Kharkiv), 254—255. Kharkiv [in Ukrainian]
Khrupovych, S. Ye., Borysova, T. M. (2021). Using artificial intelligence in marketing analysis of unstructured data. Marketing and digital technology, 5(1), 17—26. URL: https://elartu.tntu.edu.ua/handle/lib/41207 (Last accessed: 16.08.2024) [in Ukrainian]. https://doi.org/10.15276/mdt.5.1.2021.2
Khrupovych, S. E., Okrepkyi, R. B., Dudar, V. T. (2022). Using artificial intelligence to model the consumer portrait in digital marketing. Galician Economic Herald, 74(1), 163—170. URL: https://elartu.tntu.edu.ua/handle/lib/41207 (Last accessed: 16.08.2024) [in Ukrainian]. https://doi.org/10.33108/galicianvisnyk_tntu2022.01.163
Korsunova, K. Y. (2022). The impact of artificial intelligence on international digital marketing. Bulletin of the Volodymyr Dahl East Ukrainian National University, 4(274), 25—30. URL: https://doi.org/10.33108/galicianvisnyk_tntu2022.0 (Last accessed: 16.08.2024) [in Ukrainian].
Haleem, A., Javaid, M., Qadri, M. A., Singh, R. P., Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks. https://doi.org/10.33216/1998-7927-2022-276-6-13-19
Boddu, R. S. K., Santoki, A. A., Khurana, S., Koli, P. V., Rai, R., Agrawal, A. (2022). An analysis to understand the role of machine learning, robotics and artificial intelligence in digital marketing. Materials Today: Proceedings, 56, 2288—2292. https://doi.org/10.1016/j.matpr.2021.11.637
Rathore, B. (2023). Digital Transformation 4.0: Integration of Artificial Intelligence & Metaverse in Marketing. Eduzo ne: International Peer Reviewed/Refereed Multidisciplinary Journal, 12(1), 42—48. URL: https://www.researchgate.net/ profile/Bharati-Rathore-2/publication/368789565_Digital_Transformation_40_Integration_of_Artificial_Intelligence_ Metaverse_in_Marketing/links/63f9d19d0d98a97717b8350e/Digital-Transformation-40-Integration-of-ArtificialIntelligence-Metaverse-in-Marketing.pdf (Last accessed: 16.08.2024). https://doi.org/10.56614/eiprmj.v12i1y23.248
Kopalle, P. K., Gangwar, M., Kaplan, A., Ramachandran, D., Reinartz, W., Rindfleisch, A. (2022). Examining artificial in telligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities. International Journal of Research in Marketing, 39(2), 522—540. https://doi.org/10.1016/j.ijresmar.2021.11.002
Hermann, E. (2022). Leveraging artificial intelligence in marketing for social good — An ethical perspective. Journal of Business Ethics, 179(1), 43—61. https://doi.org/10.1007/s10551-021-04843-y
Atanasov, V. (2020). Digital Capitalism and Internet Utopias. Kyiv [in Russian].
Zuboff, S. (2019). The Age of Surveillance Capitalism. New York.
Dyer-Witheford, N., Kjøsen, A. M., Steinhoff, J. (2019). Inhuman Power: Artificial Intelligence and the Future of Capitalism. London. https://doi.org/10.2307/j.ctvj4sxc6
Steinhoff, J. (2021). Automation and Autonomy Labour, Capital and Machines in the Artificial Intelligence Industry. Marx, Engels, and Marxisms. Palgrave Macmillan, Cham. Washington. https://doi.org/10.1007/978-3-030-71689-9
Thaler, R. H. (2015). Misbehaving: The story of behavioral economics. New York. Norton & Company, Incorporated.
Sunstein, C. R., Thaler, R. H. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven. Yale University Press.
Thaler, R. H., Ganser, L. J. (2015). Misbehaving: The Making of Behavioral Economics. The Review of Austrian Economics, 30(1), 137—141. https://doi.org/10.1007/s11138-015-0330-z
Arrow, K. J. (1973). Information and Economic Behavior. Fort Belvoir. https://doi.org/10.21236/ad0768446
Novikov, D. A. (2014). Reflexion and Control: Mathematical Models. London. https://doi.org/10.1201/b16625
Chkhartishvili, A. G., Gubanov, D. A., Novikov, D. A. (2018). Models of Influence in Social Networks. In Social Networks: Models of Information Influence, Control and Confrontation. Springer International Publishing. https://doi.org/10.1007/978-3-030-05429-8_1
Korepanov, V. O., Chkhartishvili, A. G., Shumov, V. V. (2022). Game-theoretic and reflexive combat models. Computer Research and Modeling, 14(1), 179—203. https://doi.org/10.20537/2076-7633-2022-14-1-179-203
Mints, A., Kamyshnykova, E., Zherlitsyn, D., Bukrina, K., Bessonova, A. (2021). Corporate Social Responsibility Impact on Financial Performance: a Case for the Metallurgical Industry. WSEAS Transactions on Environment and Development, 17(39), 398—409. https://doi.org/10.37394/232015.2021.17.39,
Mints, A., Schumann, А., Kamyshnykova, E. (2020). Stakeholders’ rank of reflexion diagnostics in a corporate social responsibility system. Economic Annals-XXI, 181(1—2), 92—104. https://doi.org/10.21003/ea.V181-08
Turlakova, S. S. (2022). Conceptual provisions for managing the behaviour of economic agents in the digital space using artificial intelligence tools. Economics and entrepreneurship, 49, 40—54. https://www.doi.org/10.33111/EE.2022.49.TurlakovaS [in Ukrainian].
Shumilo, Y. M. (2023). Reflective management of the behaviour of marketing specialists using artificial intelligence tools. In: Actual problems of system analysis and modelling of management processes (monograph) (Eds. V. Ponomarenko, L. Guryanova, J. Peliova, E. Nizhynsky). Bratislava [in Ukrainian].
Stashkevych, I., Turlakova, S., Shevchenko, O., Derzhevetska, M. (2020). IDEF0-Technology of Modeling of Processes of Minimization the Resistance of the Personnel to Organizational Changes at the Enterprise. WSEAS Transactions on Environment and Development, 16, 286—296. https://doi.org/10.37394/232015.2020.16.30
Turlakova, S., Vyshnevskyi, O., Lohvinenko, B., Fomichenko, I. (2020). Role of Reflexive Characteristics of Agents in the Level of Consistency of Goals in the Group. WSEAS Transactions on Environment and Development, 16, 297—304. https://doi.org/10.37394/232015.2020.16.31
Turlakova, S., Lohvinenko, B. (2023). Artificial intelligence tools for managing the behavior of economic agents at micro level. Neuro-Fuzzy Modeling Techniques in Economics, 12, 3—39. https://doi.org/10.33111/nfmte.2023.003
Turlakova, S. S. (2022). Modeling the values of reflexive characteristics of agents within the management of herd behavio r at the enterprises. Neuro-Fuzzy Modeling Techniques in Economics, 11, 48—77. https://doi.org/10.33111/nfmte.2022.048
Shumilo, Y. M. (2023). Reflexive management of the behavior of marketing specialists with the help of artificial intelligence tools. Actual problems of system analysis and modeling of management processes: monograph. (Eds. V. Ponomarenko, L. Guryanova, Ya. Peliova, E. Nizhynskyi). Bratislava-Kharkiv.
Shumilo, Y. M. (2022). Conceptual provisions of the mechanism of reflexive management of consumer behaviour in the marketing activities of enterprises. Economics of Industry, 1(97), 103—117 [in Ukrainian]. https://doi.org/10.15407/econindustry2022.01.103
Lefebvre, V., Schwartz, R. (2014). An Empirical Ratio in Search of a Theory. The American psychologist, 69, 634—635. https://doi.org/10.1037/a0036949.
Lefebvre, V. (2017). Bipolar choice in the experimental chamber. Bipolar Disord: Open Access, 3(1), 115. https://doi.org/10.4172/2472-1077.1000115
Oleksiuk, O., & Shafalyuk, O. (2023). Management of pharmaceutical online retail through a regional marketplace with neural network and statistical analytical tools. Neuro-Fuzzy Modeling Techniques in Economics, 12, 155—174. https://doi.org/10.33111/nfmte.2023.155
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Copyright Notice Authors published in the journal “Science and Innovation” agree to the following conditions: Authors retain copyright and grant the journal the right of first publication. Authors may enter into separate, additional contractual agreements for non-exclusive distribution of the version of their work (article) published in the journal “Science and Innovation” (for example, place it in an institutional repository or publish in their book), while confirming its initial publication in the journal “Science and innovation.” Authors are allowed to place their work on the Internet (for example, in institutional repositories or on their website).

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.