Keywords:
marketing
chatbot
predictive analytics
digitisation
automated mailings
business

  • Abstract

    Thanks to its capabilities, artificial intelligence (AI) contributes to business development by attracting customers and investors. However, the wide spread of various AI technology software complicates understanding its concept and reduces its implementation in local businesses. Our research was devoted to the development of an algorithm for building marketing strategies with the use of AI based on the definition of the concept, functions, principles of action and assessment of the perception of digitalisation by consumers for the application of a personalised approach to interaction with the client and improving communication. The results confirmed the low awareness of the population, including entrepreneurs, about the possibilities of AI. We identified the main functions, concepts, and types of AI and provided examples of their application. We explored ways to personalise marketing and improve communication based on the achievement of trust criteria, customer satisfaction, ease of purchase, fulfilment of seller obligations, quality support service, security of personal data, and creation of recommended offers based on the analysis of data obtained during communication with the consumer. To achieve these criteria, recommendations are given for using different types of AI: chatbots, automated mailings, intelligent assistants, installation of filters and sorting programs, automatic recommendations and multifactor identification. The AI ​​algorithm created in this way to ensure the personalisation of marketing and quality communication will increase the implementation of digitalisation in the country's entrepreneurship and improve the economy.

  • References

    1. Arsenijevic, U., & Jovic, M. (2019). Artificial intelligence marketing: chatbots. In 2019 International Conference on Artificial Intelligence: Applications and Innovations (pp. 19–193). Belgrade, Serbia. https://doi.org/10.1109/IC-AIAI48757.2019.00010
    2. Borges, A. F., Laurindo, F. J., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International journal of information management, 57, art. no. 102225. https://doi.org/10.1016/j.ijinfomgt.2020.102225
    3. Chintalapati, S., & Pandey, S. K. (2022). Artificial intelligence in marketing: A systematic literature review. International Journal of Market Research, 64(1), 38–68. https://doi.org/10.1177/14707853211018428
    4. Chung, K., Thaichon, P., & Quach, S. (2022). Types of artificial intelligence (AI) in marketing management. In P. Thaichon & S. Quach (Eds.), Artificial intelligence for marketing management (pp. 29–40). Routledge.
    5. Crawford, K. (2019). Halt the use of facial-recognition technology until it is regulated. Nature, 572(7771), 565–566. https://doi.org/10.1038/d41586-019-02514-7
    6. Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24–42. https://doi.org/10.1007/s11747-019-00696-0
    7. De Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K. U., & Von Wangenheim, F. (2020). Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive Marketing, 51(1), 91–105. https://doi.org/10.1016/j.intmar.2020.04.007
    8. Ethics guidelines for trustworthy AI. (2019). European Commission. https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai. Accessed on June 20, 2024.
    9. Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627-660.
    10. Helm, J. M., Swiergosz, A. M., Haeberle, H. S., Karnuta, J. M., Schaffer, J. L., Krebs, V. E., & Ramkumar, P. N. (2020). Machine learning and artificial intelligence: definitions, applications, and future directions. Current reviews in musculoskeletal medicine, 13, 69–76. https://doi.org/10.1007/s12178-020-09600-8
    11. https://doi.org/10.5465/annals.2018.0057
    12. Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30–50. https://doi.org/10.1007/s11747-020-00749-9
    13. Hwang, G. J., & Chien, S. Y. (2022). Definition, roles, and potential research issues of the metaverse in education: An artificial intelligence perspective. Computers and Education: Artificial Intelligence, 3, art. no. 100082. https://doi.org/10.1016/j.caeai.2022.100082
    14. Jarek, K., & Mazurek, G. (2019). Marketing and artificial intelligence. Central European Business Review, 8(2). http://doi.org/10.18267/j.cebr.213
    15. Kelly, S., Kaye, S. A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, art. no. 101925. https://doi.org/10.1016/j.tele.2022.101925
    16. Khan, R.U., Saienko, V., & Tolchieva, H. (2021). Dependence of the company’s reputation and the quality of customer relations. Economic Studies journal, 2, 159-176.
    17. Khatniuk, N., Shestakovska, T., Rovnyi, V., Pobiianska, N., & Surzhyk, Y. (2023). Legal principles and features of artificial intelligence use in the provision of legal services. Journal of Law and Sustainable Development, 11(5), art. no. e01173. https://doi.org/10.55908/sdgs.v11i5.1173
    18. Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalised engagement marketing. California Management Review, 61(4), 135–155. https://doi.org/10.1177/0008125619859317
    19. Kussainov, K., Goncharuk, N., Prokopenko, L., Pershko, L., Vyshnivska, B., & Akimov, O. (2023). Anti-corruption management mechanisms and constructing a security landscape in the financial sector of the EU economic system against the background of challenges to European integration: Implications for artificial intelligence technologies. Economic Affairs, 68(1), 509–521. https://doi.org/10.46852/0424-2513.1.2023.20
    20. Lins, S., Pandl, K. D., Teigeler, H., Thiebes, S., Bayer, C., & Sunyaev, A. (2021). Artificial intelligence as a service: classification and research directions. Business & Information Systems Engineering, 63, 441–456. https://doi.org/10.1007/s12599-021-00708-w
    21. Manesh, M. F., Pellegrini, M. M., Marzi, G., & Dabic, M. (2020). Knowledge management in the fourth industrial revolution: Mapping the literature and scoping future avenues. IEEE Transactions on Engineering Management, 68(1), 289–300. https://doi.org/10.1109/TEM.2019.2963489
    22. Nikitas, A., Michalakopoulou, K., Njoya, E. T., & Karampatzakis, D. (2020). Artificial intelligence, transport and the smart city: Definitions and dimensions of a new mobility era. Sustainability, 12(7), art. no. 2789. https://doi.org/10.3390/su12072789
    23. Pantano, E., & Scarpi, D. (2022). I, robot, you, consumer: Measuring artificial intelligence types and their effect on consumers emotions in service. Journal of Service Research, 25(4), 583–600 https://doi.org/10.1177/10946705221103538
    24. Peyravi, B., Nekrošienė, J., & Lobanova, L. (2020). Revolutionised technologies for marketing: Theoretical review with a focus on artificial intelligence. Business: Theory and Practice, 21(2), 827–834. https://doi.org/10.3846/btp.2020.12313
    25. Potwora, M., Zakryzhevska, I., Mostova, A., Kyrkovskyi, V., & Saienko, V. (2023). Marketing strategies in e-commerce: personalised content, recommendations, and increased customer trust. Financial and Credit Activity: Problems of Theory and Practice, 5(52), 562–573. https://doi.org/10.55643/fcaptp.5.52.2023.4190
    26. Ruiz-Real, J. L., Uribe-Toril, J., Torres, J. A., & De Pablo, J. (2021). Artificial intelligence in business and economics research: Trends and future. Journal of Business Economics and Management, 22(1), 98–117. https://doi.org/10.3846/jbem.2020.13641
    27. Saienko, V., Mezentseva, I., Tolchieva, H., & Salkova, I. (2020). Efficiency of tourism operators’ and tourism agents’ activities in the market of tourist services of Ukraine. Journal of Environmental Management and Tourism, XI, Winter, 8(48), 1960-1966. https://doi.org/10.14505/jemt.v11.8(48).08
    28. Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), art. no. 100002. https://doi.org/10.1016/j.jjimei.2020.100002
    29. Vlacic, B., Corbo, L., e Silva, S. C., & Dabić, M. (2021). The evolving role of artificial intelligence in marketing: A review and research agenda. Journal of Business Research, 128, 187–203. https://doi.org/10.1016/j.jbusres.2021.01.055
    30. Yau, K. L. A., Saad, N. M., & Chong, Y. W. (2021). Artificial intelligence marketing (AIM) for enhancing customer relationships. Applied Sciences, 11(18), art. no. 8562. https://doi.org/10.3390/app11188562
    31. Zhao, J., & Zhu, C. (2023). Modeling and quantifying the impact of personified communication on purchase behavior in social commerce. Behavioral Sciences, 13(8), 627. https://doi.org/10.3390/bs13080627
Creative Commons License

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

Copyright (c) 2024 The Authors

How to cite

Kotyrlo, O., Naboka, R., Nestor, V., Tyshko, D., & Panasenko, O. (2024). Ways to use artificial intelligence to improve the personalisation of marketing strategies and improve the effectiveness of communication with consumers. Multidisciplinary Reviews, 8, 2024spe074. https://doi.org/10.31893/multirev.2024spe074
Crossref
Scopus
Google Scholar
Europe PMC
  • Article viewed - 755
  • PDF downloaded - 406