MODERN METHODS OF CONDUCTING MARKETING RESEARCH
Abstract
The article examines modern methods of conducting marketing research, which play a crucial role in the process of making strategic and tactical business decisions. Given the development of digital technologies and changes in consumer behavior, traditional approaches to marketing research are being transformed, giving way to innovative methodologies based on big data analysis, artificial intelligence, social media, and neuromarketing.
The article provides a detailed examination of the main approaches to marketing research: the classical approach (systematic collection and analysis of information for decision-making), the analytical approach (a scientific method for forecasting market trends), the applied approach (a practical tool for business), the systemic approach (integration of research into the company's overall strategy), and the innovative-digital approach (utilization of modern technologies). The characteristics of each approach and their relevance in various business situations are described.
The key methods of conducting marketing research have been identified, including desk research (analysis of secondary data, statistical reports, and publications) and field research (surveys, observations, focus groups, and product testing). Special attention is given to innovative methods, which encompass the use of Big Data, A/B testing, user behavior analysis on the Internet, social media analytics, and the application of VR/AR technologies for modeling consumer behavior.
The main tasks addressed through marketing research have been examined, including market analysis, consumer studies, competitor research, evaluation of marketing effectiveness, forecasting and planning, and product or service assessment. It has been determined that the use of modern research methods enables the acquisition of more accurate and up-to-date information for making effective managerial decisions.
The marketing research process in the article is divided into several stages: problem identification and research objectives, plan development, hypothesis testing, data collection, data analysis, conclusion formulation, and recommendation development. An improved marketing research process is proposed, integrating both traditional and digital methods to enhance the effectiveness of marketing strategies and the accuracy of market trend forecasting.
The research findings confirm the importance of utilizing modern marketing methods to enhance company competitiveness, personalize marketing campaigns, improve consumer engagement, and forecast market changes. The adoption of innovative approaches facilitates faster business adaptation to a dynamic environment and more effective management of marketing processes.
Downloads
References
Al-Surmi, A., Bashiri, M., & Koliousis, I. (2021). AI-based decision making: Combining strategies to improve operational performance. International Journal of Production Research, 60, 4464–4486. doi: https://doi.org/10.1080/00207543.2021.1966540
Babatunde, N. S. O., Odejide, N. O. A., Edunjobi, N. T. E., & Ogundipe, N. D. O. (2024). The role of AI in marketing personalization: A theoretical exploration of consumer engagement strategies. International Journal of Management & Entrepreneurship Research, 6(3), 936–949. doi: https://doi.org/10.51594/ijmer.v6i3.964
Chen, H., Chan-Olmsted, S., Kim, J., & Sanabria, M. I. (2022). Consumers' perception of artificial intelligence applications in marketing communication. Qualitative Market Research, 25(1), 125–142. doi: https://doi.org/10.1108/QMR-03-2021-0040
Amons, S. E., & Krasnyak, O. P. (2020). Market research of the market and its structural elements: Theoretical aspect. Naukovy Visnyk Polissia, 5, 106–114. doi: https://doi.org/10.32702/2307-2105-2020.5.106 (in Ukrainian)
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(1), 24–42. doi: https://doi.org/10.1007/s11747-019-00696-0
Grechanyk, N., & Manzevyta, D. (2021). Features of marketing research on consumer behavior during the COVID-19 crisis. Hrail Nauky. doi: https://doi.org/10.32702/2307-2105.2024.8.36 (in Ukrainian)
Brei, V. A. (2020). Machine learning in marketing. Foundations and Trends in Marketing, 14(3), 173–236. doi: https://doi.org/10.1561/1700000065
Afshar, P., Jalili, M., & Aghighi, A. (2022). Providing a neuromarketing intelligent model to convert potential market to actual market in the IRAN mushroom and its conversion industries by grounded theory. Business Intelligence Management Studies, 11(41), 155–187. doi: https://doi.org/10.22054/IMS.2022.65973.2120
Budnykevych, I. M., Cherdantseva, I. H., & Krupenna, I. A. (2020). Innovative tools for marketing research in the marketing of goods and communication policies of trade establishments. Ekonomika ta Pravo, 2(57). doi: https://doi.org/10.15407/econlaw.2020.02.066 (in Ukrainian)
Hoyer, W. D., Kroschke, M., Schmitt, B., Kraume, K., & Shankar, V. (2020). Transforming the customer experience through new technologies. Journal of Interactive Marketing, 51, 57–71. doi: https://doi.org/10.1016/j.intmar.2020.04.001
Amiri, A. M., Kushwaha, B. P., & Singh, R. (2023). Visualisation of global research trends and future research directions of digital marketing in small and medium enterprises using bibliometric analysis. Journal of Small Business and Enterprise Development, 30(3), 621–641. doi: https://doi.org/10.1108/JSBED-04-2022-0206
Kumar, V., Ramachandran, D., & Kumar, B. (2021). Influence of new-age technologies on marketing: A research agenda. Journal of Business Research, 125, 864–877. doi: https://doi.org/10.1016/j.jbusres.2020.01.007
Li, S., Peng, G., Xing, F., Zhang, J., & Zhang, B. (2021). Value co-creation in industrial AI: The interactive role of B2B supplier, customer and technology provider. Industrial Marketing Management, 98, 105–114. doi: https://doi.org/10.1016/j.indmarman.2021.07.015
Keegan, B. J., Canhoto, A. I., & Yen, D. A.-W. (2022). Power negotiation on the tango dancefloor: The adoption of AI in B2B marketing. Industrial Marketing Management, 100, 36–48. doi: https://doi.org/10.1016/j.indmarman.2021.11.001
Aulia, F., Adviorika, U. C. Y., Fahlevi, M., Prabowo, H., & Muchardie, B. G. (2021). Analysis of chatbot program features towards customer satisfaction in the era of digitalisation. Proceedings of 2021 International Conference on Information Management and Technology, ICIMTech 2021, 604–607. doi: https://doi.org/10.1109/ICIMTech53080.2021.9535034
Kukhta, Y. A., Voskonyuk, S. E., Rozhnova, Y. S., Derzhypilskyi, A. I., & Kovshova, I. O. (2020). The impact of Big Data on marketing research in Ukraine. Young Scientist, 5(81), 163. doi: https://doi.org/10.32839/2304-5809/2020-5-81-35 (in Ukrainian)
Mnykh, O., & Dovhun, O. (2023). Specialization of marketing research in the innovative development of global companies. Adaptive Management: Theory and Practice. Series Economics, 17(34). doi: https://doi.org/10.33296/2707-0654-17(34)-02 (in Ukrainian)
Purnomo, A., Asitah, N., Firdausi, N., Putra, S. W., & Raya, M. K. F. (2021). A study of digital marketing research using bibliometric analysis. Proceedings of 2021 International Conference on Information Management and Technology, ICIMTech (рр. 807–812). doi: https://doi.org/10.1109/ICIMTech53080.2021.9535086
Nadeak, T., Rahayu, A., & Hendrayati, H. (2024). Innovative marketing strategies in the digital era: Systematic literature review analysis. West Science Social and Humanities Studies, 2(01). doi: https://doi.org/10.58812/wsshs.v2i01.5499
Paschen, J., Paschen, U., Pala, E., & Kietzmann, J. (2021). Artificial intelligence (AI) and value co-creation in B2B sales: Activities, actors and resources. Australasian Marketing Journal, 29(3), 243–251. doi: https://doi.org/10.1016/j.ausmj.2020.06.004
This work is licensed under a Creative Commons Attribution 4.0 International License.
