Analysis of the project of a marketing campaign to promote robotic solutions using Random Forest classification

Authors

DOI:

https://doi.org/10.15587/2706-5448.2024.320367

Keywords:

project management, digital marketing, machine learning, robotics complex, competitiveness

Abstract

The object of research is marketing strategies for promotion in social networks, which are the basis for achieving the basic requirements of brands: audience commitment, brand loyalty, awareness, positioning, conversion and reputation. Because of this, a significant number of modern companies that manufacture robotic complexes are considering the possibility of implementing such strategies using a project approach.

The work is aimed at analyzing data and evaluating marketing campaigns for the promotion of robotic solutions, carried out using Random Forest classification, in order to identify patterns and increase the effectiveness of such campaigns. The analysis was conducted on the example of three advertising campaigns. The analysis showed how the criteria taken into account when displaying advertisements on social networks, namely the age category of a person, gender, interest group of a person (according to the public profile of the social network), the number of ad impressions affect the number of clicks on the corresponding advertisement. As well as the total number of people who became interested in the product after seeing the advertisement, the total number of people who bought the product after watching the advertisement. The essence of the results obtained is that the study showed the possibility of assessing the effectiveness of marketing campaigns at the early stages, the measurability of performance indicators in terms of audience reach, level of interaction and conversion into reverse actions. The results of the study reflect the complex relationship between the conversion indicators of advertising campaigns and the main criteria for their implementation, emphasizing the importance of a project approach and the use of machine learning for building marketing campaigns. The study focuses on practical aspects. From a practical point of view, mastering the basic metrics of data mining, segmentation, the ability to use A/B testing and the use of machine learning methods, in particular the Random Forest classification algorithm, allows to increase the effectiveness of campaigns. And also reduce the risks of losing money due to incorrect conclusions regarding the segmentation of target audiences. The results of the study can become the basis for the formation of new strategies for conducting marketing campaigns when promoting robotic systems, adjusting existing ones, capable of effectively and flexibly adapting depending on the target audience and the dynamics of working with it.

Author Biographies

Tatyana Vlasenko, State Bioengineering University

PhD, Associate Professor

Department of Production, Business and Management Organisation

Vitaliy Vlasovets, Lviv National University of Environmental Management

Doctor of Technical Sciences, Professor, Head of Department

Department of Mechanical Engineering

Oleksandra Kovalyshyn, Lviv National University of Life and Environmental Sciences

Doctor of Economic Sciences, Professor

Department of Land Cadastre

 

Oleksandra Bilovod, Poltava State Agrarian University

PhD, Associate Professor

Department of Mechanical and Electrical Engineering

References

  1. Lakshmi, D., Tyagi, A. K. (Eds.). (2024). Emerging Technologies and Security in Cloud Computing. Advances in Information Security, Privacy, and Ethics. Hershey: IGI Global. https://doi.org/10.4018/979-8-3693-2081-5
  2. Vijay, K., Gnanavel, S., Sowmia, K. R., Vijayakumar, R., Elsisi, M.; Lakshmi, D., Tyagi, A. K. (Eds.) (2024). Industry 4.0: Linking with Different Technologies – IoT, Big Data, AR and VR, and Blockchain. Advances in Information Security, Privacy, and Ethics. IGI Global, 395–421. https://doi.org/10.4018/979-8-3693-2081-5.ch017
  3. Jagatheesaperumal, S. K., Rahouti, M., Ahmad, K., Al-Fuqaha, A., Guizani, M. (2022). The Duo of Artificial Intelligence and Big Data for Industry 4.0: Applications, Techniques, Challenges, and Future Research Directions. IEEE Internet of Things Journal, 9 (15), 12861–12885. https://doi.org/10.1109/jiot.2021.3139827
  4. Dondapati, A., Sheoliha, N., Panduro-Ramirez, J., Bakhare, R., Sreejith, P. M., Devi Prasad Kotni, V. V. (2022). An integrated artificial intelligence framework for knowledge production and B2B marketing rational analysis for enhancing business performance. Materials Today: Proceedings, 56, 2232–2235. https://doi.org/10.1016/j.matpr.2021.11.557
  5. White, K., Cakanlar, A., Sethi, S., Trudel, R. (2025). The past, present, and future of sustainability marketing: How did we get here and where might we go? Journal of Business Research, 187, 115056. https://doi.org/10.1016/j.jbusres.2024.115056
  6. Culot, G., Podrecca, M., Nassimbeni, G. (2024). Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions. Computers in Industry, 162, 104132. https://doi.org/10.1016/j.compind.2024.104132
  7. Vlasenko, T., Glowacki, S., Vlasovets, V., Hutsol, T., Nurek, T., Lyktei, V., Efremenko, V., Khrunyk, Y. (2024). Increasing Exploitation Durability of Two-Layer Cast Mill Rolls and Assessment of the Applicability of the XGBoost Machine Learning Method to Manage Their Quality. Materials, 17 (13), 3231. https://doi.org/10.3390/ma17133231
  8. Vlasovets, V. (2023). Effect of various factors on the measurement error of structural components of machine parts materials microhardness using computer vision methods. Przegląd Elektrotechniczny, 1 (1), 325–331. https://doi.org/10.15199/48.2023.01.67
  9. de Jong, A., de Ruyter, K., Keeling, D. I., Polyakova, A., Ringberg, T. (2021). Key trends in business-to-business services marketing strategies: Developing a practice-based research agenda. Industrial Marketing Management, 93, 1–9. https://doi.org/10.1016/j.indmarman.2020.12.004
  10. Arnett, D. B., Wittmann, C. M., Hansen, J. D. (2021). A process model of tacit knowledge transfer between sales and marketing. Industrial Marketing Management, 93, 259–269. https://doi.org/10.1016/j.indmarman.2021.01.012
Analysis of the project of a marketing campaign to promote robotic solutions using Random Forest classification

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Published

2024-12-31

How to Cite

Vlasenko, T., Vlasovets, V., Kovalyshyn, O., & Bilovod, O. (2024). Analysis of the project of a marketing campaign to promote robotic solutions using Random Forest classification. Technology Audit and Production Reserves, 6(4(80), 43–50. https://doi.org/10.15587/2706-5448.2024.320367

Issue

Vol. 6 No. 4(80) (2024): Economics of enterprises. Macroeconomics

Section

Economics and Enterprise Management

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Copyright (c) 2024 Tatyana Vlasenko, Vitaliy Vlasovets, Oleksandra Kovalyshyn, Oleksandra Bilovod

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