Using machine learning to improve brand management in an online environment
| dc.contributor.author | Iankovets, Tetiana | |
| dc.contributor.author | Järvis, Marina | |
| dc.contributor.author | Fomin, Olexander | |
| dc.contributor.author | Chernobrovkina, Svitlana | |
| dc.contributor.author | Shvets, Sofia | |
| dc.date.accessioned | 2025-11-03T11:25:12Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | The artifiial intelligence (AI) solutions are used to personalize content, analyse consumer behaviour, and automate marketing processes. Studying the impact of machine learning (ML) on brand management (BM) helps to understand its role in improving the companies’ competitiveness in the global market. The aim of the study is to assess the ML’s impact on the BM effctiveness of leading companies from diffrent countries for 2020-2023. The research employed the following methods: econometric methods, including multiple linear regression, panel data analysis, and comparative analysis of BM effctiveness between companies from diffrent countries. The study confimed the signifiant ML’s impact on BM effctiveness. Companies that actively use AI have higher social reach and positive reviews. Adidas demonstrates the highest BM Score (99.21), confiming the effctiveness of ML strategies in marketing. Amazon (85.51) and Apple (86.81) also have stable results due to personalized content and analysis of customer behaviour. Alibaba leads in social engagement (SE = 16.87%), which helps to engage customers. Burberry (PR = 68.40%) and Almarai (PR = 70.66%) have high levels of positive reviews, increasing consumer trust. Faster response times improve customer loyalty. It was found that companies that invest in content personalization and consumer behaviour analysis achieve better fiancial results and higher customer loyalty. Investment in social interaction and fast processing of customer requests are positively correlated with the overall success of the brand. The uniqueness of the study is that the proposed model quantitatively assesses the impact of individual factors on the BM effctiveness. Acomparative analysis of the effctiveness of AI-based BM between selected countries was conducted for the fist time. Further research can focus on analysing the long-term effcts of implementing ML in BM. An important direction is to assess the AI role in shaping consumer behaviour. As well as studying the impact of AI algorithms on the fiancial performance of companies in various sectors of the economy. | |
| dc.identifier.citation | Using machine learning to improve brand management in an online environment / Tetiana Iankovets, Marina Järvis, Olexander Fomin [et al.] // International Review of Management and Marketing. – 2025. – Vol. 15, Iss. 6. – P. 118-125. | |
| dc.identifier.doi | https://doi.org/10.32479/irmm.20649 | |
| dc.identifier.uri | https://repository.kpi.kharkov.ua/handle/KhPI-Press/94765 | |
| dc.language.iso | en | |
| dc.publisher | EconJournals | |
| dc.subject | machine learning | |
| dc.subject | brand management | |
| dc.subject | content personalization | |
| dc.subject | marketing strategies | |
| dc.subject | social outreach | |
| dc.subject | artifiial intelligence | |
| dc.title | Using machine learning to improve brand management in an online environment | |
| dc.type | Article |
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