Strategic management of omnichannel marketing using generative artificial intelligence

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dc.contributor.authorKovalchuk S.
dc.contributor.authorChunikhina T.
dc.contributor.authorGrigoryan V.
dc.contributor.authorShevchenko V.
dc.contributor.authorKoroleva U.
dc.date.accessioned2026-07-10T14:38:58Z
dc.date.available2026-07-10T14:38:58Z
dc.date.issued2025-10-30
dc.description.abstractThe relevance of the issue under research is determined by the rapid introduction of generative artificial intelligence (GenAI) into omnichannel marketing strategies. This radically changes approaches to personalization, communication, and strategic management in the digital economy. There is a growing need for quantitative analysis of the impact of such technologies on marketing efficiency, which justifies the need for this study. Particular attention is paid to personalization, communication automation, and data integration. The aim of the study is to determine the relationship between the level of implementation of GenAI and marketing efficiency. The problem is the lack of quantitative assessments of the impact of artificial intelligence (AI) on omnichannel management indicators. The analysis covers 10 companies from 10 countries for 2022-2024. An econometric model with six variables was applied: GAI, AdSpend, Data Integration, CSAT, OMDEPTH, and BRAND. The highest OM_EFF values in 2024 were recorded at Bosideng (98.3), Woolworths (97.0), and Zara (96.9). Companies with high GenAI have seen faster OM_EFF growth and market adaptation. Woolworths’ GenAI increased from 0.58 to 0.77, OM_EFF from 85.7 to 97.0. Hudson’s Bay showed the smallest changes because of limited digitalization and weak AI development. The study found that a high level of integration of GenAI is directly related to the increase in omnichannel marketing efficiency (OM_EFF). Bosideng achieved the highest OM_EFF – 98.3 in 2024 – with the maximum value of the GenAI Index (0.83) and the active use of personalized content. The obtained data confirm that GenAI enhances the effectiveness of marketing strategies through automation, deep data integration and multi-channel interaction with customers. The article provides practical recommendations for increasing OM_EFF. The study has applied significance for retail trade strategies. Further research may include other industries and regions.
dc.identifier.citationUlrich's Periodicals Directory, ReserachGate, Scimago, Google & Google Scholar, Schementic Scholar, The Index of Information Systems Journals, Information Technology Resources Collection, ZDNet Australia, Computing Research and Education Association of Australasia, Elsevier SCOPUS
dc.identifier.issnE-ISSN 1817-3195 / ISSN 1992-8645
dc.identifier.urihttps://ir.duan.edu.ua/handle/123456789/6648
dc.language.isoen
dc.publisherJournal of Theoretical and Applied Information Technology
dc.relation.ispartofseriesVol.103. No.22
dc.subjectOmnichannel Marketing
dc.subjectGenerative AI
dc.subjectEfficiency
dc.subjectStrategic Management
dc.subjectCustomer Personalization
dc.subjectDigital Transformation
dc.subjectEconometric Model
dc.titleStrategic management of omnichannel marketing using generative artificial intelligence
dc.typeArticle

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