Integration of feeling AI tools to support marketing solutions in e-commerce

Galyna Chornous (), Radi Dimitrov (), Yana Fareniuk (), Milena Penkova () and Roman Nosko ()
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Galyna Chornous: Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
Radi Dimitrov: University of Telecommunications and Post, Sofia, Bulgaria
Yana Fareniuk: Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
Milena Penkova: University of Telecommunications and Post, Sofia, Bulgaria
Roman Nosko: Taras Shevchenko National University of Kyiv, Kyiv, Ukraine

Access Journal, 2025, vol. 6, issue 2, 415-436

Abstract: The use of AI allows you to increase the effectiveness of advertising activities, automate marketing strategies, making them relevant for each user, ensure a higher level of customer engagement, audience loyalty, and increase conversions. Also, AI technologies can be a tool for performing in-depth analysis and predicting consumer behavior for making informed marketing decisions. Thus, research on supporting marketing decisions in e-commerce based on AI technologies can become extremely important to ensure the sustainable development of online businesses in the digital economy. The goal of the study is to develop a conceptual approach to the features of the implementation and use of AI for e-commerce enterprises, in particular, the outline of an ecosystem of effective AI-based products that will contribute to solving the key tasks of planning and implementing marketing activities. In particular, this article highlights the key aspects of the implementation of feeling AI tools, which have significant potential in promoting mutual understanding between businesses of any level and consumers. Feeling AI can take a central place in the entire ecosystem of products that facilitate effective interaction with the consumer at various stages of the marketing activity of an e-commerce enterprise. As part of this research, we also use sentiment analysis tools to research user feedback, combined in a single software tool. The proposed conceptual approach was introduced by us in one of the European enterprises of the e-commerce market. The results demonstrated a significant improvement in marketing planning and customer experience management processes. The results of the study will be interesting, first of all, to companies operating in the field of e-commerce, marketers, and customer service managers, thanks to the proposed mechanisms for the implementation of the analysis of textual data of user reviews in order to obtain a better understanding of the trend of reviews, customer satisfaction levels, key topics being discussed, and identifying negative and positive sentiments that can help businesses improve products, services, and customer engagement strategies.

Keywords: sentiment analysis; e-commerce; modelling; machine learning; artificial intelligence; Python; NLP (search for similar items in EconPapers)
JEL-codes: C6 C88 L81 M3 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:aip:access:v:6:y:2025:i:2:p:415-436

DOI: 10.46656/access.2025.6.2(10)

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