Integration of feeling AI tools to support marketing solutions in e-commerce
Published Online: Apr 28, 2025
Email:
chornous@univ.kiev.ua
Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
Email:
r.dimitrov@utp.bg
University of Telecommunications and Post, Sofia, Bulgaria
Email:
yfareniuk@gmail.com
Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
Email:
m.penkova@utp.bg
University of Telecommunications and Post, Sofia, Bulgaria
Email:
roman.nosko@knu.ua
Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
Views: 378
Downloads: 20
Download PDF
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:
JEL Classification:
L81, C6, C88, M3
How to cite:
Chornous, G., Dimitrov, R., Fareniuk, Y., Penkova, M., Nosko, R. (2025). Integration of feeling AI tools to support marketing solutions in e-commerce. Access to science, business, innovation in the digital economy, ACCESS Press, 6(2), 415-436, https://doi.org/10.46656/access.2025.6.2(10)
References:
-
Alaparthi, S. & Mishra, M. (2020). Bidirectional Encoder Representations from Transformers (BERT): A sentiment analysis odyssey. Retrieved from https://arxiv.org/pdf/2007.01127 (accessed: 08 March 2025)
-
Al-Hajjar, D. & Syed, A.Z. (2015). Applying sentiment and emotion analysis on brand tweets for digital marketing. 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), Amman, Jordan, pp. 1-6, doi: 10.1109/AEECT.2015.7360592
-
Antonova, D., Beloeva, S., Todorova, A. (2024). The dual impact of artificial intelligence: catalyst for innovation or threat to stability. Strategies for policy in science and education-strategii na obrazovatelnata i nauchnata politika. Vol. 32 (6s). pp.49-58. https://doi.org/10.53656/str2024-6s-4-dua
-
Beloeva, S., Venelinova, N. (2024). Digital Discrimination Risks in the Transformation of Higher Education. Strategies for policy in science and education-strategii na obrazovatelnata i nauchnata politika. Vol. 32 (5s). pp.98-108. https://doi.org/10.53656/str2024-5s-9-dig
-
Hutto, C. & Gilbert, E. (2014). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216-225, https://doi.org/10.1609/icwsm.v8i1.14550
-
Chaisatitkul, A., Luangngamkhum, K., Noulpum, K., & Kerdvibulvech, C. (2024). The power of AI in marketing: enhancing efficiency and improving customer perception through AI-generated storyboards. International Journal of Information Technology (Singapore), 16(1), 137–144, https://doi.org/10.1007/s41870-023-01661-5
-
Chinnasamy, P., Ayyasamy, R.K., Madhukar, G., Bhargav, T., & Nayak, D.Y.K. Comment Analyzer by Sentimental Analysis through Natural Language Processing. Proceedings of the 2024 10th International Conference on Communication and Signal Processing (ICCSP), 1123–1128, Melmaruvathur, India, https://doi.org/10.1109/ICCSP60870.2024.10544106
-
Cutler, K. (2024). The evolution of digital marketing in the era of AI. Applied Marketing Analytics, 10(1), 6–17
-
Dash, P., Mishra, J., & Dara, S. (2022). Sentiment Analysis on Social Network Data and Its Marketing Strategies: A Review. ECS Transactions, 107(1), 7417–7425, https://doi.org/10.1149/10701.7417ecst
-
Dorokhova, L., Kuusik, A., Dimitrov, R., Pentus, K., Dorokhov, O., & Petrova, M. (2023). Planning the digital marketing budget: computer modelling for decision making. Access to science, business, innovation in the digital economy, ACCESS Press, 4(2), 248-260, https://doi.org/10.46656/access.2023.4.2(7)
-
Gallastegui, L.M.G., Forradellas, R.R., & Alonso, S.L.N. (2024). Applying advanced sentiment analysis for strategic marketing insights: A case study of BBVA using machine learning techniques. Innovative Marketing, 20(2), 100–115, https://doi.org/10.21511/im.20(2).2024.09
-
Geetha, L. (2023). Vader: A Comprehensive Guide to Sentiment Analysis in Python. Retrieved from https://medium.com/@rslavanyageetha/vader-a-comprehensive-guide-to-sentiment-analysis-in-python-c4f1868b0d2e (accessed: 08 March 2025)
-
Georgieva-Tsaneva, G., Serbezova, I., & Beloeva, S. (2025). Application of Virtual Reality, Artificial Intelligence, and Other Innovative Technologies in Healthcare Education (Nursing and Midwifery Specialties): Challenges and Strategies. Education Sciences, 15(1), 11. https://doi.org/10.3390/educsci15010011
-
Golondrino, G.E.C., Alarcón, M.A.O., & Muñoz, W.Y.C. Proposal of an Automated Tool for the Application of Sentiment Analysis Techniques in the Context of Marketing. International Journal of Advanced Computer Science and Applications, 13(3), 396–402, https://doi.org/10.14569/IJACSA.2022.0130348
-
Gooljar, V., Issa, T., Hardin-Ramanan, S., & Abu-Salih, B. (2024). Sentiment-based predictive models for online purchases in the era of marketing 5.0: a systematic review. Journal of Big Data, 11(1), 107, https://doi.org/10.1186/s40537-024-00947-0
-
Huang, M. H. & Rust., R. (2020). A strategic framework for artificial intelligence in marketing. https://doi.org/10.1007/s11747-020-00749-9
-
Huang, M. H., Rust, R.T. (2018). Artificial intelligence in service. Journal of Service Research. https://doi.org/10.1177/1094670517752
-
Huang M. H., Rust R. T., & Maksimovic V. (2019). The feeling economy: Managing in the next generation of artificial intelligence (AI). California Management Review. https:///doi.org/10.1177/0008125619863436
-
IBM (2024). Natural language understanding. Retrieved from https://www.ibm.com/products/natural-language-understanding (accessed: 08 March 2025)
-
Islam, T., Miron, A., Nandy, M., Liu, X., & Li, Y. (2024). Transforming Digital Marketing with Generative AI. Computers, 13(7), 168, https://doi.org/10.3390/computers13070168
-
Jain, P., & Agarwal, R. (2023). Sentiment Analysis using SVM, Naïve Bayes, and LSTM. Proceedings of the IEEE 2023 5th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N 2023), Greater Noida, India, 436–439, https://doi.org/10.1109/ICAC3N60023.2023.10541381
-
Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2017). Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey. DOI: 10.48550/arXiv.1711.04305
-
Jindal, S., & Sharma, K. (2019). A review on sentiment classification: Natural language understanding. Recent Patents on Engineering, 13(1), 20–27, https://doi.org/10.2174/1872212112666180731113353
-
Kihlström, G. (2024). Using AI in marketing: An introduction Using AI in Marketing: An Introduction (book), 1–158. https://doi.org/10.1515/9781501519765
-
Kshetri, N., Dwivedi, Y.K., Davenport, T.H., & Panteli, N. (2023). Generative artificial intelligence in marketing: Applications, opportunities, challenges, and research agenda. International Journal of Information Management, 75, 102716, https://doi.org/10.1016/j.ijinfomgt.2023.102716
-
Kulshrestha, R. (2019). A Beginner’s Guide to Latent Dirichlet Allocation (LDA). Medium. Towards Data Science. Retrieved from https://towardsdatascience.com/latent-dirichlet-allocation-lda-9d1cd064ffa2 (accessed: 03 March 2025)
-
Lasi, M.A., Hamid, A.B.A., Jantan, A.H., Goyal, S.B., & Tarmidzi, N.N. (2024). Improving Digital Marketing Using Sentiment Analysis with Deep LSTM. Lecture Notes in Networks and Systems, 785, 217–232. https://doi.org/10.1007/978-981-99-6544-1_17
-
Lee, G.H., Lee, K.J., Jeong, B., & Kim, T. (2024). Developing Personalized Marketing Service Using Generative AI. IEEE Access, 12, 22394–22402, https://doi.org/10.1109/ACCESS.2024.3361946
-
Lexalytics (2024). Retrieved from https://www.lexalytics.com/ (accessed: 13 March 2025)
-
Lin, H.-C.K., Wang, T.-H., Lin, G.-C., Chen, H.-R., & Huang, Y.-M. (2020). Applying sentiment analysis to automatically classify consumer comments concerning marketing 4Cs aspects. Applied Soft Computing Journal, 97, https://doi.org/10.1016/j.asoc.2020.106755
-
Liu Y., Ott M., Goyal N., Du J., Joshi M., Chen D., Levy O., Lewis M., Zettlemoyer L., & Stoyanov V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. Retrieved from https://arxiv.org/pdf/1907.11692 (accessed: 13 March 2025)
-
McDuff, D., Czerwinski, M. (2018). Designing emotionally sentient agents. Communications of the ACM, Volume 61, Issue 12, pp. 74 – 83. https://doi.org/10.1145/318659
-
Medallia (2024). Real Time Text Analytics Software. Retrieved from https://www.medallia.com/platform/text-analytics/?utm_campaign=monkeylearnmigration (accessed: 08 March 2025)
-
Pandey, K.K., Thorat, M., Joshi, A., Hussein, A., & Alazzam, M.B. (2023). Natural Language Processing for Sentiment Analysis in Social Media Marketing. 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE 2023), Greater Noida, India, 326–330. https://doi.org/10.1109/ICACITE57410.2023.10182590
-
Peruchini, M., da Silva, G.M., & Teixeira, J.M. (2024). Between artificial intelligence and customer experience: a literature review on the intersection. Discover Artificial Intelligence, 4(1), 4, https://doi.org/10.1007/s44163-024-00105-8
-
Precedence Research (2024). Artificial Intelligence in E-commerce Market. Retrieved from https://www.precedenceresearch.com/artificial-intelligence-in-e-commerce-market (accessed: 08 March 2025)
-
Raj, A., Ganu, H., Das, S.K., Singh, S., & Vempati, S. Maya (2024). A Conversational Shopping Assistant for Fashion at Myntra. WSDM 2024 - Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 1178–1179, https://doi.org/10.1145/3616855.3635740
-
Rambocas, M., & Pacheco, B.G. (2018). Online sentiment analysis in marketing research: a review. Journal of Research in Interactive Marketing, 12(2), 146–163, https://doi.org/10.1108/JRIM-05-2017-0030
-
Rui, M., Sparacino, A., Merlino, V.M., Massaglia, S., & Blanc, S. (2025). Exploring consumer sentiments and opinions in wine E-commerce: A cross-country comparative study. Journal of Retailing and Consumer Services, 82, 104097, https://doi.org/10.1016/j.jretconser.2024.104097
-
Sánchez-Núñez, P., de las Heras-Pedrosa, C., & Peláez, J.I. (2020). Opinion mining and sentiment analysis in marketing communications: A science mapping analysis in web of science (1998-2018). Social Sciences, 9(3), 23. https://doi.org/10.3390/socsci9030023
-
Silvester, S., & Kurian, S. Recommendation Systems: Enhancing Personalization and Customer Experience. 2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON 2023), Bangalore, India, 2023, pp. 1-6, https://doi.org/10.1109/SMARTGENCON60755.2023.10442402
-
Singh, K.U., Kumar, A., Kumar, G., Singh, T., & Kotecha, K. (2024). Sentiment Analysis in Social Media Marketing: Leveraging Natural Language. Processing for Customer Insights Lecture Notes in Networks and Systems, 878, 457–467, https://doi.org/10.1007/978-981-99-9489-2_40
-
Tairov, I., Stefanova, N., Aleksandrova, A., Aleksandrov, M. (2024). Review of AI-Driven Solutions in Business Value and Operational Efficiency. Economics Ecology Socium, 8, 55-66. https://doi.org/10.61954/2616-7107/2024.8.3-5
-
Thakkar, A. (2024). The intriguing possibilities of generative AI in marketing. Advances in Business and Management, ISBN 979-889113933-6, Vol. 23, 161–178
-
Thakur, J., & Kushwaha, B.P. (2024). Artificial intelligence in marketing research and future research directions: Science mapping and research clustering using bibliometric analysis. Global Business and Organizational Excellence, 43(3), 139–155, https://doi.org/10.1002/joe.22233
-
Thomas, I. (2023). Using generative AI to turbocharge digital marketing. Applied Marketing Analytics, 9(3), 270–280, https://doi.org/10.69554/dxfn2668
-
Thukral, V., Latvala, L., Swenson, M., & Horn, J. (2023). Customer journey optimisation using large language models: Best practices and pitfalls in generative AI. Applied Marketing Analytics, 9(3), 281–292, https://doi.org/10.69554/dmiv5161
VIEW INDEXES
