AI-Driven Marketing in Agrifood: How Smart Technologies Shape Consumer Choices and Retail Strategies. A Critical Analysis of the Literature

Badea Ana-Maria (), Necsescu Maria-Bogdana () and Tzedec Betty Cohen- ()
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Badea Ana-Maria: Bucharest University of Economic Studies, Bucharest, Romania
Necsescu Maria-Bogdana: Bucharest University of Economic Studies, Bucharest, Romania
Tzedec Betty Cohen-: Bucharest University of Economic Studies, Bucharest, Romania

Proceedings of the International Conference on Business Excellence, 2025, vol. 19, issue 1, 3910-3922

Abstract: With the digitalization era, artificial intelligence (AI) is the primary catalyst of agri-food marketing strategies with a significant impact on consumers’ buying behavior and business plans of retailers. This article discusses the role of smart technologies towards optimizing the shopping experience, personalizing offers, and reducing the supply chain. One, collaborative filtering- and content-analysis-based recommendation schemes allow personalized marketing campaigns, demand forecasting, and customer satisfaction improvements. Two, machine learning-augmented dynamic price models provide real-time adaptive pricing for the retailer to achieve maximum profitability along with product offer availability. Three, applications of neural networks and deep learning by recommendation engines improve providing more accurate suggestions to optimize customer experience. AI also enables agri-food product traceability by utilizing blockchain, providing consumers with information and increasing product trust when they buy. Problems concerning ethics, privacy, and potential algorithmic price collusion are also identified by the study. By means of critical literature review, the article puts forward benefits of AI deployment in agri-food retailing and suggests directions for development. The findings emphasize the need for clear rules of the right application of AI, and continued potential for innovation in the domain.

Keywords: agri-food marketing; artificial intelligence; consumer behavior; machine learning; retailers and customers (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:poicbe:v:19:y:2025:i:1:p:3910-3922:n:1038

DOI: 10.2478/picbe-2025-0299

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