This article develops effective methods to boost consumer foot traffic in shopping malls using event marketing and AI technologies.
The article examines the theoretical, methodological, and practice-oriented foundations for the development and testing of a methodology for attracting consumer foot traffic to shopping malls based on the integration of event marketing, social media marketing, and the analytical capabilities of artificial intelligence technologies. The relevance of the study is substantiated in the context of growing competition between offline and online environments, which necessitates the implementation of new audience engagement models by shopping malls. The study reveals the specifics of event marketing as a means of establishing emotional connections between a brand and the consumer, its synergy with social media promotion, and the role of AI in generating personalized content and targeted communication. The structural components of the methodology are identified; an original conceptual model and process-based algorithm are proposed, enabling the implementation of marketing activities based on the behavioral, cognitive, and emotional characteristics of the target audience. The practical testing of the methodology was conducted using the Northwoods Mall (Peoria, USA) case, with the Placer AI platform. Following the Peoria Comic & Toy Show event and its targeted social media promotion, foot traffic increased by 42,8% compared to the previous weekend and by 6,8% compared to the same period the previous year. The average visit duration also rose by 14%. Empirical data confirms the viability of the developed approach. The scientific novelty of the study lies in substantiating and integrating event and digital marketing with AI tools within the framework of offline space promotion. The theoretical significance is reflected in the systematization and clarification of the principles of integrative marketing of shopping malls under digital transformation, based on event marketing and social media promotion using AI technologies. The practical significance lies in offering a reproducible methodology applicable to various commercial facilities.
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Lementueva et al. (2025) studied this question.
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