Applying Marketing Innovation Models to Urban Events: A Comparative Study of the Ghriba Pilgrimage in Djerba

This article examines the applicability of mathematical models traditionally utilized in marketing innovation to the planning and analysis of urban events, with the Ghriba Pilgrimage in Djerba serving as a case study. Marketing innovation models, including the Bass Model, Rogers' Diffusion of Innovations, and Product Lifecycle Theory, offer invaluable frameworks for forecasting market behavior, comprehending consumer adoption processes, and formulating effective strategies. In this study, these models are adapted for the analysis of urban events, which, like marketing innovations, exhibit distinctive patterns of adoption and evolve through defined stages of the product lifecycle.Urban events, which encompass a range of activities from cultural festivals to public gatherings, exert a considerable influence on the social and economic dynamics of cities. Such events serve to drive tourism, foster community engagement, and stimulate local economies. The objective of this study is to apply marketing innovation models in order to predict and analyze the impact of these events on urban dynamics, with the aim of enhancing event planning, management, and evaluation.The paper presents a comparative analysis between marketing innovations and urban events, demonstrating parallels in their respective adoption processes and lifecycle stages. The Ghriba Pilgrimage provides an illustrative case study demonstrating the applicability of these models. The study employs text mining and natural language processing (NLP) techniques to examine social media sentiments and visitor reviews, thereby gaining insight into the emotional and experiential aspects of the event.The findings indicate that mathematical models from marketing can be effectively adapted to urban events, providing valuable insights into their planning and impact. The study highlights the significance of innovative and engaging experiences for event attendees and suggests that these models can inform strategic decisions in urban planning.