Designing a Hyper-Personalized Marketing Model for Clean Fuel Campaigns Using a Grounded Theory Approach

Abstract

In the last decade, personalized marketing, and particularly hyper-personalized models, have faced multiple challenges in the field of energy and clean fuels. The first major challenge is the lack of comprehensive and adaptive models for predicting customer behavior and designing targeted campaigns that enhance engagement, loyalty, and personalized purchasing experiences. Moreover, existing studies have primarily been conducted in general industries or traditional e-commerce, while there is a clear lack of comparative research in the field of clean energy and the environment. The purpose of this study is to design a hyper-personalized marketing model for clean fuel campaigns by utilizing a grounded theory approach and analyzing customer behavior to optimize customer experience, loyalty, and the effectiveness of digital advertising. This model, with a focus on integrating behavioral, psychological, and contextual data, enables advanced and targeted personalization. The research methodology is mixed-method and grounded theory. In the qualitative phase, behavioral and psychological analyses of customers, along with interviews with digital marketing experts, were conducted, and data coding was performed to extract patterns. In the quantitative phase, big data from consumers and social networks were analyzed, and artificial intelligence–based clustering and predictive algorithms were employed. The findings indicate that integrating behavioral and psychological data with artificial intelligence predictive models significantly enhances personalized purchasing experiences and customer loyalty. Furthermore, the use of targeted and context-based advertising increases campaign effectiveness while reducing marketing costs. The proposed model also allows for expansion in other clean energy and environmental industries and largely bridges the existing research gaps in deep personalization and long-term ROI.