Introduction to EcoPersona AI: Generative Consumer Personas Based on Carbon-Footprint Sensitivity
DOI:
https://doi.org/10.66635/zfwk6f58Keywords:
Green marketing, consumer behavior, carbon labeling, ISO 14067, AI personalization, sustainability communicationAbstract
Consumers today encounter a growing volume of sustainability-related marketing information, including carbon footprint labels, eco-scores, and product transparency dashboards. Yet, despite the global emphasis on sustainability, the behavioral impact of such information remains inconsistent. Many consumers acknowledge environmental issues but vary widely in how deeply they process, trust, or act on sustainability claims. This inconsistency highlights the need for a segmentation approach that goes beyond traditional demographic or psychographic boundaries.
This paper introduces EcoPersona AI - designed to classify consumers by their Carbon-Footprint Sensitivity (CFS)—a measure of how individuals perceive and respond to product-level sustainability data. The model uses verified environmental metrics such as ISO 14067-based product carbon footprints, coupled with behavioral interaction signals, to develop adaptive personas that guide message framing, creative design, and communication strategy.
Grounded in the Value-Belief-Norm (VBN) theory, the Theory of Planned Behavior (TPB), and Construal Level Theory (CLT), EcoPersona AI seeks to humanize the use of artificial intelligence in marketing by connecting behavioral science with measurable sustainability outcomes. The model aims to help mid-sized and large product-based businesses translate verified impact data into effective and credible communication strategies that increase comprehension, trust, and green product adoption.
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