Marketing mix rebuilding for the age of generative AI: From 7P to 7G marketing mix model

Existing marketing mix including the 4Ps and 7Ps were developed for human-designed offerings, linear communication flows, and stable market structures. However, markets increasingly operate through algorithmic mediation, synthetic content production, autonomous optimization, and self-evolving customer experiences. This paper proposes the Generative Marketing Mix (GMM) of 7Gs as the first theoretical expansion of the 7Ps for AI dominated markets. The paper positions the 7G Generative Marketing Mix as the necessary next stage in the conceptual evolution of marketing frameworks. Whereas the 4Ps reflected industrial logic and the 7Ps reflected service logic, the 7G model reflects generative logic. Based on service dominant logic, generativity theory, algorithmic value formation, and post-digital consumer behaviour, we reconceptualize product, price, place, promotion, people, process, and physical evidence into seven new generative counterparts: Generative offer models, algorithmic value calibration, autonomous delivery ecosystems, synthetic persuasion systems, human AI collaborative agents, self-evolving experience flows, and virtualized trust signals. A hybrid conceptual empirical modeling approach is adopted, incorporating mathematical formalization of generative capability interactions and a simulated dataset to demonstrate construct dimensionality and discriminant validity. The proposed framework identifies structural gaps in existing marketing theory, articulates propositions for future empirical testing, and outlines how generative systems reshape strategic decision-making, market signaling, and competitive advantage.