Deepsona: An Agent-Based Framework for Multi-Trait Synthetic Audiences in Market Research

Traditional market research methods face significant limitations in cost, speed, and scalability when evaluating product concepts, pricing strategies, and marketing messages before market launch. This paper introduces a novel agent-based framework for generating synthetic consumer populations that produce high-fidelity behavioral predictions aligned with real market patterns. Unlike single-profile persona simulations or role-based chatbot approaches, our system constructs populations of AI agents with multi-dimensional trait configurations including demographic, psychographic, and behavioral attributes. We validate this approach through two retrospective studies comparing synthetic audience responses against peer-reviewed empirical research: a USDA-commissioned study on country-of-origin labeling (n=4,834) and a cross-cultural organic food preference study. Results demonstrate quantitative alignment with observed human behavioral patterns, with synthetic populations reproducing directional effects, segment-level heterogeneity, and relative magnitude differences across conditions. The framework employs a six-agent architecture comprising persona generation, controlled exposure, inter-segment deliberation, multi-dimensional scoring, quality assurance, and insight synthesis. Population-level aggregation with calibration weighting produces stable estimates suitable for early-stage concept testing, pricing optimization, and message refinement. This methodology offers researchers and practitioners a complementary tool for rapid directional insight generation prior to large-scale human studies, with applications in product development, market entry strategy, and advertising optimization.