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

Deepsona: An Agent-Based Framework for Multi-Trait Synthetic Audiences in Market Research | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Method Article Deepsona: An Agent-Based Framework for Multi-Trait Synthetic Audiences in Market Research M.Malukas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8212512/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. Marketing Operations Research synthetic audiences agent-based modeling consumer behavior prediction market research methodology AI personas multi-trait simulation behavioral economics concept testing Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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