Agentic Multimodal AI for Hyperpersonalized B2B and B2C Advertising in Competitive Markets: An AI-Driven Competitive Advertising Framework

2025-04-01

AI Summary

To address challenges of insufficient personalization, weak cultural adaptation, and privacy compliance in dynamic B2B/B2C advertising markets, this paper proposes a multilingual, multimodal advertising agent framework. Methodologically, it introduces a novel synergistic paradigm integrating retrieval-augmented generation (RAG), multimodal large language model reasoning, in-context learning (ICL), and adaptive persona-driven targeting; it further constructs a synthetic agent community that simulates real consumer behavior to enable privacy-preserving, large-scale strategy optimization. Contributions include: (1) the first integration of persona modeling and multimodal reasoning into the advertising content generation pipeline; (2) support for hyper-personalized, cross-cultural content generation grounded in diverse linguistic and sociocultural contexts; and (3) empirically validated improvements in user engagement and return on ad spend (ROAS) in real-product experiments, effectively mitigating market cannibalization.

Technology Category

• Multiagent Systems: Adversarial Agents
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Search and Retrieval-Augmented AI: Ad search and search for Web retail
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

The growing use of foundation models (FMs) in real-world applications demands adaptive, reliable, and efficient strategies for dynamic markets. In the chemical industry, AI-discovered materials drive innovation, but commercial success hinges on market adoption, requiring FM-driven advertising frameworks that operate in-the-wild. We present a multilingual, multimodal AI framework for autonomous, hyper-personalized advertising in B2B and B2C markets. By integrating retrieval-augmented generation (RAG), multimodal reasoning, and adaptive persona-based targeting, our system generates culturally relevant, market-aware ads tailored to shifting consumer behaviors and competition. Validation combines real-world product experiments with a Simulated Humanistic Colony of Agents to model consumer personas, optimize strategies at scale, and ensure privacy compliance. Synthetic experiments mirror real-world scenarios, enabling cost-effective testing of ad strategies without risky A/B tests. Combining structured retrieval-augmented reasoning with in-context learning (ICL), the framework boosts engagement, prevents market cannibalization, and maximizes ROAS. This work bridges AI-driven innovation and market adoption, advancing multimodal FM deployment for high-stakes decision-making in commercial marketing.

Problem

• Develop adaptive AI-driven advertising for dynamic B2B/B2C markets
• Create hyper-personalized ads using multimodal reasoning and RAG
• Optimize ad strategies safely without risky real-world A/B testing

Innovation

• Multimodal AI framework for hyperpersonalized advertising
• Integrates RAG, multimodal reasoning, and adaptive targeting
• Uses simulated agents for privacy-compliant strategy optimization