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

TL;DR

This work tackles the challenge of translating AI-discovered chemical innovations into market adoption by proposing an agentic, multimodal advertising framework for hyperpersonalized campaigns in competitive B2B and B2C settings. It introduces MAAMS (Multimodal Agentic Advertisement Market Survey), PAG (Personalized Market-Aware Targeted Advertisement Generation), and CHPAS (Competitive Hyper-Personalized Advertisement System) orchestrated by a Meta-Agent and powered by Retrieval-Augmented Generation ($RAG$) and LLMs to synthesize market intelligence, generate persona-specific ads, and differentiate competing products. Validation combines real-world data from 50+ FMCG companies with 2,000+ products and synthetic simulations via a Simulated Humanistic Colony of Agents to model personas and test strategies offline, with CTR and Ad Quality metrics and an Optimized RAG QA interface. The results show that hyper-personalized campaigns maximize engagement and $ROAS$ while preserving privacy and regulatory compliance, establishing a scalable blueprint for AI-driven marketing in high-stakes domains.

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.

Figures (31)

• Figure 1: AI framework architecture showing the main systems (MAAMS, PAG, CHPAS) and supporting features for chemical product advertising optimization.
• Figure 2: The MAAMS system, led by a Meta-Agent, collects data from Text, Image, Video, Finance, and Market agents to analyze brand sentiment, visual identity, emotional engagement, financial performance, and market trends. It compiles these insights into a comprehensive report on a product’s overall performance, marketing effectiveness, financial health, and market standing.
• Figure 3: The figure illustrates the workflow of the PAG system, designed to generate personalized, multilingual advertisements tailored to diverse consumer personas. Leveraging multimodal agglomerated knowledge, a simulated humanistic colony of agents mimics consumer personas (e.g., Logical Strategist, Visionary Trailblazer) to align ads with specific preferences, such as innovation, sustainability, or emotional connections. The Adv Curator Agent creates personalized multilingual advertisements, ensuring cultural and linguistic appropriateness for global relevance and engagement. The Social Media Agent further optimizes these ads for platforms like Twitter, Instagram, and Facebook, maximizing their impact across diverse audiences.
• Figure 4: The figure demonstrates the PAG system for creating customized ads tailored to diverse consumer personas. Using multimodal agglomerated knowledge, the Adv Curator Agent evaluates individual preferences, cultural contexts, and feedback to produce personalized multilingual advertisements for specific consumer segments. This approach ensures that the ads are engaging, emotionally resonant, and effective in driving consumer action, highlighting their value for modern personalized advertising.
• Figure 5: The figure demonstrates the generation of hyper-personalized advertisements for competing products within the same category from different manufacturers, tailored to a specific consumer persona. Each advertisement emphasizes unique selling points—such as affordability, functionality, freshness, and trendy appeal—while aligning with the persona's preferences, lifestyle, and shopping behaviors. The system strategically highlights each product's competitive advantages, ensuring relevance and engagement for the target audience. This approach showcases the system’s ability to create differentiated advertisements for competing products, effectively positioning each manufacturer’s unique strengths while maintaining personalization and platform optimization.