How can LLMs create personalized ads in competitive markets?
AGENTIC MULTIMODAL AI FOR HYPER-PERSONALIZED B2B AND B2C ADVERTISING IN COMPETITIVE MARKETS: AN AI-DRIVEN COMPETITIVE ADVERTISING FRAMEWORK
April 2, 2025
https://arxiv.org/pdf/2504.00338This paper introduces an AI-driven framework for creating hyper-personalized and competitive advertisements, particularly for chemical products. It uses a multi-agent system where different agents gather market intelligence (text, images, video, finance, market data), generate personalized multilingual ads tailored to various simulated consumer personas (e.g., "Logical Strategist," "Visionary Trailblazer"), and optimize these ads for competing products from different manufacturers.
Key points for LLM-based multi-agent systems:
- Agentic architecture with LLMs: The framework utilizes LLMs as the core intelligence for each agent, enabling sophisticated multimodal data processing and ad generation.
- Simulated Humanistic Colony of Agents: Instead of real-world A/B testing, a simulated environment is used to model diverse consumer personas and test ad performance in various market scenarios, maintaining privacy and reducing costs.
- Adaptive persona-based targeting: Ads are tailored to individual personas by leveraging LLM's ability to generate diverse and culturally relevant content.
- Competitive ad optimization: The system emphasizes unique selling points for competing products, maximizing engagement and preventing market cannibalization.
- Retrieval-Augmented Generation (RAG): An optimized RAG system is used to answer user queries about technical product information, leveraging the gathered market intelligence. This has potential implications for user interfaces and information retrieval in multi-agent web apps.
- Synthetic experimentation: The framework uses synthetic data and simulated scenarios to train and optimize the AI models, enabling scalable and controlled testing. This approach can be valuable for developing robust multi-agent systems.