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

• URL: http://arxiv.org/abs/2504.00338v1
• Date: Tue, 01 Apr 2025 01:37:02 GMT
• Title: Agentic Multimodal AI for Hyperpersonalized B2B and B2C Advertising in Competitive Markets: An AI-Driven Competitive Advertising Framework
• Authors: Sakhinana Sagar Srinivas, Akash Das, Shivam Gupta, Venkataramana Runkana,
• Abstract summary: In the chemical industry, AI-discovered materials drive innovation, but commercial success hinges on market adoption.<n>We present a multilingual, multimodal AI framework for autonomous, hyper-personalized advertising in B2B and B2C markets.
• Score: 2.368662284133926
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Generative Large-Scale Pre-trained Models for Automated Ad Bidding Optimization [5.460538555236247]
  We propose GRAD (Generative Reward-driven Ad-bidding with Mixture-of-Experts), a scalable foundation model for auto-bidding.<n>We show that GRAD significantly enhances platform revenue, highlighting its effectiveness in addressing the evolving and diverse requirements of modern advertisers.
  arXiv  Detail & Related papers  (2025-08-04T02:46:18Z)
• TeamCMU at Touché: Adversarial Co-Evolution for Advertisement Integration and Detection in Conversational Search [1.187456026346823]
  integration of advertisements into generated responses presents both commercial opportunities and challenges for user experience.<n>We propose a modular pipeline for advertisement management in RAG-based conversational systems, consisting of an ad-rewriter for seamless ad integration and a robust ad-classifier for detection.
  arXiv  Detail & Related papers  (2025-07-01T07:24:29Z)
• Empowering Economic Simulation for Massively Multiplayer Online Games through Generative Agent-Based Modeling [53.26311872828166]
  We take a preliminary step in introducing a novel approach using Large Language Models (LLMs) in MMO economy simulation.<n>We design LLM-driven agents with human-like decision-making and adaptability.<n>These agents are equipped with the abilities of role-playing, perception, memory, and reasoning, addressing the aforementioned challenges effectively.
  arXiv  Detail & Related papers  (2025-06-05T07:21:13Z)
• LAMeTA: Intent-Aware Agentic Network Optimization via a Large AI Model-Empowered Two-Stage Approach [68.198383438396]
  We present LAMeTA, a Large AI Model (LAM)-empowered Two-stage Approach for intent-aware agentic network optimization.<n>First, we propose Intent-oriented Knowledge Distillation (IoKD), which efficiently distills intent-understanding capabilities.<n>Second, we develop Symbiotic Reinforcement Learning (SRL), integrating E-LAMs with a policy-based DRL framework.
  arXiv  Detail & Related papers  (2025-05-18T05:59:16Z)
• Against Opacity: Explainable AI and Large Language Models for Effective Digital Advertising [45.512178197258066]
  Meta Ads and Googles of the world attract countless advertisers who rely on intuition, with billions of dollars lost on ineffective social media ads.<n>This lack of transparency hinders the advertisers' ability to make informed decisions and necessitates efforts to promote transparency, standardize industry metrics, and strengthen regulatory frameworks.<n>In this work, we propose novel ways to assist marketers in optimizing their advertising strategies via machine learning techniques designed to analyze and evaluate content, in particular, predict the click-through rates (CTR) of novel advertising content.
  arXiv  Detail & Related papers  (2025-04-22T08:46:47Z)
• Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents [61.132523071109354]
  This paper investigates the interplay between AI developers, regulators and users, modelling their strategic choices under different regulatory scenarios. Our research identifies emerging behaviours of strategic AI agents, which tend to adopt more "pessimistic" stances than pure game-theoretic agents.
  arXiv  Detail & Related papers  (2025-04-11T15:41:21Z)
• Media and responsible AI governance: a game-theoretic and LLM analysis [61.132523071109354]
  This paper investigates the interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems.<n>Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes.
  arXiv  Detail & Related papers  (2025-03-12T21:39:38Z)
• Grounded Persuasive Language Generation for Automated Marketing [55.61700785451037]
  This paper develops an agentic framework that employs large language models (LLMs) to automate the generation of persuasive and grounded marketing content.<n>Our method is designed to align the generated content with user preferences while highlighting useful factual attributes.
  arXiv  Detail & Related papers  (2025-02-24T03:36:57Z)
• Strategic Collusion of LLM Agents: Market Division in Multi-Commodity Competitions [0.0]
  Machine-learning technologies are seeing increased deployment in real-world market scenarios. We explore the strategic behaviors of large language models (LLMs) when deployed as autonomous agents in multi-commodity markets.
  arXiv  Detail & Related papers  (2024-09-19T20:10:40Z)
• AI in Food Marketing from Personalized Recommendations to Predictive Analytics: Comparing Traditional Advertising Techniques with AI-Driven Strategies [0.0]
  Artificial Intelligence (AI) has revolutionized food marketing by providing advanced techniques for personalized recommendations, consumer behavior prediction, and campaign optimization. Traditional approaches were successful in building brand awareness but lacked the level of personalization that modern consumers demand. This paper compares the strengths and weaknesses of traditional and AI-driven food marketing techniques.
  arXiv  Detail & Related papers  (2024-09-14T17:53:32Z)
• Aggregate effects of advertising decisions: a complex systems look at search engine advertising via an experimental study [26.218512292529635]
  We develop and validate a simulation framework that supports assessments of various advertising strategies and estimations of the impact of mechanisms on the search market. We conduct three experiments on the aggregate impact of electronic word-of-mouth, the competition level, and strategic bidding behaviors.
  arXiv  Detail & Related papers  (2022-03-04T09:16:15Z)
• Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning [72.23843557783533]
  We show that deep reinforcement learning can discover stable solutions that are epsilon-Nash equilibria for a meta-game over agent types. Our approach is more flexible and does not need unrealistic assumptions, e.g., market clearing. We demonstrate our approach in real-business-cycle models, a representative family of DGE models, with 100 worker-consumers, 10 firms, and a government who taxes and redistributes.
  arXiv  Detail & Related papers  (2022-01-03T17:00:17Z)
• Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market [58.720142291102135]
  This paper focuses precisely on the study of these markets makers strategies from an agent-based perspective. We propose the application of Reinforcement Learning (RL) for the creation of intelligent market markers in simulated stock markets.
  arXiv  Detail & Related papers  (2021-12-08T14:55:21Z)
• Towards Realistic Market Simulations: a Generative Adversarial Networks Approach [2.381990157809543]
  We propose a synthetic market generator based on Conditional Generative Adversarial Networks (CGANs) trained on real aggregate-level historical data. A CGAN-based "world" agent can generate meaningful orders in response to an experimental agent.
  arXiv  Detail & Related papers  (2021-10-25T22:01:07Z)
• A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising [53.636153252400945]
  We propose a general Multi-Agent reinforcement learning framework for Auto-Bidding, namely MAAB, to learn the auto-bidding strategies. Our approach outperforms several baseline methods in terms of social welfare and guarantees the ad platform's revenue.
  arXiv  Detail & Related papers  (2021-06-11T08:07:14Z)
• MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding [47.555870679348416]
  We propose a Multi-ecTive Actor-Critics algorithm named MoTiAC for the problem of bidding optimization with various goals. Unlike previous RL models, the proposed MoTiAC can simultaneously fulfill multi-objective tasks in complicated bidding environments.
  arXiv  Detail & Related papers  (2020-02-18T07:16:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.