MindFuse: Towards GenAI Explainability in Marketing Strategy Co-Creation

2025-11-30

AI Summary

This paper addresses the limited interpretability and shallow strategic co-creation capability of generative AI in digital marketing. We propose an interpretable AI co-creation framework that integrates click-through rate (CTR) modeling with large language models (LLMs), leveraging attention mechanisms to enable fine-grained ad performance attribution and dynamic narrative logic construction. The framework supports end-to-end strategic co-creation—from competitive analysis and creative ideation to real-time campaign optimization. Its key innovation lies in elevating AI from a content-generation tool to a reasoning-capable, iterative strategic collaborator, enhanced by real-time telemetry feedback and data-driven narrative generation algorithms. Empirical deployment at advertising agencies demonstrates up to a 12× improvement in operational efficiency. The framework is interoperable with third-party audience datasets (e.g., GWI, Nielsen) and supports full-funnel attribution systems.

Technology Category

• Natural Language Processing: Generation
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Ad search and search for Web retail

Abstract

The future of digital marketing lies in the convergence of human creativity and generative AI, where insight, strategy, and storytelling are co-authored by intelligent systems. We present MindFuse, a brave new explainable generative AI framework designed to act as a strategic partner in the marketing process. Unlike conventional LLM applications that stop at content generation, MindFuse fuses CTR-based content AI-guided co-creation with large language models to extract, interpret, and iterate on communication narratives grounded in real advertising data. MindFuse operates across the full marketing lifecycle: from distilling content pillars and customer personas from competitor campaigns to recommending in-flight optimizations based on live performance telemetry. It uses attention-based explainability to diagnose ad effectiveness and guide content iteration, while aligning messaging with strategic goals through dynamic narrative construction and storytelling. We introduce a new paradigm in GenAI for marketing, where LLMs not only generate content but reason through it, adapt campaigns in real time, and learn from audience engagement patterns. Our results, validated in agency deployments, demonstrate up to 12 times efficiency gains, setting the stage for future integration with empirical audience data (e.g., GWI, Nielsen) and full-funnel attribution modeling. MindFuse redefines AI not just as a tool, but as a collaborative agent in the creative and strategic fabric of modern marketing.

Problem

• Develops an explainable AI framework for marketing strategy co-creation
• Integrates AI to interpret and iterate narratives using real advertising data
• Enables real-time campaign adaptation and learning from audience engagement

Innovation

• Explainable AI framework for marketing strategy co-creation
• Fuses CTR-based AI with LLMs for data-driven narrative iteration
• Uses attention-based explainability to diagnose and optimize campaigns