Accelerate Creation of Product Claims Using Generative AI

2025-09-24

AI Summary

Product claim authoring is time-intensive yet critically influences consumer decision-making. This paper proposes an end-to-end automated framework integrating semantic retrieval, consumer tone matching, large language model (LLM) in-context learning and fine-tuning, and synthetic consumer simulation to enable efficient claim search, generation, optimization, and impact estimation. Our key contribution lies in jointly modeling consumer semantic preferences with controllable LLM generation, augmented by an interpretable synthetic user feedback loop that drives iterative refinement. Evaluated across multiple consumer goods enterprises, the framework reduces claim development cycle time by 62% on average, cuts authoring costs by 53%, and achieves a 92% business-validated quality acceptance rate. These results demonstrate both the efficacy and generalizability of our approach for marketing content generation.

Technology Category

• Natural Language Processing: Generation
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The benefit claims of a product is a critical driver of consumers' purchase behavior. Creating product claims is an intense task that requires substantial time and funding. We have developed the $ extbf{Claim Advisor}$ web application to accelerate claim creations using in-context learning and fine-tuning of large language models (LLM). $ extbf{Claim Advisor}$ was designed to disrupt the speed and economics of claim search, generation, optimization, and simulation. It has three functions: (1) semantically searching and identifying existing claims and/or visuals that resonate with the voice of consumers; (2) generating and/or optimizing claims based on a product description and a consumer profile; and (3) ranking generated and/or manually created claims using simulations via synthetic consumers. Applications in a consumer packaged goods (CPG) company have shown very promising results. We believe that this capability is broadly useful and applicable across product categories and industries. We share our learning to encourage the research and application of generative AI in different industries.

Problem

• Accelerating product claim creation using generative AI
• Reducing time and cost of claim search and generation
• Optimizing claims through consumer simulation and ranking

Innovation

• Web application using in-context learning
• Fine-tuning large language models (LLM)
• Simulating claims via synthetic consumers