Accelerate Creation of Product Claims Using Generative AI
This addresses the problem of inefficient product claim creation for businesses, particularly in consumer goods, though it is incremental as it applies existing AI methods to a specific domain.
The paper tackles the time-consuming and costly process of creating product claims by developing the Claim Advisor web application, which uses large language models to accelerate claim search, generation, optimization, and simulation, showing promising results in consumer packaged goods applications.
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 $\textbf{Claim Advisor}$ web application to accelerate claim creations using in-context learning and fine-tuning of large language models (LLM). $\textbf{Claim Advisor}$ 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.