Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMs
This addresses a specific problem for product designers by making personas more actionable, though it appears incremental as it builds on existing multimodal LLM capabilities.
The paper tackles the problem of personas being abstract and hard to translate into actionable design features in product design by introducing Personagram, an interactive system using multimodal LLMs that helps designers explore personas and extract product features, resulting in higher engagement, perceived transparency, and satisfaction in a study with 12 professional designers.
Product designers often begin their design process with handcrafted personas. While personas are intended to ground design decisions in consumer preferences, they often fall short in practice by remaining abstract, expensive to produce, and difficult to translate into actionable design features. As a result, personas risk serving as static reference points rather than tools that actively shape design outcomes. To address these challenges, we built Personagram, an interactive system powered by multimodal large language models (MLLMs) that helps designers explore detailed census-based personas, extract product features inferred from persona attributes, and recombine them for specific customer segments. In a study with 12 professional designers, we show that Personagram facilitates more actionable ideation workflows by structuring multimodal thinking from persona attributes to product design features, achieving higher engagement with personas, perceived transparency, and satisfaction compared to a chat-based baseline. We discuss implications of integrating AI-generated personas into product design workflows.