Seeing Like a Shopper: Validating Vision-Language Models as Synthetic Consumers

원문 초록

Language models are widely used as synthetic respondents in surveys, behavioral experiments, and market research. Most of these respondents read text, whereas shoppers in a store choose by looking at shelves, which makes vision-language models (VLMs) natural synthetic shoppers. We test whether VLM shoppers follow real consumers at two levels. At the purchase level, VLM shoppers built from household records plan their trips from a store directory and select products from shelf images, and we compare their purchases with six public datasets from three countries. The synthetic baskets hit held-out purchases as often as the households' own past trips, and synthetic group differences correlate with real ones twice as strongly as those of a trained regression. Without fitting, VLM shoppers reproduce alcohol purchases by gender and age in Russia and trip sizes by store distance in Taiwan, and respond to situations, prices, and displays within measured ranges. At the level of a single shelf choice, a model's answer confounds the product with its position and letter label. We therefore introduce ShelfSight, shelf renderings that vary products, positions, labels, viewpoints, and prices independently, together with a factor model that separates product preferences from the effects of label and position, which we call answer effects. On ShelfSight, hosted VLMs read the shelf, follow stated needs, keep their product preferences across views, and respond to price as budget shoppers. These answer effects, a preference for the letter A and for positions in the image, are estimated once per image type and removed. VLMs are thus good synthetic shoppers whose corrected shelf choices reflect product preferences.