Studies with large product assortments cannot present every alternative without overburdening respondents. Conventional methods reduce the set through preliminary screening, but identifying relevant alternatives generally still requires a prohibitive number of screening tasks. We propose replacing these tasks with an LLM-led conversation that learns each respondent’s preferences from their needs, habits, and desired benefits. The LLM then uses this profile to screen the available items and create a personalized set for the subsequent conjoint exercise.
