  With the rise of voice assistants and an increase in mobile search usage,
natural language has become an important query language. So far, most of the
current systems are not able to process these queries because of the vagueness
and ambiguity in natural language. Users have adapted their query formulation
to what they think the search engine is capable of, which adds to their
cognitive burden. With our research, we contribute to the design of interactive
search systems by investigating the genuine information need in a product
search scenario. In a crowd-sourcing experiment, we collected 132 information
needs in natural language. We examine the vagueness of the formulations and
their match to retailer-generated content and user-generated product reviews.
Our findings reveal high variance on the level of vagueness and the potential
of user reviews as a source for supporting users with rather vague search
intents.
