A model is most useful when its assumptions can be challenged.
Market research is full of questions that are easy to ask and difficult to identify: What caused demand to change? How much of a market is reachable? Which customers would respond differently to a new offer?
Mathematical training does not automatically answer those questions. It does encourage explicit definitions, careful treatment of uncertainty, and attention to whether the available data can support the desired conclusion.
In practice, that means checking sampling bias before interpreting survey results, testing whether a segment is stable enough to act on, and showing how a forecast changes when assumptions shift. It also means knowing when a simpler model is more honest.
The best outcome is not more technical work for its own sake. It is a recommendation whose reasoning can be inspected, debated, and improved.