Absolutely. In this case this means your pre-process will be less deterministic, hence your success ratio will be lower (maybe a lot lower). Perhaps improving your communication and data analysis skills would do good, as this sort of context is characterized by asymmetric information. Even though you’re in the market, this is still research, just of a different kind. I would try and find the “hidden variables” for each candidate hypothesis. Assuming “performance” to be the true abstract metric you’ll be striving for, it’s important to identify what particular concrete performance metric is worthwhile improving. For instance, consumers may suggest interest in feature A and B, but once you analyze their yield you identify they are time-saving features at their core. If time is the hidden variable, then where in the product lies the best opportunity for saving user time, even though the customers might not be mentioning it? I would guess that most requests or indications coming from customers will fall more or less in the same few buckets in respect to hidden variables and related metrics. Overall you shouldn’t take customer feedback as prescriptions, but as symptoms, and those symptoms might not even be related to what they’re being vocal about, since they might be stuck in a corner not by the lack of improvements in that existing domain but maybe by the lack of an entire novel domain. Think of it like a Tetris game, locally, each region in the bricks stack require some particular piece to solve that region, but globally, that’s not a good strategy, as a whole different piece in a whole different region would solve a larger portion of the stack.