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Feature Models are popular tools for describing software product lines. Analysis of feature models has traditionally focused on consistency checking (yielding a yes/no answer) and product selection assistance, interactive or offline. In this paper, we describe a novel approach to identify the most critical decisions in product selection/configuration by taking advantage of a large pool of randomly generated, generally inconsistent, product variants. Range Ranking, a data mining technique, is utilized to single out the most critical design choices, reducing the job of the human designer to making less consequential decisions. A large feature model is used as a case study; we show preliminary results of the new approach to illustrate its usefulness for practical product derivation.