Designers are challenged to create sustainable products that succeed in the marketplace, often relying on life cycle analyses to identify engineered sustainable features while neglecting perceived-as-sustainable (PAS) features. PAS features may not contribute to engineered sustainability but are identified by customers as sustainable. In previous papers, we proposed methods for extracting PAS features from online reviews using machine learning techniques and validating them using collage placement techniques. We demonstrated our methods using French presses (and other products). In this paper, we combined design and marketing approaches to test previously extracted PAS features in terms of purchasing products that include PAS features, as compared to others that do not. We built a simulated Amazon shopping experience using incentive alignment and constructed a within-subject, fractional factorial design with a variety of product features and physical appearances. We collected data on purchase intent, willingness to pay, and sustainability rating. We found that participants opted to purchase products with PAS features more often than products with features that are not PAS, termed “dummy” features. Participants also indicated they were willing to pay more for products with PAS features and rated those products as more sustainable, despite the features not contributing to engineered sustainability. Our findings demonstrate the potential value of identifying and including PAS features in sustainable products and a new application for shopping simulation experiments in design research. We recommend that sustainable designers include both engineered and PAS features in sustainable products to align with customer needs, drive purchasing decisions, and potentially increase profitability.