Conjoint studies with many binary features often overpredict preference for feature-rich products and underpredict preference for feature-poor products. We investigate practical solutions to this persistent challenge, combining experimental design adjustments, summed pricing, and attribute non-attendance adjustments. Using both synthetic and real respondents, we explore promising approaches to improve predictive accuracy for a study involving brand, price, and 30 binary attributes. The goal: a straightforward, practitioner-friendly methodology for CBC studies with many binary features and price.