The Divorce of Word and Deed—A Data-Mining Approach to Identify and Evaluate Customer Requirements

Whereas online reviews have become a primary data source for understanding customer requirements in both research and practice, using such information alone to guide product design can be unreliable. Our research investigates whether and how consumers’ preferences expressed through online reviews (words) align with their actual purchase decisions (deeds). Our analysis shows that features praised in online reviews do not necessarily translate to market success. This inconsistency between what consumers say and what they do poses significant challenges for manufacturers in product development decisions. We empirically identify the existence of word–deed inconsistency in consumer preferences. Some features are silent in online reviews yet significantly drive purchase decisions, whereas others are frequently praised but have limited influence on actual purchases. Building on these insights, we propose an innovative dual-weights model that extends existing two-dimensional customer requirement analysis by integrating both prepurchase choice drivers and postpurchase satisfaction determinants. Using this model, we classify features based on their importance for satisfaction versus purchase decisions and offer actionable product improvement strategies for different types of features.