Product Line Design under Preference Uncertainty using Aggregate Consumer Data

This research studies the product line design problem when consumers are subject to perceptual errors when assessing their intrinsic preferences. If the errors are driven by common variables, then a firm can filter out the impact of these variables using aggregate consumer data (e.g. survey data, conjoint studies, or anonymous usage data). In this way, we develop micro-foundations necessary to show how and when the firm may deduce consumers' perceptual errors and acquire superior knowledge on consumer preference. But is superior knowledge ever unprofitable? How should the firm with superior knowledge design its product line? Do consumers receive more-relevant products or simply have more surplus extracted? Can data collection help consumers make better choices? Our results suggest that consumers' rational suspicions may prevent the firm from exploiting its informational advantage. In addition, the burden of signaling may force the firm to offer efficient quality for its products. Therefore, allowing the firm to collect aggregate consumer data may be strictly Pareto improving