Product Line Design Under Preference Uncertainty Using Aggregate Consumer Data

Zibin Xu () and Anthony Dukes ()
Additional contact information
Zibin Xu: Antai College of Economics and Management, Shanghai Jiao Tong University, 200030 Shanghai, China

Marketing Science, 2019, vol. 38, issue 4, 669-689

Abstract: This research studies the product line design problem when consumers are subject to perceptual errors in assessing their intrinsic preferences. If perceptual errors are driven by common variables, then a firm may use aggregate consumer data (e.g., conjoint studies or anonymous usage data) to deduce the errors and infer the consumer preferences. In this way, we develop microfoundations necessary to show when and how the firm can understand consumer preferences better than consumers themselves, a situation we call superior knowledge . 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 superior knowledge. 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.

Keywords: consumer data collection; product line design; superior knowledge; uninformed preference; perceptual error; signaling model (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
https://doi.org/10.1287/mksc.2019.1160 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:38:y:2019:i:4:p:669-689

Access Statistics for this article

More articles in Marketing Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().