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Product Line Design Under Preference Uncertainty Using Aggregate Consumer Data
by
Xu, Zibin
, Dukes, Anthony
in
Aggregate data
/ Analysis
/ Consumer behavior
/ consumer data collection
/ Consumer preferences
/ Consumer research
/ Consumers
/ Data collection
/ Errors
/ Knowledge
/ Marketing
/ Perceptions
/ perceptual error
/ Preferences
/ Product design
/ Product development
/ product line design
/ Product lines
/ Product quality
/ signaling model
/ superior knowledge
/ Uncertainty
/ uninformed preference
2019
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Product Line Design Under Preference Uncertainty Using Aggregate Consumer Data
by
Xu, Zibin
, Dukes, Anthony
in
Aggregate data
/ Analysis
/ Consumer behavior
/ consumer data collection
/ Consumer preferences
/ Consumer research
/ Consumers
/ Data collection
/ Errors
/ Knowledge
/ Marketing
/ Perceptions
/ perceptual error
/ Preferences
/ Product design
/ Product development
/ product line design
/ Product lines
/ Product quality
/ signaling model
/ superior knowledge
/ Uncertainty
/ uninformed preference
2019
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Do you wish to request the book?
Product Line Design Under Preference Uncertainty Using Aggregate Consumer Data
by
Xu, Zibin
, Dukes, Anthony
in
Aggregate data
/ Analysis
/ Consumer behavior
/ consumer data collection
/ Consumer preferences
/ Consumer research
/ Consumers
/ Data collection
/ Errors
/ Knowledge
/ Marketing
/ Perceptions
/ perceptual error
/ Preferences
/ Product design
/ Product development
/ product line design
/ Product lines
/ Product quality
/ signaling model
/ superior knowledge
/ Uncertainty
/ uninformed preference
2019
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Product Line Design Under Preference Uncertainty Using Aggregate Consumer Data
Journal Article
Product Line Design Under Preference Uncertainty Using Aggregate Consumer Data
2019
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Overview
This research studies the product line design problem when consumers are subject to perceptual errors in assessing their intrinsic preferences.
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.
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