Consumer profiling with data requirements: Structure and policy implications

File Description SizeFormat 
main.pdfFile embargoed until 22 September 2021950.28 kBAdobe PDF    Request a copy
Title: Consumer profiling with data requirements: Structure and policy implications
Authors: Valletti, T
Wu, J
Item Type: Journal Article
Abstract: We consider a model where a monopolist can profile consumers in order to price discriminate among them, and consumers can take costly actions to protect their identities and make the profiling technology less effective. A novel aspect of the model consists in the profiling technology: the signal that the monopolist gets about a consumer’s willingness-to-pay can be made more accurate either by having more consumers revealing their identities, or by spending larger amounts of money (e.g., on third-party complementary data or data analytics capabilities). We show that both consumer surplus and social welfare are convex in the ability of consumers to conceal their identities. The interest of this result stems from the fact that consumers’ concealing cost can be interpreted as a policy tool: a stricter privacy law would make the concealing cost lower, and vice-versa. Consequently, a policymaker who promotes total welfare should either make data protection very easy or very costly. The right direction of data regulations depends on data requirements. In particular, a higher (lower) data requirement is an instance when more (less) consumers are needed to achieve the same signal precision. We show that a strict data privacy law is preferable under a high data requirement so that firms are less likely to invest in profiling inefficiently, whereas there is less concern with little or no data regulations under a low data requirement. We also discuss when greater data protection may be beneficial to the firm.
Issue Date: 12-Feb-2020
Date of Acceptance: 1-Aug-2019
URI: http://hdl.handle.net/10044/1/73064
DOI: 10.1111/poms.13108
ISSN: 1059-1478
Publisher: Wiley
Start Page: 309
End Page: 329
Journal / Book Title: Production and Operations Management
Volume: 29
Issue: 2
Keywords: Science & Technology
Technology
Engineering, Manufacturing
Operations Research & Management Science
Engineering
profiling
privacy
price discrimination
signal accuracy
big data
PRICE-DISCRIMINATION
PRIVACY
MARKETS
COMPETITION
ECONOMICS
0102 Applied Mathematics
1503 Business and Management
Operations Research
Publication Status: Published
Embargo Date: 2021-09-22
Online Publication Date: 2019-09-22
Appears in Collections:Imperial College Business School



Unless otherwise indicated, items in Spiral are protected by copyright and are licensed under a Creative Commons Attribution NonCommercial NoDerivatives License.

Creative Commons