CoChat
Sign Up
Cited by 689Consumer Market Behavior and PricingMerger and Competition AnalysisSupply Chain and Inventory Management

Conditioning prices on purchase history

Acquisti, Alessandro, Varian, Hal R.·2005·Marketing Science
DOI 10.1287/mksc.1040.0103
Open this paper in CoChat — free account

Abstract

The rapid advance in information technology now makes it feasible for sellers to condition their price offers on consumers’ prior purchase behavior. In this paper we examine when it is profitable to engage in this form of price discrimination when consumers can adopt strategies to protect their privacy. Our baseline model involves rational consumers with constant valuations for the goods being sold and a monopoly merchant who can commit to a pricing policy. Applying results from the prior literature, we show that although it is feasible to price so as to distinguish high-value and low-value consumers, the merchant will never find it optimal to do so. We then consider various generalizations of this model, such as allowing the seller to offer enhanced services to previous customers, making the merchant unable to commit to a pricing policy, and allowing competition in the marketplace. In these cases we show that sellers will, in general, find it profitable to condition prices on purchase history.

Integrity signals

Registry

Crossref · 10.1287/mksc.1040.0103

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Data have been used in cybercrimes, to charge different prices for the same product depending on consumer characteristics, and to micro-regulate and persecute citizens in some countries.

    “Such data have been used, for example, in cybercrimes, to charge different prices for the same product depending on the characteristics of the consumer (price discrimination) and to micro-regulate and persecute citizens in some countries (e.g., Acquisti and Varian, 2005;Zuboff, 2015).”

    From The privacy and control paradoxes in the context of smartphone apps (2022) · cites this paper as Acquisti and Varian, 2005

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Acquisti, A., & Varian, H. R.
Affiliations
  • Carnegie Mellon University (US)
  • University of California, Berkeley (US)
ORCID
Acquisti, Alessandro
Venue
Marketing Science
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Published
2005-08
Volume
24
Issue
3
Pages
367-381
ISSN
  • 0732-2399 (ISSN-L)
  • 1526-548X
DOI
10.1287/mksc.1040.0103
OpenAlex
W3122338044
Cited by
689
References
1
Citations by year
2026: 22 · 2025: 62 · 2024: 30 · 2023: 50 · 2022: 42 · 2021: 32 · 2020: 36 · 2019: 35 · 2018: 37 · 2017: 48 · 2016: 27 · 2015: 37 · 2014: 31 · 2013: 25 · 2012: 26
FWCI
96.91
Type
journal article
Language
en
Open access
closed
Locations
Marketing Science (publishedVersion)
Funders
National Science Foundation
Topics
  • Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Merger and Competition Analysis
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Supply Chain and Inventory Management
Keywords
price discrimination, purchase history, consumer privacy, privacy preservation, commitment, monopoly pricing, customer segmentation, competition

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review