The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Customer Poaching and Brand Switching
Abstract
Firms sometimes try to poach the customers of their competitors by offering them inducements to switch. We analyze duopoly poaching under both short-term and long-term contracts assuming either that each consumer's brand preferences are fixed over time or that preferences are independent over time. With fixed preferences, short-term contracts lead to poaching and socially inefficient switching. The equilibrium with long-term contracts has less switching than when only short-term contracts are feasible, and it involves the sale of both short-term and long-term contracts. With independent preferences, short-term contracts are efficient, but long-term contracts lead to inefficiently little switching.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Value of Purchase History Data in Target Marketing
Rossi, P. E., McCulloch, R. E., & Allenby, G. M. (1996) · Marketing Science · doi:10.1287/mksc.15.4.321 · Cited by 754
Cited by
Goldfarb, A., & Tucker, C. (2019) · Journal of Economic Literature · doi:10.1257/jel.20171452 · Cited by 2,744
Acquisti, A., Taylor, C., & Wagman, L. (2016) · Journal of Economic Literature · doi:10.1257/jel.54.2.442 · Cited by 1,283
Conditioning prices on purchase history
Acquisti, A., & Varian, H. R. (2005) · Marketing Science · doi:10.1287/mksc.1040.0103 · Cited by 689
The Value of Personal Information in Online Markets with Endogenous Privacy
Montes, R., Sand-Zantman, W., & Valletti, T. (2019) · Management Science · doi:10.1287/mnsc.2017.2989 · Cited by 280
Dynamic targeted pricing with strategic consumers
Chen, Y., & Zhang, Z. J. (2009) · International Journal of Industrial Organization · doi:10.1016/j.ijindorg.2008.03.002 · Cited by 144
Big data and personalized price discrimination in EU competition law
Townley, C., Morrison, E., & Yeung, K. (2017) · Yearbook of European Law · doi:10.1093/yel/yex015 · Cited by 61
CUSTOMER POACHING AND ADVERTISING*
ESTEVES, R. B. (2009) · The Journal of Industrial Economics · doi:10.1111/j.1467-6451.2009.00372.x · Cited by 55
Personalized pricing and advertising: Who are the winners?
Esteves, R.-B., & Resende, J. (2019) · International Journal of Industrial Organization · doi:10.1016/j.ijindorg.2018.11.003 · Cited by 38
Behavior-based price discrimination and customer information sharing
De Nijs, R. (2017) · International Journal of Industrial Organization · doi:10.1016/j.ijindorg.2016.12.003 · Cited by 34
Behavior-based pricing under imperfectly informed consumers
Esteves, R.-B., & Cerqueira, S. (2017) · Information Economics and Policy · doi:10.1016/j.infoecopol.2017.06.007 · Cited by 34
Non-risk price discrimination in insurance: Market outcomes and public policy
Guy Thomas, R. (2012) · The Geneva Papers on Risk and Insurance - Issues and Practice · doi:10.1057/gpp.2011.32 · Cited by 22
Personalized pricing versus history-based pricing: implications for privacy policy
Miettinen, T., & Stenbacka, R. (2015) · Information Economics and Policy · doi:10.1016/j.infoecopol.2015.10.003 · Cited by 15
Metadata
- Authors
- Fudenberg, D., & Tirole, J.
- Venue
- The RAND Journal of Economics
- Publisher
- Wiley
- Published
- 2000
- Volume
- 31
- Issue
- 4
- Pages
- 634
- ISSN
- 0741-6261 (ISSN-L)
- OpenAlex
- W2068817726
- Cited by
- 629
- References
- 1
- FWCI
- 16.98
- Type
- journal article
- Language
- en
- Open access
- closed
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
