The DOI matches its Crossref record.
Competitive Coupon Targeting
Abstract
With the advent of panel data on household purchase behavior, and the development of statistical procedures to utilize this data, firms can now target coupons to selected households with considerable accuracy and cost effectiveness. In this article, we develop an analytical framework to examine the effect of such targeting on firm profits, prices, and coupon face values. We also derive comparative statics on firms' optimal mix of offensive and defensive couponing, the number of coupons distributed, redemption rates, face values, and incremental sales per redemption. Among our findings: when rival firms can target their coupon promotions at brand switchers, the outcome will be a prisoner's dilemma in which the net effect of targeting is simply the cost of distribution plus the discount given to redeemers.
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
Cited by
Acquisti, A., Taylor, C., & Wagman, L. (2016) · Journal of Economic Literature · doi:10.1257/jel.54.2.442 · Cited by 1,283
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
Self-citation
Metadata
- Authors
- Shaffer, G., & Zhang, Z. J.
- Venue
- Marketing Science
- Published
- 1995-11
- Volume
- 14
- Issue
- 4
- Pages
- 395-416
- ISSN
- 0732-2399 (ISSN-L)
- 1526-548X
- OpenAlex
- W2067534308
- Cited by
- 373
- References
- 0
- FWCI
- 1.29
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Competitive Coupon Targeting. https://app.cochat.ai/papers/10.1287/mksc.14.4.395
- The paper
- Shaffer, G., & Zhang, Z. J. (1995). Competitive Coupon Targeting. Marketing Science. https://doi.org/10.1287/mksc.14.4.395
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.
