The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Customizing Promotions in Online Stores
Abstract
The main objective of this paper is to provide a decision-support system of micro-level customized promotions, primarily for use in online stores. Our proposed approach utilizes the one-on-one and interactive nature of the Internet shopping environment and provides recommendations on when to promote how much to whom. We address the issue by first constructing a joint purchase incidence-brand choice-purchase quantity model that incorporates how variety-seeking/inertia tendency differs among households and change over time for the same household. Based on the model, we develop an optimization procedure to derive the optimal amount of price discount for each household on each shopping trip. We demonstrate that the proposed customization method could greatly improve the effectiveness of current promotion practices, and discuss the implications for retailers and consumer packaged goods companies in the age of Internet technology.
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
Brand and Quantity Choice Dynamics Under Price Uncertainty
Erdem, T., Imai, S., & Keane, M. P. (2003) · Quantitative Marketing and Economics · doi:10.1023/a:1023536326497 · Cited by 331
Investigating Household Purchase Timing Decisions: A Conditional Hazard Function Approach
Jain, D. C., & Vilcassim, N. J. (1991) · Marketing Science · doi:10.1287/mksc.10.1.1 · Cited by 270
Modeling Inertia and Variety Seeking Tendencies in Brand Choice Behavior
Bawa, K. (1990) · Marketing Science · doi:10.1287/mksc.9.3.263 · Cited by 213
Cited by
Digital marketing: a framework, review, and research agenda
Kannan, P. K., & Li, H. “. (2017) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2016.11.006 · Cited by 1,707
Marketing Analytics for Data-Rich Environments
Wedel, M., & Kannan, P. K. (2016) · Journal of Marketing · doi:10.1509/jm.15.0413 · Cited by 1,261
Adaptive personalization using social networks
Chung, T. S., Wedel, M., & Rust, R. T. (2016) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-015-0441-x · Cited by 184
Mobile App Analytics: A Multiple Discrete-Continuous Choice Framework1
Han, S. P., Park, S., & Oh, W. (2016) · MIS Quarterly · doi:10.25300/misq/2016/40.4.09 · Cited by 86
Reimer, K., & Becker, J. U. (2015) · Management Review Quarterly · doi:10.1007/s11301-014-0110-z · Cited by 11
Metadata
- Authors
- Zhang, J., & Krishnamurthi, L.
- Venue
- Marketing Science
- Published
- 2004-11
- Volume
- 23
- Issue
- 4
- Pages
- 561-578
- ISSN
- 0732-2399 (ISSN-L)
- 1526-548X
- OpenAlex
- W2095671462
- Cited by
- 202
- References
- 4
- FWCI
- 34.59
- 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: Customizing Promotions in Online Stores. https://app.cochat.ai/papers/10.1287/mksc.1040.0055
- The paper
- Zhang, J., & Krishnamurthi, L. (2004). Customizing Promotions in Online Stores. Marketing Science. https://doi.org/10.1287/mksc.1040.0055
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.
