The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
On the Heterogeneity of Demand
Abstract
Demand heterogeneity traditionally has been defined as segments of consumers that are homogeneous with regard to the benefits they seek or in their response to marketing programs (e.g., product offering, price discounts). Although it often is acknowledged that truly homogeneous segments of consumers do not exist, the approximation is assumed to be sufficiently accurate to provide a reasonable basis for the development of marketing strategy. In this article, the authors provide evidence that the homogeneous segment assumption might not be reasonable. By using a normal component mixture model that nests other, more commonly used models of heterogeneity, the authors find that the within-component heterogeneity remains substantial, even when multiple components are present. Predictive tests substantiate their finding of large within-component heterogeneity.
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Citations
Cites
Sampling-based approaches to calculating marginal densities
Gelfand, A. E., & Smith, A. F. M. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10476213 · Cited by 6,606
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
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Metadata
- Authors
- Allenby, G. M., Arora, N., & Ginter, J. L.
- Venue
- Journal of Marketing Research
- Publisher
- SAGE Publications
- Published
- 1998-08
- Volume
- 35
- Issue
- 3
- Pages
- 384-389
- ISSN
- 0022-2437 (ISSN-L)
- 1547-7193
- OpenAlex
- W1967795958
- Cited by
- 274
- References
- 2
- FWCI
- 45.51
- Type
- journal article
- Language
- en
- Open access
- closed
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- Allenby, G. M., Arora, N., & Ginter, J. L. (1998). On the Heterogeneity of Demand. Journal of Marketing Research. https://doi.org/10.1177/002224379803500308
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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.