The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Geo-Conquesting: Competitive Locational Targeting of Mobile Promotions
Open accessAbstract
As consumers spend more time on their mobile devices, a focal retailer's natural approach is to target potential customers in close proximity to its own location. Yet focal (own) location targeting may cannibalize profits on inframarginal sales. This study demonstrates the effectiveness of competitive locational targeting, the practice of promoting to consumers near a competitor's location. The analysis is based on a randomized field experiment in which mobile promotions were sent to customers at three similar shopping areas (competitive, focal, and benchmark locations). The results show that competitive locational targeting can take advantage of heightened demand that a focal retailer would not otherwise capture. Competitive locational targeting produced increasing returns to promotional discount depth, whereas targeting the focal location produced decreasing returns to deep discounts, indicating saturation effects and profit cannibalization. These findings are important for marketers, who can use competitive locational targeting to generate incremental sales without cannibalizing profits. Although the experiment focuses on the effects of unilateral promotions, it represents an initial step in understanding the competitive implications of mobile marketing technologies.
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Metadata
- Authors
- Fong, N. M., Fang, Z., & Luo, X.
- Venue
- Journal of Marketing Research
- Publisher
- SAGE Publications
- Published
- 2015-10
- Volume
- 52
- Issue
- 5
- Pages
- 726-735
- ISSN
- 0022-2437 (ISSN-L)
- 1547-7193
- OpenAlex
- W2078983314
- Cited by
- 323
- References
- 27
- FWCI
- 86.51
- Type
- journal article
- Language
- en
- Open access
- green
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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.
