The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Mobile Targeting
Open accessAbstract
Mobile technologies enable marketers to target consumers by time and location. This study builds on a large-scale randomized experiment of short message service (SMS) texts sent to 12,265 mobile users. We draw on contextual marketing theory to hypothesize how different combinations of mobile targeting determine consumer responses to mobile promotions. We identify that temporal targeting and geographical targeting individually increase sales purchases. Surprisingly, the sales effects of employing these two strategies simultaneously are not straightforward. When targeting proximal mobile users, our findings reveal a negative sales–lead time relationship; sending same-day mobile promotions yields an increase in the odds of consumer purchases compared with sending them two days prior to the promoted event. However, when targeting nonproximal mobile users, there is an inverted-U, curvilinear relationship. Sending one-day prior SMSs yields an increase in the odds of consumer purchases by 9.5 times compared with same-day SMSs and an increase in the odds of consumer purchases by 71% compared with two-day prior SMSs. These results are robust to unobserved heterogeneity, alternative estimation models, bootstrapped resamples, randomization checks, consumer mobile usage behavior, and segmentation of consumer scenarios. In addition, we conducted follow-up surveys to delve into the psychological mechanisms explaining the findings in our field experiment. In line with consumer construal arguments, consumers who received SMSs close (far) in time and location formed a more (less) concrete mental construal, which in turn, increased their involvement and purchase intent. These findings suggest that understanding the when, where, and how of mobile targeting strategies is crucial. Marketers can save money by carefully designing their mobile targeting campaigns. This paper was accepted by Sandra Slaughter, information systems.
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Digital marketing: a framework, review, and research agenda
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Lamberton, C., & Stephen, A. T. (2016) · Journal of Marketing · doi:10.1509/jm.15.0415 · Cited by 1,127
The Role of Big Data and Predictive Analytics in Retailing
Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2017) · Journal of Retailing · doi:10.1016/j.jretai.2016.12.004 · Cited by 516
Demystifying big data analytics for business intelligence through the Lens of marketing mix
Fan, S., Lau, R. Y. K., & Zhao, J. L. (2015) · Big Data Research · doi:10.1016/j.bdr.2015.02.006 · Cited by 321
Geo-Conquesting: Competitive Locational Targeting of Mobile Promotions
Fong, N. M., Fang, Z., & Luo, X. (2015) · Journal of Marketing Research · doi:10.1509/jmr.14.0229 · Cited by 323
Self-citation
Metadata
- Authors
- Luo, X., Andrews, M., Fang, Z., & Phang, C. W.
- Venue
- Management Science
- Published
- 2014-07
- Volume
- 60
- Issue
- 7
- Pages
- 1738-1756
- ISSN
- 0025-1909 (ISSN-L)
- 1526-5501
- OpenAlex
- W2913321842
- Cited by
- 299
- References
- 57
- FWCI
- 51.52
- 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.