The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
When Does Retargeting Work? Information Specificity in Online Advertising
Abstract
Firms can now offer personalized recommendations to consumers who return to their website, using consumers' previous browsing history on that website. In addition, online advertising has greatly improved in its use of external browsing data to target Internet ads. Dynamic retargeting integrates these two advances by using information from the browsing history on the firm's website to improve advertising content on external websites. When surfing the Internet, consumers who previously viewed products on the firm's website are shown ads with images of those same products. To examine whether this is more effective than simply showing generic brand ads, the authors use data from a field experiment conducted by an online travel firm. Surprisingly, the data suggest that dynamic retargeted ads are, on average, less effective than their generic equivalents. However, when consumers exhibit browsing behavior that suggests their product preferences have evolved (e.g., visiting review websites), dynamic retargeted ads no longer underperform. One explanation for this finding is that when consumers begin a product search, their preferences are initially construed at a high level. As a result, they respond best to higher-level product information. Only when they have narrowly construed preferences do they respond positively to ads that display detailed product information. This finding suggests that in evaluating how best to reach consumers through ads, managers should be aware of the multistage nature of consumers' decision processes and vary advertising content along these stages.
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Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793
The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents1
Komiak, S. Y. X., & Benbasat, I. (2006) · MIS Quarterly · doi:10.2307/25148760 · Cited by 1,374
Cited by
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611
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
Acquisti, A., Taylor, C., & Wagman, L. (2016) · Journal of Economic Literature · doi:10.1257/jel.54.2.442 · Cited by 1,283
Social Networks, Personalized Advertising, and Privacy Controls
Tucker, C. E. (2014) · Journal of Marketing Research · doi:10.1509/jmr.10.0355 · Cited by 829
Self-citationOnline behavioral advertising: A literature review and research agenda
Boerman, S. C., Kruikemeier, S., & Zuiderveen Borgesius, F. J. (2017) · Journal of Advertising · doi:10.1080/00913367.2017.1339368 · Cited by 607
Metadata
- Authors
- Lambrecht, A., & Tucker, C.
- Venue
- Journal of Marketing Research
- Publisher
- SAGE Publications
- Published
- 2013-10
- Volume
- 50
- Issue
- 5
- Pages
- 561-576
- ISSN
- 0022-2437 (ISSN-L)
- 1547-7193
- OpenAlex
- W3125090935
- Cited by
- 508
- References
- 2
- FWCI
- 98.92
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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