The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Modeling the Clickstream: Implications for Web-Based Advertising Efforts
Abstract
In this paper, we develop an analytical approach to modeling consumer response to banner ad exposures at a sponsored content Web site that reveals significant heterogeneity in (unobservable) click proneness across consumers. The effect of repeated exposures to banner ads is negative and nonlinear, and the differential effect of each successive ad exposure is initially negative, though nonlinear, and levels off at higher levels of passive ad exposures. Further, significant correlations between session and consumer click proneness and banner exposure sensitivity suggest gains from repeated banner exposures when consumers are less click prone. For a particular number of sessions, more clicks are generated from consumers who revisit over a longer period of time, than for those with the same number of sessions in a relatively shorter timeframe. We also find that consumers are equally likely to click on banner ads placed early or late in navigation path and that exposures have a positive cumulative effect in inducing click-through in future sessions. Our results have implications for online advertising response measurement and dynamic ad placement, and may help guide advertising media placement decisions.
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Building consumer trust online
Hoffman, D. L., Novak, T. P., & Peralta, M. (1999) · Communications of the ACM · doi:10.1145/299157.299175 · Cited by 2,132
Self-citationPreattentive Mere Exposure Effects
Janiszewski, C. (1993) · Journal of Consumer Research · doi:10.1086/209356 · Cited by 455
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Modeling consumer purchasing behavior in social shopping communities with clickstream data
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The personalization-privacy paradox: Implications for new media
Aguirre, E., Roggeveen, A. L., Grewal, D., & Wetzels, M. (2016) · Journal of Consumer Marketing · doi:10.1108/jcm-06-2015-1458 · Cited by 251
Thomaz, F., Salge, C., Karahanna, E., & Hulland, J. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00704-3 · Cited by 204
Internet advertising effectiveness: The effect of design on click-through rates for banner ads
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Online Display Advertising Markets: A Literature Review and Future Directions
Choi, H., Mela, C. F., Balseiro, S. R., & Leary, A. (2020) · Information Systems Research · doi:10.1287/isre.2019.0902 · Cited by 181
What Are Likes Worth? A Facebook Page Field Experiment
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Banner Advertisements through a New Lens
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Luo, X., Lu, X., & Li, J. (2019) · Information Systems Research · doi:10.1287/isre.2019.0859 · Cited by 87
Predicting online shopping cart abandonment with machine learning approaches
Rausch, T. M., Derra, N. D., & Wolf, L. (2022) · International Journal of Market Research · doi:10.1177/1470785320972526 · Cited by 36
Wear in or wear out: how consumers respond to repetitive influencer marketing
Geng, R., Chen, X., & Wang, S. (2024) · Internet Research · doi:10.1108/intr-01-2022-0075 · Cited by 39
Bremer, V., Chow, P. I., Funk, B., Thorndike, F. P., & Ritterband, L. M. (2020) · doi:10.2196/preprints.17738 · Cited by 29
Metadata
- Authors
- Chatterjee, P., Hoffman, D. L., & Novak, T. P.
- Venue
- Marketing Science
- Published
- 2003-11
- Volume
- 22
- Issue
- 4
- Pages
- 520-541
- ISSN
- 0732-2399 (ISSN-L)
- 1526-548X
- OpenAlex
- W2125045187
- Cited by
- 472
- References
- 2
- FWCI
- 53.97
- 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).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
