Predicting consumer behavior with web search
Open accessAbstract
Recent work has demonstrated that Web search volume can "predict the present," meaning that it can be used to accurately track outcomes such as unemployment levels, auto and home sales, and disease prevalence in near real time. Here we show that what consumers are searching for online can also predict their collective future behavior days or even weeks in advance. Specifically we use search query volume to forecast the opening weekend box-office revenue for feature films, first-month sales of video games, and the rank of songs on the Billboard Hot 100 chart, finding in all cases that search counts are highly predictive of future outcomes. We also find that search counts generally boost the performance of baseline models fit on other publicly available data, where the boost varies from modest to dramatic, depending on the application in question. Finally, we reexamine previous work on tracking flu trends and show that, perhaps surprisingly, the utility of search data relative to a simple autoregressive model is modest. We conclude that in the absence of other data sources, or where small improvements in predictive performance are material, search queries provide a useful guide to the near future.
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Google search data can be used to predict near-future consumer behavior.
“Da et al. (2011) use it to obtain a measure of investor attention that predicts demand for stocks, while Goel et al. (2010) use it to predict near-future consumer behavior.”
From Consumer panic in the COVID-19 pandemic (2021) · cites this paper as Goel et al. (2010)
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ggplot2: Elegant Graphics for Data Analysis
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The Parable of Google Flu: Traps in Big Data Analysis
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Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter
Dodds, P. S., Harris, K. D., Kloumann, I. M., Bliss, C. A., & Danforth, C. M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0026752 · Cited by 809 · see citing works
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Hofman, J. M., Watts, D. J., Athey, S., Garip, F., Griffiths, T. L., Kleinberg, J., Margetts, H., Mullainathan, S., Salganik, M. J., Vazire, S., et al. (2021) · Nature · doi:10.1038/s41586-021-03659-0 · Cited by 371 · see citing works
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Koutsouleris, N., Hauser, T. U., Skvortsova, V., & De Choudhury, M. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00153-4 · Cited by 251 · see citing works
Consumer panic in the COVID-19 pandemic
Keane, M., & Neal, T. (2021) · Journal of Econometrics · doi:10.1016/j.jeconom.2020.07.045 · Cited by 245 · see citing works
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“The promise of big data analytics to “predict the present” (Choi, 2012) and even the future (Goel, 2010) make big data potentially invaluable for decision-making.”
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Feuerriegel, S., Maarouf, A., Bär, D., Geissler, D., Schweisthal, J., Pröllochs, N., Robertson, C. E., Rathje, S., Hartmann, J., Mohammad, S. M., et al. (2025) · Nature Reviews Psychology · doi:10.1038/s44159-024-00392-z · Cited by 140 · see citing works
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Self-citationClassification in the Wild: The Science and Art of Transparent Decision Making
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- Authors
- Goel, S., Hofman, J. M., Lahaie, S., Pennock, D. M., & Watts, D. J.
- Affiliations
- Princeton University (US)
- Microeconomics and Social Systems, Yahoo! Research, 111 West 40th Street, New York, NY 10018
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2010-09-27
- Volume
- 107
- Issue
- 41
- Pages
- 17486-17490
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2955127
- OpenAlex
- W2147634746
- Cited by
- 636
- References
- 3
- Citations by year
- 2026: 25 · 2025: 20 · 2024: 22 · 2023: 23 · 2022: 32 · 2021: 40 · 2020: 40 · 2019: 47 · 2018: 58 · 2017: 53 · 2016: 54 · 2015: 59 · 2014: 71 · 2013: 43 · 2012: 28
- FWCI
- 21.24
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- search data, customer behavior prediction, box office revenue, video game sales, Billboard Hot 100, nowcasting
- MeSH terms
- Behavior, Consumer Behavior, Forecasting, Humans, Models, Theoretical, Search Engine
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