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The Role of Big Data and Predictive Analytics in Retailing
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“Nevertheless, the benefits of big data analytics appear not to be derived from the enormous size of the data, but only emerge when working with fine-grained data (Bradlow et al., 2017;Martens et al., 2016).”
From COVID‐19 Pandemic in the New Era of Big Data Analytics: Methodological Innovations and Future Research Directions (2021) · cites this paper as Bradlow et al., 2017
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Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,385
Data clustering: 50 years beyond K-means
Jain, A. K. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2009.09.011 · Cited by 10,021
Information rules - a strategic guide to the network economy
Shapiro, C., & Varian, H. (1999) · The Journal of Economic Education · doi:10.2307/1183273 · Cited by 5,225
Information Rules: A Strategic Guide to the Network Economy
Shaw, M. J., Shapiro, C., & Varian, H. R. (2000) · Academy of Management Review · doi:10.2307/259025 · Cited by 4,416
Detecting influenza epidemics using search engine query data
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295
The Calculation of Posterior Distributions by Data Augmentation
Tanner, M. A., & Wong, W. H. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478458 · Cited by 3,929
Big Data : The Management Revolution
マカフィー, ア., ブリニョルフソン, エ., & 有賀, 裕. (2013) · Harvard business review · Cited by 3,545
The Parable of Google Flu: Traps in Big Data Analysis
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014) · Science · doi:10.1126/science.1248506 · Cited by 2,523 · cited 1×
“Later a deeper analysis showed that these 'theory-free' projections were overpredicting actual cases by 100%, among other problems that arose (see Lazer et al. 2014 for a review).”
Background
Research works printed but not linked: 2
- Cooper, 1988, Vol. 1
- Van Bommel, 2014, Digitizing the Consumer Decision Journey, McKinsey Quarterly
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Pantano, E., & Dennis, C. (2019) · Springer eBooks · doi:10.1007/978-3-030-12608-7_7 · Cited by 1
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Smaldone, F., D’Arco, M., & Marino, V. (2021) · Springer proceedings in business and economics · doi:10.1007/978-3-030-76935-2_7 · Cited by 1
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Habbal, F. (2022) · doi:10.54878/ejbess.209 · Cited by 1
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Sokol, O., & Holý, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.10199 · Cited by 1
Aljumah, A. I., Nuseir, M. T., & Alam, M. M. (2022) · doi:10.31219/osf.io/9auec · Cited by 1
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Chen, Y., Cui, X., Li, A., Wu, B., & Liu, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4878048 · Cited by 1
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Rai, H., & Kesarwani, J. (2025) · doi:10.1002/9781394287062.ch8 · Cited by 1
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Daras, G., Bruno Agard, A., & Penz, B. (2018) · Business & Information Systems Engineering · doi:10.1007/s12599-018-0548-y
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Trembošová, M., Dubcová, A., & Nagyová, Ľ. (2018) · International Scientific Days · doi:10.15414/isd2018.s2-1.15
Machado, M. A. D. (2018) · International Journal of Business Marketing · doi:10.18568/ijbmkt.3.2.67
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Chen, R.-Y. (2018) · International Conference Big Data Engineering and Technology · doi:10.1145/3297730.3297731
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Verma, V. K., & Trivedi, H. (2018) · International Journal of Trend in Scientific Research and Development · doi:10.31142/ijtsrd14488
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Woźniczka, J. (2018) · Marketing i Rynek
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Byrd, P., Knowles, J., Andreev, D., Turner, J., Mente, B., & Wallace, L. (2019) · SMU Scholar (Southern Methodist University)
Predictive Analysis: Rolein Big Data
Sharma, M. (2019) · Indian Journal of Science and Technology · doi:10.17485/ijst/2019/v12i38/145564
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Hu, L. (2020) · Springer eBooks · doi:10.1007/978-3-030-38160-8_4
Machine Learning Infusion in Service Processes
Messer, U., & Faußer, S. (2020) · Forum Dienstleistungsmanagement · doi:10.1007/978-3-658-30168-2_14
Escamilla-García, P. E., Legal, M., de Luna, A. M., & Hernandez, F. A. (2020) · LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI) · doi:10.18687/laccei2020.1.1.74
Metadata
- Authors
- Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S.
- Affiliations
- University of Pennsylvania (US)
- Indian School of Business (IN)
- Dartmouth College (US)
- Corresponding author
- Kopalle, Praveen
- Venue
- Journal of Retailing
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 93
- Issue
- 1
- Pages
- 79-95
- ISSN
- 0022-4359 (ISSN-L)
- OpenAlex
- W2599766447
- Cited by
- 516
- References
- 86
- Citations by year
- 2026: 39 · 2025: 59 · 2024: 62 · 2023: 57 · 2022: 71 · 2021: 81 · 2020: 63 · 2019: 47 · 2018: 35 · 2017: 2
- FWCI
- 68.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Retailing (publishedVersion)
- Digital Eprints Services at ISB (DESI) (Indian School of Business) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Keywords
- big data, predictive analytics, retail trade, customer data, product data, geospatial data, retail channels, Bayesian analysis, hierarchical models, data augmentation, field experiments, data quality, privacy, data ethics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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