The Role of Big Data and Predictive Analytics in Retailing
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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.
“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
The Conclusion (p. 36) says improved data quality, rather than merely greater data volume, drives improved outcomes. The Customers section (p. 5) describes the value of individual-level data for granular targeting, but the paper does not say benefits emerge only with fine-grained data; it also emphasizes recency, linked data, statistical tools and domain knowledge. (LLM, conf=0.88)
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- 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
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