 The Role of Big Data and Predictive Analytics in RetailingEric T. Bradlow, Manish Gangwar, Praveen Kopalle and Sudhir Voleti Journal of Retailing, 2017, vol. 93, issue 1, 79-95 Abstract: The paper examines the opportunities in and possibilities arising from big data in retailing, particularly along five major data dimensions—data pertaining to customers, products, time, (geo-spatial) location and channel. Much of the increase in data quality and application possibilities comes from a mix of new data sources, a smart application of statistical tools and domain knowledge combined with theoretical insights. The importance of theory in guiding any systematic search for answers to retailing questions, as well as for streamlining analysis remains undiminished, even as the role of big data and predictive analytics in retailing is set to rise in importance, aided by newer sources of data and large-scale correlational techniques. The Statistical issues discussed include a particular focus on the relevance and uses of Bayesian analysis techniques (data borrowing, updating, augmentation and hierarchical modeling), predictive analytics using big data and a field experiment, all in a retailing context. Finally, the ethical and privacy issues that may arise from the use of big data in retailing are also highlighted. Keywords: Big data; Predictive analytics; Retailing; Pricing (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jouret:v:93:y:2017:i:1:p:79-95 DOI: 10.1016/j.jretai.2016.12.004 Access Statistics for this article Journal of Retailing is currently edited by A. Roggeveen More articles in Journal of Retailing from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.