The Role of Big Data and Predictive Analytics in Retailing
Journal of Retailing, forthcoming Praveen Kopalle, et al.
Praveen Kopalle, associate dean for the MBA program and the Signal Companies’ Professor of Management, 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. He discusses the relevance and uses of Bayesian analysis techniques, predictive analytics, and field experiments. In addition, he examines the ethical and privacy issues that may arise from the use of big data in the retail context.
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.