The role of machine learning analytics and metrics in retailing research

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

4 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)658-675
Journal / PublicationJournal of Retailing
Volume97
Issue number4
Online published23 Dec 2020
Publication statusPublished - Dec 2021

Abstract

This research presents the use of machine learning analytics and metrics in the retailing context. We first discuss what is machine learning and explain the field's origins. We then demonstrate the strengths of machine learning methods using an online retailing dataset, noting key areas of divergence from the traditional explanatory approach to data analysis. We then provide a review of the current state of machine learning in top-level retailing and marketing research, integrating ideas for future research and showcasing potential applications for practitioners. We propose that the explanatory and machine learning approaches need not be mutually exclusive. Particularly, we discuss four key areas in the general scientific research process that can benefit from machine learning: data exploration/theory building, variable creation, estimation, and predicting an outcome metric. Due to the customer-facing nature of retailing, we anticipate several challenges researchers and practitioners might face in the adoption and implementation of machine learning, such as ethical prediction and customer privacy issues. Overall, our belief is that machine learning can enhance customer experience and, accordingly, we advance opportunities for future research.

Research Area(s)

  • Analytics, Machine learning, Metrics, Prediction, Retailing, Trends

Citation Format(s)

The role of machine learning analytics and metrics in retailing research. / Wang, Xin (Shane); Ryoo, Jun Hyun (Joseph); Bendle, Neil; Kopalle, Praveen K.

In: Journal of Retailing, Vol. 97, No. 4, 12.2021, p. 658-675.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review