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.

本研究介绍了机器学习分析和指标在零售环境中的使用。我们首先讨论什么是机器学习并解释该领域的起源。然后，我们使用在线零售数据集展示机器学习方法的优势，并指出与传统的数据分析解释性方法存在差异的关键领域。然后，我们回顾了顶级零售和营销研究中机器学习的当前状态，整合了未来研究的想法，并向从业者展示了潜在的应用。我们建议解释性和机器学习方法不必相互排斥。特别是，我们讨论了可以从机器学习中受益的一般科学研究过程中的四个关键领域：数据探索/理论构建、变量创建、估计、并预测结果指标。由于零售面向客户的性质，我们预计研究人员和从业人员在采用和实施机器学习时可能会面临一些挑战，例如道德预测和客户隐私问题。总的来说，我们相信机器学习可以增强客户体验，因此我们为未来的研究提供了机会。