Retail Innovation Lab: Blending Data Science with Cutting-Edge Technology

零售创新实验室：将数据科学与尖端技术相融合

• 批准号: RGPIN-2021-02668
• 负责人: Cohen, Maxime
• 金额: $ 2.62万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=758152
• 关键词: Retail; Innovation; Lab; Blending; Data

The retail sector is in constant evolution. On the one hand, the entire sector is considered at risk, where the term "retail apocalypse" has been coined. On the other hand, retail remains a necessity, as it facilitates the supply of basic products such as groceries, drugs, and clothes. In the last few years, the explosion of new technologies has dramatically altered the retail landscape. Retail is far from referring only to physical stores anymore, so that most retailers are opting for an omni-channel strategy that includes physical stores, an e-commerce platform, a mobile shop, as well as several hybrid options (e.g., buy-online-pick-up-in-store). At the same time, it became ubiquitous for retailers to collect unprecedented volumes of data about their customers. These data can then be used to develop machine-learning algorithms to personalize services and boost retailers' profits. I am one of the two co-directors of the McGill Retail Innovation Lab (MRIL). We are in the process of opening a live retail lab in partnership with Couche-Tard/Circle K (one of the largest convenience store retailers worldwide). The MRIL will include several technological advances such as smart cameras, shelf-weight censors, and frictionless technology. These technologies will allow us to collect novel sources of data that ultimately can help sharpen our understanding on customer behaviour as well as improving retail operational decisions (e.g., prices, targeted promotions, assortment strategies). Building upon my current research on data science and retail analytics, in this five-year integrative research program my goal is to leverage data science techniques to improve retail practices, while improving social welfare. The research program is divided into four modules: (1) Adapting Retail Practices to the Post-Pandemic Landscape, (2) Frictionless Retail Operations, (3) Nudging Customers for Social Good, and (4) Using AR and VR Technologies to Conduct A/B Tests in Retail. The goal of these four research modules is to develop data-driven decision support tools that combine data science and new technologies to improve retail practices, enhance the customer experience, and benefit the society. The proposed research program will be among the first to develop a framework that can help the Canadian retail sector, while also improving the customer experience and keeping a strong focus on social good and societal benefits. In addition to the development of novel data-driven models and algorithms, we will apply latest techniques to the post-pandemic retail landscape. Finally, we will focus on developing real-world systems based on our theoretical insights that can significantly impact retailers, consumers, and the society as a whole.

零售业正在不断发展。一方面，整个行业都被认为处于风险之中，“零售末日”一词由此诞生。另一方面，零售仍然是一种必需品，因为它促进了食品杂货、药品和服装等基本产品的供应。在过去的几年里，新技术的爆炸性增长极大地改变了零售业的格局。零售远远不再只指实体店，因此大多数零售商都选择了全渠道战略，包括实体店、电子商务平台、移动商店以及几种混合选择(例如，在线购买-在店提货)。与此同时，零售商收集前所未有的海量客户数据变得无处不在。然后，这些数据可以用于开发机器学习算法，以个性化服务并提高零售商的利润。我是麦吉尔零售创新实验室(MRIL)的两名联合董事之一。我们正在与Couche-Tard/Circle K(全球最大的便利店零售商之一)合作开设一个现场零售实验室。MRIL将包括几项技术进步，如智能摄像头、货架重量审查器和无摩擦技术。这些技术将使我们能够收集新的数据来源，最终可以帮助我们加深对客户行为的理解，并改进零售运营决策(例如，价格、有针对性的促销、分类策略)。在我目前对数据科学和零售分析的研究基础上，在这个为期五年的综合研究计划中，我的目标是利用数据科学技术来改善零售实践，同时改善社会福利。研究计划分为四个模块：(1)使零售实践适应大流行后的环境，(2)无摩擦零售运营，(3)为社会公益轻推客户，以及(4)使用AR和VR技术在零售业进行A/B测试。这四个研究模块的目标是开发数据驱动的决策支持工具，将数据科学和新技术相结合，以改善零售实践，增强客户体验，并造福社会。拟议的研究计划将是首批开发框架的项目之一，该框架可以帮助加拿大零售业，同时还可以改善客户体验，并保持对社会公益和社会效益的强烈关注。除了开发新的数据驱动模型和算法外，我们还将把最新技术应用于大流行后的零售格局。最后，我们将专注于开发基于我们的理论见解的现实世界系统，这些系统可以显著影响零售商、消费者和整个社会。