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

点击翻译按钮获取中文摘要

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.