As a research domain, the retail sector has always had many appealing features, such as its size, its multi-faceted and dynamic nature, the possibility for researchers to exploit their own domain knowledge, and an extensive coverage by business analysts. In addition, the above-average availability of good-quality data has historically been an additional selling point to empirical researchers. The paper considers to what extent the latter still holds, and explores a number of additional opportunities and challenges that emerge from the ongoing big data revolution. This is done from five perspectives: retail managers, retailing researchers, publicpolicy makers, investors, and retailing educators.
The papers that have appeared in IJRM’s invited “EMAC Distinguished Scholar” series have covered a wide range of topics. A common theme in most of these contributions is that the authors have taken the opportunity to reflect not only on the past, but also (and even more so) on the future of the field. In so doing, they have focused either on the marketing discipline as a whole (see, e.g., the contributions in the current issue by Don Lehmann (2019) and Roland Rust (2019)) or on the sub-field they are most closely associated with (see, e.g., Wierenga (2011) on managerial decision making, or Lilien (2016) on the B2B knowledge gap). In terms of my own scientific career, my first professional publication was on the survival rate of retail stores (Dekimpe & Morrison, 1991), and throughout the subsequent years/decades, most of my work has continued to focus on retailing-related issues. In this article, I will first reflect on why I feel the retailing sector is a fascinating and fertile ground for managerially relevant academic research. I will argue how the above-average availability of good-quality data has historically been a key selling point for empirical researchers. After that, I will consider to what extent this still holds, and explore a number of additional opportunities and challenges that emerge from the ongoing, and rapidly accelerating, big data revolution. I will do so from five perspectives: retail managers, retailing researchers, as well as public-policy makers, investors, and retailing educators. The retailing sector is an ideal setting for such a discussion, as analysts like to present the sector as a poster child for all the benefits that one can envision from the use of big data. Industry reports have proclaimed that retailing is “one of the hottest markets for big data analytics” (Ingram Micro, 2018), that “big data is especially promising and differentiating for retailers” (IBM Analytics, 2018), or that big data will be “a complete game changer in the retail sector” (Forbes, 2015). A 2011 McKinsey report put forward that big data have the potential to increase retailers’ operating margins with up to 60%, and cause a sector-wide annual productivity gain of up to 1% in the next five years (McKinsey, 2011). Similar enthusiastic endorsements have appeared in the academic literature. Grewal, Roggeveen, and Nordfält (2017), for example, argue how new technologies along with big data/predictive analytics will cause a quantum leap in retailers’ understanding of the shopping process.