Data science for pedestrian and high street retailing as a framework for advancing urban informatics to individual scales

Abstract

Background In this paper, we consider the applicability of the customer journey framework from retailing as a driver for urban informatics at individual scales within urban science. The customer journey considers shopper experiences in the context of shopping paths, retail service spaces, and touch-points that draw them into contact. Around this framework, retailers have developed sophisticated data science for observation, identification, and measurement of customers in the context of their shopping behavior. This knowledge supports broad data-driven understanding of customer experiences in physical spaces, economic spaces of decision and choice, persuasive spaces of advertising and branding, and inter-personal spaces of customer-staff interaction. Method We review the literature on pedestrian and high street retailing, and on urban informatics. We investigate whether the customer journey could be usefully repurposed for urban applications. Specifically, we explore the potential use of the customer journey framework for producing new insight into pedestrian behavior, where a sort of empirical hyperopia has long abounded because data are always in short supply. Results Our review addresses how the customer journey might be used as a structure for examining how urban walkers come into contact with the built environment, how people actively and passively sense and perceive ambient city life as they move, how pedestrians make sense of urban context, and how they use this knowledge to build cognition of city streetscapes. Each of these topics has relevance to walking studies specifically, but also to urban science more generally. We consider how retailing might reciprocally benefit from urban science perspectives, especially in extending the reach of retailers' insight beyond store walls, into the retail high streets from which they draw custom. Conclusion We conclude that a broad set of theoretical frameworks, data collection schemes, and analytical methodologies that have advanced retail data science closer and closer to individual-level acumen might be usefully applied to accomplish the same in urban informatics. However, we caution that differences between retailers’ and urban scientists’ viewpoints on privacy presents potential controversy.

Smart and Sentient Retail High Streets

“…Dealing with data is obviously a huge component of smart retailing. Data are one of the levers that retailers have to drive action in their business models [26]. Through data, we see one of the pronounced connections between smart retailing and smart cities.…”

“…Multi-sided platforms facilitate connections between (what are often third-party) actors, working to provide services for exchange [234]. In the context of a high street and the goal of instantiating it as a community platform, different actors (retailers, local government, chambers of commerce, advertisers, event coordinators, customers, would-be customers) could be drawn together-through the shared platform of a tangible high street [26] and the virtual platform of e-commerce applications or social networking applications [235]-to exchange recommendations and reviews, to purchase goods and services, and to engage in high street experiences. Much of the smart technology discussed in Section 3 is readily available to make various aspects of this platform realizable.…”

“…In urban studies, as well as retail studies, quite a lot of research has already empirically investigated the nature of pedestrians' and shoppers' journeys along high streets, and has tied those journeys to retail KPIs (see [246] and [247] for a review in pedestrian studies; and [26] for an overview of the state of the art in retailing). In many cases, customer journeys can be isolated to individual pedestrians and to their potential shopping motivation and intent on the high street.…”

“…By mapping these states to time (e.g., as a customer journey progresses), Schmidt et al [120] suggest that contextual composites such as habits, goals, spontaneity, and group dynamics and sociality can be derived (p. 895). This shift in consideration has potentially significant implications for how one might consider sentient retail high streets if we consider, for example, that much of the sensing that might reveal user context and situational context is embedded in high streets [26,246].…”

“…Deep learning, for example, is a form of AI designed to approximate human sentience by mimicking the ways in which humans perceive and interpret their surroundings. Increasingly, deep learning is being used to build AI-driven analyses of streetscapes [26], with the motivation that various features of those settings could be identified, traced, contextualized, interpreted, and possibly even be explained by sentient-like computer vision. These technologies could see very widespread use for retailing, essentially enabling the lens of a camera to become a smart interpreter of activities that pass within its field of view (Figure 11).…”