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In recent years, web-based retailers have been taking over a growing market share from traditional brick and mortar retailers. One of the advantages leveraged by online retail is its ability to personalize the customer journey by analyzing the massive amounts of data that can be acquired easily in a digital environment. For example, click-streams from a web shop can help to identify a customer's interests in order to generate individual recommendations. To keep up, physical retailers, too, have to transform into the digital world. The main requirement for this are suitable data acquisition methods as well as resulting applications that are viable in an offline setting. In this paper, we investigate how recent technologies from the field of Computer Vision can overcome the data acquisition bottleneck and allow for data-based innovations that help traditional retailers to improve their customers' shopping experience and consequently to strengthen their market position. To this end, we introduce a conceptual tracking system for offline retail stores. Its purpose is to generate movement tracks over time for individual customers, from the time the supermarket is entered until it is left again. The acquired data allow for several data-based applications that can achieve similar goals as their counterparts in online retail.
Hernandez et al. (Mon,) studied this question.