Over the course of recent years, Radio Frequency Identification (RFID) technology has been applied in several different business domains to improve a diverse array of practical applications. For example, handheld RFID readers combined with passive RFID tags are used to perform fast and accurate stocktakes for fashion retailers. However, while this approach enables efficient inventory management, automatic localization of RFID-tagged goods in stores is still an open problem. To tackle this problem, we equip fixtures (e.g., shelves, tables,..) with reference RFID tags and use data we collect during typical RFID-based stocktakes to map articles to fixtures. Knowing the location of goods within a store enables the implementation of several practical applications, such as automated Money Mapping (e.g., creating a heat map of sales across fixtures) or visual merchandising evaluations (e.g., monitoring sales of fixtures before, during, and after the implementation visual merchandising strategies). Specifically, we conduct (i) controlled lab experiments and (ii) a case-study in two fashion retail stores to evaluate our presented approaches for article-to-fixture predictions. The approaches are based on calculating distances between read event time series of article and reference tags using dynamic time warping, and clustering of read events using DBSCAN. We find that we can use read events collected during RFID-based stocktakes to assign articles to fixtures with an accuracy of more than 90% in several of our experiments. Hence, in this paper we present an exploratory venture into novel and practical RFID-based applications, beyond the scope of stock management.