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TopMarketing researchers who aim to understand consumer behavior have used data on results of purchasing behavior, such as customer purchase history data. Customer purchase history data contains information on when and which customers bought what products and how much was purchased. There has been much research on purchase behavior models using such data (Guadagni, 1983; Gupta, 1988). However, such data only shows the results of purchasing behavior, but does not necessarily clarify the purchase process. For example, such data does not help us understand how women (consumers) in stores visit the sales locations, which sales promotions they see, and how long they think, before making a purchase. Previously, this purchase process was treated as a black box in marketing research. In recent years, new technology innovations have changed this situation. In various places, data on the customer purchase process is being collected. Such data is called path data (Hui, 2009), which is attracting the attention of many researchers. Typical examples of path data are shopping path data that tracks customer movements in a store using RFID (Sorensen, 2003), eye tracking data that records eye movements at the time of purchase (Fox, 1998; Krugman, 1994), and click-stream data that records customer page views on the Internet (Bucklin, 2002; Montgomery, 2004). In this study, we use shopping path data that tracks customer movements in a store, and propose a new purchase behavior model, with the aim of discovering useful knowledge.