Evaluation of the Shopping Path to Distinguish Customers Using a RFID Dataset

Evaluation of the Shopping Path to Distinguish Customers Using a RFID Dataset

Takanobu Nakahara (Kansai University, Japan) and Katsutoshi Yada (Kansai University, Japan)
DOI: 10.4018/ijoci.2011100101
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Abstract

In this study, the authors use radio-frequency identification (RFID) data, which show the position of a shopping cart through an RFID tag attached to the shopping cart. The RFID data contain valuable information for marketing, such as shopping time and distance as well as the number of shelf visits. The authors analyze customers’ purchasing behavior and in-store movement information using POS data combined with RFID data. The purpose of this study is to discover a promising shopping path that can distinguish customers’ instore movements by sequential pattern analysis using RFID data. These shopping paths are extracted using a pattern mining method. Finally, shopping paths are used in the decision tree analysis to generate the rules that expressed customers’ in-store movements and purchasing characteristics. As a result, in this study, the authors propose useful suggestions for more efficient in-store area management.
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Marketing 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.

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