Analyzing consumers’ shopping behavior using RFID data and pattern mining

Takanobu Nakahara () and Katsutoshi Yada

Advances in Data Analysis and Classification, 2012, vol. 6, issue 4, 355-365

Abstract: The development of sensor networks has enabled detailed tracking of customer behavior in stores. Shopping path data which records each customer’s position and time information is attracting attention as new marketing data. However, there are no proposed marketing models which can identify good customers from huge amounts of time series data on customer movement in the store. This research aims to use shopping path data resulting from tracking customer behavior in the store, using information on the sequence of visiting each product zone in the store and staying time at each product zone, to find how they affect purchasing. To discover useful knowledge for store management, shopping paths data has been transformed into sequence data including information on visit sequence and staying times in the store, and LCMseq has been applied to them to extract frequent sequence patterns. In this paper, we find characteristic in-store behavior patterns of good customers by using actual data of a Japanese supermarket. Copyright Springer-Verlag Berlin Heidelberg 2012

Keywords: Sequential pattern mining; Path data; LCM sequence; Decision tree; Data mining; 68R05 (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1007/s11634-012-0117-z

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