Analyzing consumers’ shopping behavior using RFID data and pattern mining

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.     

Complex Formation Between Poly(dimethylsilane) and Cyclodextrins

Poly(dimethylsilane)s form inclusion complexes with cyclodextrins in high selectivity to give crystalline compounds. β‐Cyclodextrin forms complexes with poly(dimethylsilane)s of low molecular weight only, γ‐cyclodextrin with poly(dimethylsilane)s of high molecular weight in high yield, and α‐cyclodextrin does not form complexes with poly(dimethylsilane) at all. Complexes were isolated and characterized by spectroscopic methods and X‐ray diffraction.