Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/200970
Title: Design and Implementation of an RFID-Based Customer Shopping Behavior Mining System
Authors: Zimu Zhou;Longfei Shangguan;Xiaolong Zheng;Lei Yang;Yunhao Liu
Year: 2017
Publisher: IEEE
Abstract: Shopping behavior data is of great importance in understanding the effectiveness of marketing and merchandising campaigns. Online clothing stores are capable of capturing customer shopping behavior by analyzing the click streams and customer shopping carts. Retailers with physical clothing stores, however, still lack effective methods to comprehensively identify shopping behaviors. In this paper, we show that backscatter signals of passive RFID tags can be exploited to detect and record how customers browse stores, which garments they pay attention to, and which garments they usually pair up. The intuition is that the phase readings of tags attached to items will demonstrate distinct yet stable patterns in a time-series when customers look at, pick out, or turn over desired items. We design ShopMiner, a framework that harnesses these unique spatial-temporal correlations of time-series phase readings to detect comprehensive shopping behaviors. We have implemented a prototype of ShopMiner with a COTS RFID reader and four antennas, and tested its effectiveness in two typical indoor environments. Empirical studies from two-week shopping-like data show that ShopMiner is able to identify customer shopping behaviors with high accuracy and low overhead, and is robust to interference.
URI: http://localhost/handle/Hannan/200970
volume: 25
issue: 4
More Information: 2405,
2418
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7896633.pdf3.05 MBAdobe PDF
Title: Design and Implementation of an RFID-Based Customer Shopping Behavior Mining System
Authors: Zimu Zhou;Longfei Shangguan;Xiaolong Zheng;Lei Yang;Yunhao Liu
Year: 2017
Publisher: IEEE
Abstract: Shopping behavior data is of great importance in understanding the effectiveness of marketing and merchandising campaigns. Online clothing stores are capable of capturing customer shopping behavior by analyzing the click streams and customer shopping carts. Retailers with physical clothing stores, however, still lack effective methods to comprehensively identify shopping behaviors. In this paper, we show that backscatter signals of passive RFID tags can be exploited to detect and record how customers browse stores, which garments they pay attention to, and which garments they usually pair up. The intuition is that the phase readings of tags attached to items will demonstrate distinct yet stable patterns in a time-series when customers look at, pick out, or turn over desired items. We design ShopMiner, a framework that harnesses these unique spatial-temporal correlations of time-series phase readings to detect comprehensive shopping behaviors. We have implemented a prototype of ShopMiner with a COTS RFID reader and four antennas, and tested its effectiveness in two typical indoor environments. Empirical studies from two-week shopping-like data show that ShopMiner is able to identify customer shopping behaviors with high accuracy and low overhead, and is robust to interference.
URI: http://localhost/handle/Hannan/200970
volume: 25
issue: 4
More Information: 2405,
2418
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7896633.pdf3.05 MBAdobe PDF
Title: Design and Implementation of an RFID-Based Customer Shopping Behavior Mining System
Authors: Zimu Zhou;Longfei Shangguan;Xiaolong Zheng;Lei Yang;Yunhao Liu
Year: 2017
Publisher: IEEE
Abstract: Shopping behavior data is of great importance in understanding the effectiveness of marketing and merchandising campaigns. Online clothing stores are capable of capturing customer shopping behavior by analyzing the click streams and customer shopping carts. Retailers with physical clothing stores, however, still lack effective methods to comprehensively identify shopping behaviors. In this paper, we show that backscatter signals of passive RFID tags can be exploited to detect and record how customers browse stores, which garments they pay attention to, and which garments they usually pair up. The intuition is that the phase readings of tags attached to items will demonstrate distinct yet stable patterns in a time-series when customers look at, pick out, or turn over desired items. We design ShopMiner, a framework that harnesses these unique spatial-temporal correlations of time-series phase readings to detect comprehensive shopping behaviors. We have implemented a prototype of ShopMiner with a COTS RFID reader and four antennas, and tested its effectiveness in two typical indoor environments. Empirical studies from two-week shopping-like data show that ShopMiner is able to identify customer shopping behaviors with high accuracy and low overhead, and is robust to interference.
URI: http://localhost/handle/Hannan/200970
volume: 25
issue: 4
More Information: 2405,
2418
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7896633.pdf3.05 MBAdobe PDF