Design and implementation of an RFID-based customer shopping behavior mining system

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, h...

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Main Authors: ZHOU, Zimu, SHANGGUAN, Longfei, ZHENG, Xiaolong, YANG, Lei, LIU, Yunhao
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Language:English
Published: Institutional Knowledge at Singapore Management University 2017
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Online Access:https://ink.library.smu.edu.sg/sis_research/4535
https://ink.library.smu.edu.sg/context/sis_research/article/5538/viewcontent/tnet17_zhou.pdf
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spelling sg-smu-ink.sis_research-55382019-12-26T09:11:40Z Design and implementation of an RFID-based customer shopping behavior mining system ZHOU, Zimu SHANGGUAN, Longfei ZHENG, Xiaolong YANG, Lei LIU, Yunhao 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. 2017-04-12T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4535 info:doi/10.1109/TNET.2017.2689063 https://ink.library.smu.edu.sg/context/sis_research/article/5538/viewcontent/tnet17_zhou.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Shopping behavior RFID Backscatter communication Databases and Information Systems Software Engineering Systems Architecture
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Shopping behavior
RFID
Backscatter communication
Databases and Information Systems
Software Engineering
Systems Architecture
spellingShingle Shopping behavior
RFID
Backscatter communication
Databases and Information Systems
Software Engineering
Systems Architecture
ZHOU, Zimu
SHANGGUAN, Longfei
ZHENG, Xiaolong
YANG, Lei
LIU, Yunhao
Design and implementation of an RFID-based customer shopping behavior mining system
description 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.
format text
author ZHOU, Zimu
SHANGGUAN, Longfei
ZHENG, Xiaolong
YANG, Lei
LIU, Yunhao
author_facet ZHOU, Zimu
SHANGGUAN, Longfei
ZHENG, Xiaolong
YANG, Lei
LIU, Yunhao
author_sort ZHOU, Zimu
title Design and implementation of an RFID-based customer shopping behavior mining system
title_short Design and implementation of an RFID-based customer shopping behavior mining system
title_full Design and implementation of an RFID-based customer shopping behavior mining system
title_fullStr Design and implementation of an RFID-based customer shopping behavior mining system
title_full_unstemmed Design and implementation of an RFID-based customer shopping behavior mining system
title_sort design and implementation of an rfid-based customer shopping behavior mining system
publisher Institutional Knowledge at Singapore Management University
publishDate 2017
url https://ink.library.smu.edu.sg/sis_research/4535
https://ink.library.smu.edu.sg/context/sis_research/article/5538/viewcontent/tnet17_zhou.pdf
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