ShopMiner: Mining customer shopping behavior in physical clothing stores with passive RFIDs

Shopping behavior data are of great importance to understand the effectiveness of marketing and merchandising efforts. Online clothing stores are capable capturing customer shopping behavior by analyzing the click stream and customer shopping carts. Retailers with physical clothing stores, however,...

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Main Authors: SHANGGUAN, Longfei, ZHOU, Zimu, ZHENG, Xiaolong, YANG, Lei, LIU, Yunhao, HAN, Jinsong
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Language:English
Published: Institutional Knowledge at Singapore Management University 2015
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Online Access:https://ink.library.smu.edu.sg/sis_research/4749
https://ink.library.smu.edu.sg/context/sis_research/article/5752/viewcontent/shopminer_sensys15.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-57522020-01-16T10:36:40Z ShopMiner: Mining customer shopping behavior in physical clothing stores with passive RFIDs SHANGGUAN, Longfei ZHOU, Zimu ZHENG, Xiaolong YANG, Lei LIU, Yunhao HAN, Jinsong Shopping behavior data are of great importance to understand the effectiveness of marketing and merchandising efforts. Online clothing stores are capable capturing customer shopping behavior by analyzing the click stream and customer shopping carts. Retailers with physical clothing stores, however, still lack effective methods to identify comprehensive 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 items of clothes they pay attention to, and which items of clothes they usually match with. The intuition is that the phase readings of tags attached on desired items will demonstrate distinct yet stable patterns in the time-series when customers look at, pick up 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 twoweek shopping-like data show that ShopMiner could achieve high accuracy and efficiency in customer shopping behavior identification. 2015-11-04T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4749 info:doi/10.1145/2809695.2809710 https://ink.library.smu.edu.sg/context/sis_research/article/5752/viewcontent/shopminer_sensys15.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 Digital Communications and Networking OS and Networks
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
Digital Communications and Networking
OS and Networks
spellingShingle Shopping behavior
RFID
Backscatter communication
Digital Communications and Networking
OS and Networks
SHANGGUAN, Longfei
ZHOU, Zimu
ZHENG, Xiaolong
YANG, Lei
LIU, Yunhao
HAN, Jinsong
ShopMiner: Mining customer shopping behavior in physical clothing stores with passive RFIDs
description Shopping behavior data are of great importance to understand the effectiveness of marketing and merchandising efforts. Online clothing stores are capable capturing customer shopping behavior by analyzing the click stream and customer shopping carts. Retailers with physical clothing stores, however, still lack effective methods to identify comprehensive 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 items of clothes they pay attention to, and which items of clothes they usually match with. The intuition is that the phase readings of tags attached on desired items will demonstrate distinct yet stable patterns in the time-series when customers look at, pick up 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 twoweek shopping-like data show that ShopMiner could achieve high accuracy and efficiency in customer shopping behavior identification.
format text
author SHANGGUAN, Longfei
ZHOU, Zimu
ZHENG, Xiaolong
YANG, Lei
LIU, Yunhao
HAN, Jinsong
author_facet SHANGGUAN, Longfei
ZHOU, Zimu
ZHENG, Xiaolong
YANG, Lei
LIU, Yunhao
HAN, Jinsong
author_sort SHANGGUAN, Longfei
title ShopMiner: Mining customer shopping behavior in physical clothing stores with passive RFIDs
title_short ShopMiner: Mining customer shopping behavior in physical clothing stores with passive RFIDs
title_full ShopMiner: Mining customer shopping behavior in physical clothing stores with passive RFIDs
title_fullStr ShopMiner: Mining customer shopping behavior in physical clothing stores with passive RFIDs
title_full_unstemmed ShopMiner: Mining customer shopping behavior in physical clothing stores with passive RFIDs
title_sort shopminer: mining customer shopping behavior in physical clothing stores with passive rfids
publisher Institutional Knowledge at Singapore Management University
publishDate 2015
url https://ink.library.smu.edu.sg/sis_research/4749
https://ink.library.smu.edu.sg/context/sis_research/article/5752/viewcontent/shopminer_sensys15.pdf
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