Publication Type

Journal Article

Version

publishedVersion

Publication Date

4-2017

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.

Keywords

Shopping behavior, RFID, Backscatter communication

Discipline

Databases and Information Systems | Software Engineering | Systems Architecture

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE/ACM Transactions on Networking

Volume

25

Issue

4

First Page

2405

Last Page

2418

ISSN

1063-6692

Identifier

10.1109/TNET.2017.2689063

Publisher

Institute of Electrical and Electronics Engineers (IEEE) / Association for Computing Machinery (ACM)

Additional URL

https://doi.org/10.1109/TNET.2017.2689063

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