In-store Customer Shopping Behavior Analysis by Utilizing RFID-enabled Shelf and Multilayer Perceptron Model

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27350%2F20%3A10245022" target="_blank" >RIV/61989100:27350/20:10245022 - isvavai.cz</a>

Result on the web

<a href="https://iopscience.iop.org/article/10.1088/1757-899X/803/1/012022" target="_blank" >https://iopscience.iop.org/article/10.1088/1757-899X/803/1/012022</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1757-899X/803/1/012022" target="_blank" >10.1088/1757-899X/803/1/012022</a>

Result language

angličtina

Original language name

In-store Customer Shopping Behavior Analysis by Utilizing RFID-enabled Shelf and Multilayer Perceptron Model

Original language description

Understanding customer shopping behavior in retail store is important to improve the customers&apos; relationship with the retailer, which can help to lift the revenue of the business. However, compared to online store, the customer browsing activities in the retail store is difficult to be analysed. Therefore, in this study the customer shopping behavior analysis (i.e., browsing activity) in retail store by utilizing radio frequency identification (RFID)-enabled shelf and machine learning model is proposed. First, the RFID technology is installed in the store shelf to monitor the movement tagged products. The dataset was gathered from receive signal strength (RSS) of the tags for different customer behavior scenario. The statistical features were extracted from RSS of tags. Finally, machine learning models were utilized to classify different customer shopping activities. The experiment result showed that the proposed model based on Multilayer Perceptron (MLP) outperformed other models by as much as 97.00%, 96.67%, 97.50%, and 96.57% for accuracy, precision, recall, and f-score, respectively. The proposed model can help the managers better understand what products customer interested in, so that can be utilized for product placement, promotion as well as relevant product recommendations to the customers. (C) Published under licence by IOP Publishing Ltd.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/TF03000053" target="_blank" >TF03000053: IoT Based Intelligent Store</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

IOP Conference Series: Materials Science and Engineering. Volume 803

ISBN

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ISSN

1757-8981

e-ISSN

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Number of pages

7

Pages from-to

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Publisher name

IOP Publishing

Place of publication

Bristol

Event location

Jogdžakarta

Event date

Nov 15, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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