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Consumer behavior analysis based on Internet of Things platform and the development of precision marketing strategy for fresh food e-commerce
The traditional approach to e-commerce marketing encounters challenges in effectively extracting and utilizing user data, as well as analyzing and targeting specific user segments. This manuscript aims to address these limitations by proposing the establishment of a consumer behavior analysis system...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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PeerJ Inc.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496010/ https://www.ncbi.nlm.nih.gov/pubmed/37705616 http://dx.doi.org/10.7717/peerj-cs.1531 |
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author | Zhang, Mengmeng |
author_facet | Zhang, Mengmeng |
author_sort | Zhang, Mengmeng |
collection | PubMed |
description | The traditional approach to e-commerce marketing encounters challenges in effectively extracting and utilizing user data, as well as analyzing and targeting specific user segments. This manuscript aims to address these limitations by proposing the establishment of a consumer behavior analysis system based on an Internet of Things (IoT) platform. The system harnesses the potential of radio frequency identification devices (RFID) technology for product identification encoding, thus facilitating the monitoring of product sales processes. To categorize consumers, the system incorporates a k-means algorithm within its architectural framework. Furthermore, a similarity metric is employed to evaluate the gathered consumption information and refine the selection strategy for initial clustering centers. The proposed methodology is subjected to rigorous testing, revealing its effectiveness in resolving the issue of insufficient differentiation between customer categories after clustering. Across varying values of k, the average false recognition rate experiences a notable reduction of 20.6%. The system consistently demonstrates rapid throughput and minimal overall latency, boasting an impressive processing time of merely 2 ms, thereby signifying its exceptional concurrent processing capability. Through the implementation of the proposed system, the opportunity for further target market segmentation arises, enabling the establishment of core market positioning and the formulation of distinct and precise marketing strategies tailored to diverse consumer cohorts. This pioneering approach introduces an innovative and efficient methodology that e-commerce enterprises can embrace to amplify their marketing endeavors. |
format | Online Article Text |
id | pubmed-10496010 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104960102023-09-13 Consumer behavior analysis based on Internet of Things platform and the development of precision marketing strategy for fresh food e-commerce Zhang, Mengmeng PeerJ Comput Sci Data Mining and Machine Learning The traditional approach to e-commerce marketing encounters challenges in effectively extracting and utilizing user data, as well as analyzing and targeting specific user segments. This manuscript aims to address these limitations by proposing the establishment of a consumer behavior analysis system based on an Internet of Things (IoT) platform. The system harnesses the potential of radio frequency identification devices (RFID) technology for product identification encoding, thus facilitating the monitoring of product sales processes. To categorize consumers, the system incorporates a k-means algorithm within its architectural framework. Furthermore, a similarity metric is employed to evaluate the gathered consumption information and refine the selection strategy for initial clustering centers. The proposed methodology is subjected to rigorous testing, revealing its effectiveness in resolving the issue of insufficient differentiation between customer categories after clustering. Across varying values of k, the average false recognition rate experiences a notable reduction of 20.6%. The system consistently demonstrates rapid throughput and minimal overall latency, boasting an impressive processing time of merely 2 ms, thereby signifying its exceptional concurrent processing capability. Through the implementation of the proposed system, the opportunity for further target market segmentation arises, enabling the establishment of core market positioning and the formulation of distinct and precise marketing strategies tailored to diverse consumer cohorts. This pioneering approach introduces an innovative and efficient methodology that e-commerce enterprises can embrace to amplify their marketing endeavors. PeerJ Inc. 2023-08-28 /pmc/articles/PMC10496010/ /pubmed/37705616 http://dx.doi.org/10.7717/peerj-cs.1531 Text en ©2023 Zhang https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Data Mining and Machine Learning Zhang, Mengmeng Consumer behavior analysis based on Internet of Things platform and the development of precision marketing strategy for fresh food e-commerce |
title | Consumer behavior analysis based on Internet of Things platform and the development of precision marketing strategy for fresh food e-commerce |
title_full | Consumer behavior analysis based on Internet of Things platform and the development of precision marketing strategy for fresh food e-commerce |
title_fullStr | Consumer behavior analysis based on Internet of Things platform and the development of precision marketing strategy for fresh food e-commerce |
title_full_unstemmed | Consumer behavior analysis based on Internet of Things platform and the development of precision marketing strategy for fresh food e-commerce |
title_short | Consumer behavior analysis based on Internet of Things platform and the development of precision marketing strategy for fresh food e-commerce |
title_sort | consumer behavior analysis based on internet of things platform and the development of precision marketing strategy for fresh food e-commerce |
topic | Data Mining and Machine Learning |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496010/ https://www.ncbi.nlm.nih.gov/pubmed/37705616 http://dx.doi.org/10.7717/peerj-cs.1531 |
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