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Customer Segmentation through Path Reconstruction

This paper deals with the automatic classification of customers on the basis of their movements around a sports shop center. We start by collecting coordinates from customers while they visit the store. Consequently, any costumer’s path through the shop is formed by a list of coordinates, obtained w...

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Detalles Bibliográficos
Autor principal: Carbajal, Santiago García
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001414/
https://www.ncbi.nlm.nih.gov/pubmed/33809118
http://dx.doi.org/10.3390/s21062007
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author Carbajal, Santiago García
author_facet Carbajal, Santiago García
author_sort Carbajal, Santiago García
collection PubMed
description This paper deals with the automatic classification of customers on the basis of their movements around a sports shop center. We start by collecting coordinates from customers while they visit the store. Consequently, any costumer’s path through the shop is formed by a list of coordinates, obtained with a frequency of one measurement per minute. A guess about the trajectory is constructed, and a number of parameters are calculated before performing a Clustering Process. As a result, we can identify several types of customers, and the dynamics of their behavior inside the shop. We can also monitor the state of the shop, identify different situations that appear during limited periods of time, and predict peaks in customer traffic.
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spelling pubmed-80014142021-03-28 Customer Segmentation through Path Reconstruction Carbajal, Santiago García Sensors (Basel) Technical Note This paper deals with the automatic classification of customers on the basis of their movements around a sports shop center. We start by collecting coordinates from customers while they visit the store. Consequently, any costumer’s path through the shop is formed by a list of coordinates, obtained with a frequency of one measurement per minute. A guess about the trajectory is constructed, and a number of parameters are calculated before performing a Clustering Process. As a result, we can identify several types of customers, and the dynamics of their behavior inside the shop. We can also monitor the state of the shop, identify different situations that appear during limited periods of time, and predict peaks in customer traffic. MDPI 2021-03-12 /pmc/articles/PMC8001414/ /pubmed/33809118 http://dx.doi.org/10.3390/s21062007 Text en © 2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Technical Note
Carbajal, Santiago García
Customer Segmentation through Path Reconstruction
title Customer Segmentation through Path Reconstruction
title_full Customer Segmentation through Path Reconstruction
title_fullStr Customer Segmentation through Path Reconstruction
title_full_unstemmed Customer Segmentation through Path Reconstruction
title_short Customer Segmentation through Path Reconstruction
title_sort customer segmentation through path reconstruction
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001414/
https://www.ncbi.nlm.nih.gov/pubmed/33809118
http://dx.doi.org/10.3390/s21062007
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