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Technical Note

Customer Segmentation through Path Reconstruction

Computer Science Department, Campus de Viesques, University of Oviedo, Office 1.b.15, Gijón, 33003 Oviedo, Asturias, Spain
Academic Editor: Sylvie Le Hegarat-Mascle
Sensors 2021, 21(6), 2007; https://doi.org/10.3390/s21062007
Received: 27 November 2020 / Revised: 5 March 2021 / Accepted: 6 March 2021 / Published: 12 March 2021
(This article belongs to the Section Intelligent Sensors)
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. View Full-Text
Keywords: path analysis; in-store behavior; customer clustering; indoor positioning; trajectory analysis; multilateration path analysis; in-store behavior; customer clustering; indoor positioning; trajectory analysis; multilateration
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MDPI and ACS Style

Carbajal, S.G. Customer Segmentation through Path Reconstruction. Sensors 2021, 21, 2007. https://doi.org/10.3390/s21062007

AMA Style

Carbajal SG. Customer Segmentation through Path Reconstruction. Sensors. 2021; 21(6):2007. https://doi.org/10.3390/s21062007

Chicago/Turabian Style

Carbajal, Santiago G. 2021. "Customer Segmentation through Path Reconstruction" Sensors 21, no. 6: 2007. https://doi.org/10.3390/s21062007

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