Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Customer Segmentation Through Path Reconstruction

Version 1 : Received: 27 November 2020 / Approved: 30 November 2020 / Online: 30 November 2020 (15:57:36 CET)

How to cite: García, S. Customer Segmentation Through Path Reconstruction. Preprints 2020, 2020110736 (doi: 10.20944/preprints202011.0736.v1). García, S. Customer Segmentation Through Path Reconstruction. Preprints 2020, 2020110736 (doi: 10.20944/preprints202011.0736.v1).

Abstract

With the rapid development of smart phones, tablets and their operative systems, many positioning enabled sensors have been built into these devices. Users can now accurately fix their location according to the function of GPS receivers. For indoor environments, as in the case we are studying, WiFi based positioning is preferred to GPS due to the attenuation or obstruction of signals. This paper deals with the automatic classification of customers in a Sports Shop Center on the basis of their movements around the shop's premises. To achieve this goal, we start by collecting (x,y) 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. Then, a guess about the full trajectory is constructed and a number of parameters about these trajectories is calculated before performing an Unsupervised Learning Clustering Process. As a result, we can identify several types of customers, and the dynamics of their behavior inside the shop. This information is of great value to the company, to be used both in the long term and also in short periods of time, monitoring the current state of the shop at any moment, identifying different types of situation appearing during restricted periods, or predicting customer flow conditions

Subject Areas

Path analysis; in-store behavior; customer Clustering; indoor positioning; trajectory analysis; multilateration

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