A Large Scale Trajectory Dataset for Shopper Behaviour Understanding

Gabellini, Patrizia; D’Aloisio, Mauro; Fabiani, Matteo; Placidi, Valerio

doi:10.1007/978-3-030-30754-7_29

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…On top of it, the lights of a Christmas tree run different special effects because of the emotions of the subjects around the tree. Gabellini et al [12] provided the scientific community with a large scale trajectory of shopper movements in stores employing a real-time locating system with Ultra-Wideband (UWB). Vaira et al [25] put in place a system to avoid the so-called OOS (out of stock) problem in stores.…”

Section: Related Techniquesmentioning

confidence: 99%

A Saliency-Based Technique for Advertisement Layout Optimisation to Predict Customers’ Behaviour

Bruno

Lancette

Zhang

et al. 2021

Pattern Recognition. ICPR International Workshops and Challenges

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Section: Related Techniquesmentioning

confidence: 99%

A Saliency-Based Technique for Advertisement Layout Optimisation to Predict Customers’ Behaviour

Bruno

Lancette

Zhang

et al. 2021

Pattern Recognition. ICPR International Workshops and Challenges

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“…Understanding shoppers behavior is a hot topic in the management field. Recently, the first large-scale trajectory data set for shopper behavior understanding has been collected [ 1 ], reporting results for more than ten million shoppers in Germany. Some empirical patterns about in-store behaviors have been described and confirmed in the past [ 2 ], while others like the correlation between longer in-store travel distance and unplanned spending are still controversial [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Customer Segmentation through Path Reconstruction

Carbajal

2021

Sensors

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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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“…Understanding shoppers behavior is a hot topic in the management field. Recently, the first large scale trajectory data set for shopper behaviour understanding has been collected [1], reporting results for more than ten million shoppers in Germany. Some empirical patterns about in-store behaviors have been described and confirmed in the past, [2], while others like the correlation between longer in-store travel distance and unplanned spending are still controversial [3].…”

Section: Introductionmentioning

confidence: 99%

Customer Segmentation Through Path Reconstruction

García¹

2020

Preprint

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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

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A Large Scale Trajectory Dataset for Shopper Behaviour Understanding

Cited by 6 publications

References 20 publications

A Saliency-Based Technique for Advertisement Layout Optimisation to Predict Customers’ Behaviour

A Saliency-Based Technique for Advertisement Layout Optimisation to Predict Customers’ Behaviour

Customer Segmentation through Path Reconstruction

Customer Segmentation Through Path Reconstruction

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