Research Projects

Retail Innovation Lab Steering Committee
Retail innovation lab Steering Committee: James Clark, Magnus Tägtström, Maxime Cohen, Saibal Ray, Marina Nikoghossian, Zahoor Chughtai, Colette Matthews (absent)

The lab is led by two research directors at McGill: Professor Maxime Cohen of the Desautels Faculty of Management and Professor James Clark of the Faculty of Engineering. Governed by a joint steering committee, preliminary research themes will involve helping customers make healthier, more sustainable choices, and finding the right balance between personalization and privacy in the shopping experience. Researchers will use state-of-the-art artificial intelligence methods, while ensuring stringent data privacy and confidentiality protocols, to improve demand forecasting and customer recommendations, as well as virtual reality to make it easier for customers to find what they are looking for. In addition to enabling ground-breaking research, the lab will allow McGill students to deepen their knowledge through on-site experiential learning in an innovative commercial environment.


 

Adapting retail practices to the post-pandemic landscape

Graduate student internship supported by Mitacs via the Accelerate program

The pandemic has impacted consumer behavior dramatically by spending less time shopping, shopping in larger quantities and minimizing physical contact. In this project, the MRIL will be working with Alimentation Couche-Tard to understand and predict the current behavior of customers to help them adapt their retail practices to the post-pandemic world. By leveraging both historical (pre-pandemic) and present-time data (during and post-pandemic), researchers at the MRIL will develop state-of-the-art machine-learning and artificial intelligence algorithms. More specifically, novel demand forecasting, tracking and influencing methods (e.g., nudging interventions, sending app notifications to customers) will be tested in terms of prediction accuracy and their possible application to personalization purposes. The main objective is to identify key features related to purchasing behaviour that can help retailers better predict customer demand in these uncertain times. 

Retail innovation lab: data science for socially responsible food choices

IVADO-funded project 

Led by a multidisciplinary team, this project will apply artificial intelligence techniques to study, implement, and validate systems for guiding customers to make healthy food choices in a convenience store setting, while being cognizant of privacy concerns.

Data visualization and social distancing

This project aims to reconstruct the customer shopping journey and identify the busiest zones of the store by analyzing point-of-sales data and camera-tracking trajectories (anonymized and privacy-preserving). Such valuable information will help retailers re-organize the store planogram to satisfy desired requirements, such as social distancing.

Undergraduate students from the Department of Electrical and Computer Engineering:

Maxime Cardinal
Alexis Franche
Alexa Normandin
Oliver James Murphy

Student co-supervisors: Derek Nowrouzezahrai and Maxime Cohen

Touchless retail experiences

Inspired by the paradigm shift caused by the pandemic on the interactions between retailers and consumers, this project will explore the implementation of a dynamic, touchless shopping experience in the form of an online and mobile application that will be deployed in the MRIL, with the goal of enhancing the customer experience.

Undergraduate students from the Department of Electrical and Computer Engineering:

Sophie Deng
Bin Yuan Sun
Ai Di Wang
Mia Zhou

Student co-supervisors: Derek Nowrouzezahrai and Maxime Cohen

 


Research Grants:

Funding source: IVADO Fundamental Research Project Grant
Project title: Retail Innovation Lab: Data Science for Socially Responsible Food Choices
Award amount: $221,000
Period: 2020-2022

Funding source: Mitacs Accelerate
Project title: Adapting Retail Practices to the Post-Pandemic Landscape
Award amount: $30,000
Period: 2020-2021


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