Delve: How Predictive Data Analysis Illuminates the Future of Retail, with Maxime Cohen
Artificial Intelligence innovation thrives in an environment where business arenas, fundamental research, and thought leadership overlap. As seen in recent AI initiatives in Montreal, determining the capacities of AI and related data analytics applications is essential to understanding how they will play out in the wider world, whether analyzing healthcare data or implementing predictive analytics in retail. On the Delve podcast, Desautels Professor Maxime Cohen demystifies how retailers can use data analytics to predict demand, make operational decisions, and boost revenue.
“It’s impossible for the human brain to process so much information and to find all the hidden patterns and correlations between different types of features in order to make accurate predictions,” says Cohen. “That's why in the specific case of demand prediction in retail, machine learning algorithms are very useful and have been successfully applied to get very high prediction demands.”
In his recent book, Demand Prediction in Retail – A Practical Guide to Leverage Data and Predictive Analytics, published by Springer this year, Cohen and his co-authors (Paul-Emile Gras, Arthur Pentecoste and Renyu Zhang) provide a detailed guide for data scientists and students of business analytics. The book offers business insights to a wider audience as well, pointing to something essential for all retailers today: how to leverage their historical data to predict future demand for their products.
For more insights, listen to the full interview with Professor Maxime Cohen on the Delve podcast.
Founded in 2019, Delve is the official thought leadership publication of McGill University’s Desautels Faculty of Management. Under the direction of Professor Saku Mantere, inaugural Editor-in-Chief, Delve features the latest in management thinking that stretches perspectives, sparks new ideas, and brings clarity to decision-makers at all levels and across sectors.