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 cross-sector 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 a 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 a demand prediction in retail, machine learning algorithms are very useful and have been successfully applied to get very high prediction demands.”
Delve is the official thought leadership platform of McGill University's Desautels Faculty of Management. Delve's Managing Editor, Robyn Fadden, is the host for this episode. You can find out more about Delve at delve.mcgill.ca. Subscribe to the Delve McGill podcast on all major podcast platforms, including Apple podcasts and Spotify, and follow Delve on: LinkedIn, Facebook, Twitter, Instagram, and YouTube.