Inaugural address: Gui Liberali

On Friday, the 25th of May 2018, Gui Liberali, Endowed Professor of Digital Marketing presented his inaugural address entitled  'Learning with a purpose: The balancing acts of machine learning and individuals in the digital society'. 

Professor Liberali’s address was preceded by a two-day Workshop on Multi-Armed Bandits and Learning Algorithms. Participants from all around the world actively working in the development and application of multi-armed bandits and learning algorithms in various disciplines attended. This forum will particularly encouraged discussions of the approaches that have evolved in computer science, management science, operations research and statistics.

About the Inaugural Address

The Internet transformed some of the most basic processes in our society, such as trade, payment, and communication. We now have more access to products, services, and opinions than we ever had, but at the same time, our behaviour is tracked more closely than ever. For example, large online retailers offer hundreds of thousands of products, and can readily observe in great detail how each consumer interacts with any them. They can also rapidly deploy individual-level, in-vivo, randomized online experiments at population scale to test concepts, insights and communication approaches which can lead to better services and products. However, there are often billions of possibilities, such as product-consumer combinations for product recommendations. The scale and complexity of these experiments create amazing challenges.
Thus, firms face balancing acts. For example, they need to constantly choose between profiting from what they already know about consumers (such as the genres of movies already watched) and learning more about the same consumers (such as by recommending a movie of an untested genre). Consumers are also facing their own balancing acts. In the digital society, we inevitably leave digital footprints but we have some discretion in terms of how much information we want to keep private. Typically, a consumer that is more open to sharing her preferences is also exposed to higher risks, but at the same time she can get better access to products and services she needs.
In this talk, Professor Liberali shows how advances in machine learning and reinforcement learning can alleviate these challenging balancing acts. After providing some background information, he briefly describes how these methods are helping firms and consumers, illustrating with his own work. Then he indicated key exciting areas for future research. He concluded this address by illustrating the implications for marketing science and prescriptive analytics more generally.

About Gui Liberali

Gui Liberali is an Endowed Professor of Digital Marketing at RSM. He holds a doctorate in marketing and a BSc in computer science. His research interests include optimal learning, multi-armed bandits, digital experimentation, natural language processing, morphing theory and applications (e.g., website morphing, advertisement morphing), dynamic programming, machine learning, and product line optimisation. His work has appeared in journals such as Marketing Science, Management Science, International Journal of Marketing Research, Sloan Management Review, and European Journal of Operational Research. He is Vice-President for Membership of the INFORMS Society for Marketing Science (ISMS) for the 2018-2019 term.
He is the founder and director of the Erasmus Centre for Optimization of Digital Experiments (eCode), with the goal of developing and disseminating machine learning and optimisation methods, concepts, and tools that help firms improve the way they use the internet to connect with and interact with their consumers. This includes new ways to design and optimise digital experiments, new multi-armed bandits algorithms (such as website morphing), randomised controlled trials of marketing methods and tools (A/B experiments), online field experiments, and behavioural analytics.

Photos: Chris Gorzeman / Capital Images