18/10/2025 –, Track 02 - E04, A01
Idioma: English
AI and dynamic pricing are transforming traditional pricing strategies and expanding rapidly. In a subtle and clever game, algorithms analyze your behavior and preferences to determine the exact price you are willing to pay. It's no longer just the big companies controlling the board; now, the power lies in a complex network of data that watches your every click. Are you ready to challenge the established rules and uncover the secrets of the powerful game of dynamic pricing?
In this keynote, we will analyze how different machine learning algorithms meticulously analyze your behavior, preferences, and personal data to infer the exact price you are willing to pay to maximize business profits. Should I make an offer to my customer to prevent them from canceling their subscription, or should I keep increasing the price because they will never cancel it? Should I sell this product at a higher price or discount it because, otherwise, they wouldn't buy it?
Different approaches to dynamic pricing will be addressed, ranging from traditional techniques based on elasticity calculations through classical Machine Learning algorithms to less known but equally useful techniques such as Bayesian approaches or reinforcement learning systems through contextual bandits.
Machine Learning and Artificial Intelligence (ML, deep learning, AI ethics, generative models...)
Temáticas adicionales:Data Science and Data Engineering (analytics, visualization, pipelines, data engineering, notebooks...)
Nivel de la propuesta:Intermediate (it is necessary to understand the related bases to go into detail)
Soy ingeniero informático con formación en Big Data e Inteligencia Artificial. A lo largo de mi carrera profesional he trabajado como investigador en el ámbito del machine learning y como científico de datos en diferentes sectores.
R. Mena-Yedra is a senior data scientist. He holds a PhD in computing from the Universitat Politècnica de Catalunya (UPC) in Barcelona, Spain. His expertise lies in industrial data science, where he has applied AI/ML techniques across various domains including energy demand modelling, automatic control of microalgae photobioreactors, transportation and mobility research, and has made contributions to the financial industry and cheminformatics. With his diverse experience, he currently works in the AI industry creating innovative solutions to complex problems.