Webinars
Beyond Personalization: Using Reinforcement Learning to Drive Customer Engagement and Retention
Most AI personalization systems optimize for immediate clicks and conversions. Reinforcement learning (RL) offers a more powerful approach by continuously adapting customer experiences to maximize long-term value.
In this webinar, we introduce a new AI framework that helps RL systems learn from spending, engagement, and retention signals simultaneously. Using a real-world case study, we show how this approach delivered a 39% increase in 30-day spending while improving the ability to identify and re-engage at-risk customers.
Attendees will leave with practical insights into how advanced AI techniques can improve personalization, retention, and long-term customer value across digital platforms.
speaker
Ta-Wei (David) Huang is an Assistant Professor of Marketing at the National University of Singapore. His research integrates causal inference and machine learning to address fundamental challenges in modern customer management. He studies how firms can design and personalize marketing interventions in complex decision environments, with applications in customer targeting, dynamic interventions, and privacy-preserving analytics. Methodologically, his work combines tools from causal inference, machine learning, and optimization to develop scalable methods for data-driven marketing decisions. He received his Ph.D. in Marketing from Harvard Business School.