Personalization, Forecasting, and Adoption

This unit integrates predictive and prescriptive techniques—recommendation, propensity, personalized pricing, forecasting, and optimization—with behavioral science to design interventions that influence customer behavior. It also addresses experimentation, interpretation, and organizational enablers (communication, trust, and adoption) to prepare learners to deploy and evaluate Behavioral AI solutions in business contexts.

Learning Objectives

• Analyze the trade-offs, evaluation metrics, and ethical implications of recommendation and propensity models to optimize targeted campaigns and personalized pricing strategies
• Apply forecasting and optimization methods to produce demand forecasts and translate them into actionable pricing and allocation decisions that incorporate behavioral drivers
• Design and conduct controlled experiments (A/B and randomized trials) to measure the causal impact of personalization and behavioral interventions, and interpret results using multilayered explanation techniques
• Develop communication and adoption plans that build stakeholder trust, address ethical and diversity considerations, and translate technical findings into clear business recommendations