Behavioral Modeling, Experimentation, and Adoption

This unit teaches applied behavioral AI techniques for influencing customer decisions by combining recommendation and propensity modeling, personalized pricing, and demand forecasting with behavioral science insights. Students will learn to design and evaluate experiments, interpret model outputs for stakeholder communication, and craft adoption strategies that address biases, trust, and ethical considerations—building on foundational skills from earlier units and preparing learners to scale solutions in the final unit.

Learning Objectives

• Analyze recommendation and propensity model outputs to segment audiences, prioritize interventions, and estimate expected uplift and risks for targeted campaigns
• Apply personalized pricing, forecasting, and optimization methods to design interventions that influence behavior while measuring financial impact and demand dynamics
• Demonstrate the ability to design, run, and interpret experiments (A/B tests, randomized controlled trials, and adaptive allocation) and translate results into reliable decision rules and interpretation layers
• Evaluate behavioral biases (e.g., loss aversion, status quo bias, commitment devices, peer effects, sunk cost fallacy) and develop communication, trust-building, and adoption strategies that address ethical, diversity, and stakeholder concerns