Module 2 Narration#
Opening#
Open with the professional setting: a growth team deciding how AI-generated customer insights should guide campaign targeting. Ask students what decision is being made, who is affected, and what evidence would be persuasive to a skeptical reviewer.
Middle#
Move through the module in four passes:
Define Recommendation systems in the context of AI for Marketing & Customer Insights.
Walk through the lab as a proxy-data exercise, emphasizing what it can and cannot show.
Compare a baseline with an AI-enabled or more sophisticated alternative.
Translate the result into stakeholder language: recommendation, risk, mitigation, and next evidence.
Closing#
Close by returning to the module artifact: customer insights package with segmentation, measurement design, and consent constraints focused on recommendation systems: Prototype or specify a recommender.. Students should leave knowing exactly what artifact they are producing and how it will be judged.