Syllabus: AINS6200 AI for Marketing & Customer Insights

Catalog Description

Uses AI for segmentation, recommendations, campaign optimization, customer analytics, content workflows, and measurement.

Course Structure

Each week includes readings, a lecture/slide sequence, an executable lab, and an applied deliverable. Students maintain a reproducible project record and submit work through the LMS or GitHub workflow selected by the instructor.

Weekly Schedule

Week	Topic	Essential Question	Deliverable
1	Customer data and segmentation	How does AI identify useful customer groups?	Lab notebook + assignment brief
2	Recommendation systems	How do recommendations balance relevance, diversity, and business goals?	Lab notebook + assignment brief
3	Campaign optimization	How can AI improve experimentation and targeting?	Lab notebook + assignment brief
4	Customer journey analytics	How do signals across channels form a coherent picture?	Lab notebook + assignment brief
5	Generative AI for marketing operations	Where can generation increase throughput without weakening brand control?	Lab notebook + assignment brief
6	Measurement, attribution, and incrementality	What evidence shows marketing impact?	Lab notebook + assignment brief
7	Privacy, consent, and trust	How do marketing AI systems respect customers?	Lab notebook + assignment brief
8	AI customer insights portfolio	What should executives trust and act on?	Lab notebook + assignment brief

Assessment

Component	Weight
Weekly labs and notebooks	30%
Applied assignments	35%
Participation and technical critique	15%
Final synthesis portfolio	20%

Graduate Expectations

Submissions must show technical reasoning, evidence awareness, clear limitations, and responsible use of AI assistance. Code and analysis should be reproducible enough for instructor review.