# 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.