MARK6582 – AI & Marketing

1.5 Credits • Elective

Instructor: Professor Simon Blanchard

Email: [email protected]

Teaching Assistant: Resham Natt

Email: [email protected]

Course Description

This course provides students with an introduction to artificial intelligence in marketing with an emphasis on both application and critical evaluation. Students will learn how AI tools—including generative AI, predictive analytics, conversational agents, and recommendation systems—are applied across the customer lifecycle: from acquisition through monetization and retention. At each stage, cases and exercises highlight both the opportunities for improved performance and the risks of misapplication. The course culminates with cases that address data privacy, regulation, and brand trust, forcing students to weigh the long-term implications of adopting AI at scale.

Artificial intelligence is increasingly integrated into marketing practice, but its adoption raises as many questions as it answers. Firms now use AI to attract customers through targeted advertising, optimize pricing and promotions, and sustain loyalty with predictive analytics and conversational agents. These applications can be powerful, but they also introduce risks: algorithmic bias, untested innovations with uncertain payoffs, and the possibility that short-term efficiencies may undermine brand trust. This course is designed to help students not only understand where AI creates value across the customer lifecycle, but also critically assess its costs and risks.

While the course is case-driven, it is also intentionally practical and applied. Students will not be expected to code extensively; instead, they will engage in structured exercises designed to approximate real managerial decisions. These include analyzing data dashboards, running spreadsheet-based pricing simulations, and conducting a live test of a generative AI chatbot using a Qualtrics integration. The goal is not to make students technical experts, but to provide enough exposure to how these tools work to allow them to critically evaluate their role in marketing strategy.

Learning Outcomes

By the end of this course, students will be able to:

1. Appreciate how AI enables new forms of marketing compared to traditional approaches.
2. Distinguish the types and roles of various AI applications in the marketing lifecycle.
3. Understand the strengths and weaknesses of AI-driven metrics and decisions.
4. Develop an experimental mindset to assess incremental return of AI campaigns.
5. Examine the benefits and risks associated with deploying AI in marketing, including privacy and brand equity.

Grading Summary

Grading Details

Attendance, Preparation, and Class Contribution (20%)

In-class preparation quizzes will be administered paper and pencil, in class, with no makeups. The lowest quiz grade will be dropped. Completion of peer surveys also counts toward participation.

Class contribution is evaluated as follows:

“Majority” means 80%+ of sessions. Negative in-class behavior—lateness, disruption, or inattention—will reduce your participation grade.

Group Case Assignments (20%)

Throughout the course, teams will prepare short case deliverables designed to ensure careful preparation, structured analysis, and active participation in class discussion. Assignments emphasize both analytical rigor and strategic framing. Students are expected to apply quantitative tools (dashboards, simulations, or data provided with the case) when available, and to link their analysis to broader marketing strategy questions. Teams may be called on to present their work in class.

Group Project: Chatbot Study (20%)

Teams of 4 or 5 will design and run a real experiment on a customer-facing AI chatbot. Each team is responsible for:

1. A research question — how does chatbot design choice X affect customer outcome Y?
2. A scenario — a realistic customer situation
3. A manipulation — two versions of the chatbot that differ on one behavioral dimension

The post-interaction survey is standardized across all teams. Teams do not design it.

The project unfolds in three phases:

1. Proposal (Week 3): Submit research question, scenario, and manipulation design.
2. Testing (Weeks 4–5): Deploy a user test via Qualtrics + LUCID. Classmates interact with each chatbot and complete the survey.
3. Presentation (Week 6): Analyze feedback, make recommendations, and present to the class. Final deliverables include the survey instrument, prompts and design instructions, slide deck, and cost and risk assessment.

Graded by instructor and peer assessment.

SEMrush AI Visibility Exam (15%)

Students are required to complete the AI Visibility Essentials certification by SEMrush. To receive full credit:

1. Complete the training materials using your Georgetown.edu account.
2. Take the AI Visibility exam in-class on April 8, 2026, paper and pencil, without outside assistance or AI tools.

Highest score will be kept. This is individual work.

Final Exam (25%)

The final exam is in-person, paper and pencil, and closed book. It covers all lectures, cases, and exercises. The format includes short answers, multiple-choice questions, and calculations. Scheduled during the allocated final exam period.

Schedule & Readings

The final exam will occur during the allocated final exam period.