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Note: Due to the high demand for the Marketing Mobility seminar and the limited availability of spaces, an application is required. To apply for the seminar, please provide the following: 1. Your CV 2. Your TUM grade transcript Please upload both documents here: https://syncandshare.lrz.de/preparefilelink?folderID=2HPPAgPqXXeUjMywrrRz If you can not provide a grade transcript yet, please only upload your CV. Applications will be evaluated on a rolling basis. This means that applications submitted earlier may be more likely to be accepted and receive feedback sooner. All documents must be submitted by 23:59 on 6 October 2025 at the latest.

By the end, students can plan and produce a GenAI-enabled marketing concept for a mobility provider, grounded in policy realities and measurable outcomes.

- Apply core mobility-policy concepts (derived demand, externalities, user vs. system optimum) to marketing decisions.
- Prompt and evaluate state-of-the-art generative models across modalities; prototype creative assets.
- Define KPIs, test variations and reason about impact vs. constraints.
- Identify AI risks (bias, IP/safety, energy) and propose mitigations.
- Pitch a coherent campaign to executives with clear narrative and ROI logic.

In this hands-on seminar (Digital Marketing × Mobility Policy), student teams tackle a real-world GenAI marketing challenge from an industry partner and design a multi-modal GenAI-enabled marketing campaign they pitch at the end. This semester, Flughafen München will provide the marketing challenge.

First, we cover the essentials of marketing, mobility policy, and GenAI tools and limits. After the content day, teams enter a self-study/project work phase in which they develop their campaign independently and receive feedback from us in a coached feedback session. Seminar days are held at the Lab Campus at Munich Airport and include an airport tour with insights into the world of aviation. Further, additional lectures on the aviation industry and marketing in the aviation sector are provided at the Lab Campus.

- Foundations: key marketing concepts; core mobility-policy ideas (demand, externalities, user vs. system optimum).
- GenAI toolkit: trends & state-of-the-art tools across modalities (text, image, video, audio, code).
- Responsible AI: biases, legal/ethical issues, energy & sustainability considerations.
- Project work: independent team project work between blocks with a structured feedback session; build partner-ready marketing concept, assets, and KPI plan.
- Field visit & context immersion: airport/Lab Campus tour and expert insights.

No specific prior course is required; curiosity for digital marketing, mobility, and AI is essential. Basic analytics and a “learn-by-doing” mindset help. Coding experience is very welcome but optional.
Comfort with slides/spreadsheets; basic stats literacy.

- Interest in urban mobility & policy trade-offs.
- Readiness to use GenAI tools and create accounts where needed.
- Teamwork, presentation skills, and time for Lab Campus commutes.

We emphasise in-class discussion, interactive materials, and hands-on practice grounded in a real-world marketing challenge from a mobility provider. Instructions are research-based and interdisciplinary (Digital Marketing × Mobility Policy), with live code examples in Python. Between blocks, teams work in a self-study/project phase and receive structured feedback.

- Short concept inputs (marketing, mobility policy, GenAI).
- Mid-term feedback
- Field tour for contextual insight; stakeholder Q&A.
- Final team pitch to partner representatives.

The final grade is based on the team presentation of the GenAI-enabled marketing campaign to representatives of Flughafen München and the teaching team. Evaluation follows a transparent set of criteria (shared in the seminar). Active participation in discussions, feedback sessions, and field activities is expected and may be considered for the seminar grading.

Hartmann, J., Heitmann, M., Schamp, C., & Netzer, O. (2021). The power of brand selfies. Journal of Marketing Research, 58(6), 1159-1177.